Springer Handbook of Robotics

Kinematics pertains to the motion of bodies in a robotic mechanism without regard to the forces/torques that cause the motion. Since robotic mechanisms are by their very essence designed for motion, kinematics is the most fundamental aspect of robot design, analysis, control, and simulation. The robotics community has focused on efficiently applying different representations of position and orientation and their derivatives with respect to time to solve foundational kinematics problems. This chapter will present the most useful representations of the position and orientation of a body in space, the kinematics of the joints most commonly found in robotic mechanisms, and a convenient convention for representing the geometry of robotic mechanisms. These representational tools will be applied to compute the workspace, the forward and inverse kinematics, the forward and inverse instantaneous kinematics, and the static wrench transmission of a robotic mechanism. For brevity, the focus will be on algorithms applicable to open-chain mechanisms. The goal of this chapter is to provide the reader with general tools in tabulated form and a broader overview of algorithms that can be applied together to solve kinematics problems pertaining to a particular robotic mechanism.

The dynamic equations of motion provide the relationships between actuation and contact forces acting on robot mechanisms, and the acceleration and motion trajectories that result. Dynamics is important for mechanical design, control, and simulation. A number of algorithms are important in these applications, and include computation of the following: inverse dynamics, forward dynamics, the joint-space inertia matrix, and the operational-space inertia matrix. This chapter provides efficient algorithms to perform each of these calculations on a rigid-body model of a robot mechanism. The algorithms are presented in their most general form and are applicable to robot mechanisms with general connectivity, geometry, and joint types. Such mechanisms include fixed-base robots, mobile robots, and parallel robot mechanisms. In addition to the need for computational efficiency, algorithms should be formulated with a compact set of equations for ease of development and implementation. The use of spatial notation has been very effective in this regard, and is used in presenting the dynamics algorithms. Spatial vector algebra is a concise vector notation for describing rigid-body velocity, acceleration, inertia, etc., using six-dimensional (6-D) vectors and tensors. The goal of this chapter is to introduce the reader to the subject of robot dynamics and to provide the reader with a rich set of algorithms, in a compact form, that they may apply to their particular robot mechanism. These algorithms are presented in tables for ready access.

This chapter focuses on the principles that guide the design and construction of robotic systems. The kinematics equations and Jacobian of the robot characterize its range of motion and mechanical advantage, and guide the selection of its size and joint arrangement. The tasks a robot is to perform and the associated precision of its movement determine detailed features such as mechanical structure, transmission, and actuator selection. Here we discuss in detail both the mathematical tools and practical considerations that guide the design of mechanisms and actuation for a robot system. The following sections discuss characteristics of the mechanisms and actuation that affect the performance of a robot. The first four sections discuss the basic features of a robot manipulator and their relationship to the mathematical model that is used to characterize its performance. The next two sections focus on the details of the structure and actuation of the robot and how they combine to yield various types of robots. The final section relates these design features to various performance metrics.

Sensing sensing and estimation estimation are essential aspects of the design of any robotic system. At a very basic level, the state of the robot itself must be estimated for feedback control. At a higher level, perception perception, which is defined here to be task-oriented interpretation of sensor data, allows the integration of sensor information across space and time to facilitate planning. This chapter provides a brief overview of common sensing methods and estimation techniques that have found broad applicability in robotics. The presentation is structured according to a process model that includes sensing, feature extraction, data association, parameter estimation, and model integration. Several common sensing modalities are introduced and characterized. Common methods for estimation in linear and nonlinear systems are discussed, including statistical estimation, the Kalman filter, and sample-based methods. Strategies for robust estimation are also briefly described. Finally, several common representations for estimation are introduced.

This chapter first provides a formulation of the geometric path planning problem in Sect. 5.1 and then introduces sampling-based planning in Sect. 5.2. Sampling-based planners are general techniques applicable to a wide set of problems and have been successful in dealing with hard planning instances. For specific, often simpler, planning instances, alternative approaches exist and are presented in Sect. 5.3. These approaches provide theoretical guarantees and for simple planning instances they outperform sampling-based planners. Section 5.4 considers problems that involve differential constraints, while Sect. 5.5 overviews several other extensions of the basic problem formulation and proposed solutions. Finally, Sect. 5.7 addresses some important and more advanced topics related to motion planning.

This Chapter will focus on the motion control of robotic rigid manipulators. In other words, this Chapter does not treat the motion control of mobile robots, flexible manipulators, and manipulators with elastic joints. The main challenge in the motion control problem of rigid manipulators is the complexity of their dynamics and uncertainties. The former results from nonlinearity and coupling in the robot manipulators. The latter is twofold: structured and unstructured. Structured uncertainty means imprecise knowledge of the dynamic parameters and will be touched upon in this Chapter, whereas unstructured uncertainty results from joint and link flexibility, actuator dynamics, friction, sensor noise, and unknown environment dynamics, and will be treated in other Chapters. In this Chapter, we begin with an introduction to motion control of robot manipulators from a fundamental viewpoint, followed by a survey and brief review of the relevant advanced materials. Specifically, the dynamic model and useful properties of robot manipulators are recalled in Sect. 6.1. The joint and operational space control approaches, two different viewpoints on control of robot manipulators, are compared in Sect. 6.2. Independent joint control and proportional–integral–derivative (PID) control, widely adopted in the field of industrial robots, are presented in Sections 6.3 and 6.4, respectively. Tracking control, based on feedback linearization, is introduced in Sect. 6.5. The computed-torque control and its variants are described in Sect. 6.6. Adaptive control is introduced in Sect. 6.7 to solve the problem of structural uncertainty, whereas the optimality and robustness issues are covered in Sect. 6.8. Since most controllers of robot manipulators are implemented by using microprocessors, the issues of digital implementation are discussed in Sect. 6.9. Finally, learning control, one popular approach to intelligent control, is illustrated in Sect. 6.10.

A fundamental requirement for the success of a manipulation task is the capability to handle the physical contact between a robot and the environment. Pure motion control turns out to be inadequate because the unavoidable modeling errors and uncertainties may cause a rise of the contact force, ultimately leading to an unstable behavior during the interaction, especially in the presence of rigid environments. Force feedback and force control becomes mandatory to achieve a robust and versatile behavior of a robotic system in poorly structured environments as well as safe and dependable operation in the presence of humans. This chapter starts from the analysis of indirect force control strategies, conceived to keep the contact forces limited by ensuring a suitable compliant behavior to the end effector, without requiring an accurate model of the environment. Then the problem of interaction tasks modeling is analyzed, considering both the case of a rigid environment and the case of a compliant environment. For the specification of an interaction task, natural constraints set by the task geometry and artificial constraints set by the control strategy are established, with respect to suitable task frames. This formulation is the essential premise to the synthesis of hybrid force/motion control schemes.

Robot software systems tend to be complex. This complexity is due, in large part, to the need to control diverse sensors and actuators in real time, in the face of significant uncertainty and noise. Robot systems must work to achieve tasks while monitoring for, and reacting to, unexpected situations. Doing all this concurrently and asynchronously adds immensely to system complexity. The use of a well-conceived architecture, together with programming tools that support the architecture, can often help to manage that complexity. Currently, there is no single architecture that is best for all applications – different architectures have different advantages and disadvantages. It is important to understand those strengths and weaknesses when choosing an architectural approach for a given application. This chapter presents various approaches to architecting robotic systems. It starts by defining terms and setting the context, including a recounting of the historical developments in the area of robot architectures. The chapter then discusses in more depth the major types of architectural components in use today – behavioral control (Chap. 38), executives, and task planners (Chap. 9) – along with commonly used techniques for interconnecting connecting those components. Throughout, emphasis will be placed on programming tools and environments that support these architectures. A case study is then presented, followed by a brief discussion of further reading.

Artificial intelligence (AI) reasoning technology involving, e.g., inference, planning, and learning, has a track record with a healthy number of successful applications. So, can it be used as a toolbox of methods for autonomous mobile robots? Not necessarily, as reasoning on a mobile robot about its dynamic, partially known environment may differ substantially from that in knowledge-based pure software systems, where most of the named successes have been registered. This Chapter sketches the main robotics-relevant topics of symbol-based AI reasoning. Basic methods of knowledge representation and inference are described in general, covering both logic- and probability-based approaches. Then, some robotics-related particularities are addressed specially: issues in logic-based high-level robot control, fuzzy logics, and reasoning under time constraints. Two generic applications of reasoning are then described in some detail: action planning and learning. General reasoning is currently not a standard feature onboard autonomous mobile robots. Beyond sketching the state of the art in robotics-related AI reasoning, this Chapter points to the involved research problems that remain to be solved towards that end. The Chapter first reviews knowledge representation and deduction in general (Sect. 9.1), and then goes into some detail regarding reasoning issues that are considered particularly relevant for applications in robots (Sect. 9.2). Having presented reasoning methods, we then enter the field of generic reasoning applications, namely, action planning (Sect. 9.3) and machine learning (Sect. 9.4). Section 9.5 concludes.

This chapter is devoted to the design of robots, with a focus on serial architectures. In this regard, we start by proposing a stepwise design procedure; then, we recall the main issues in robot design. These issues pertain to workspace geometry, the kinetostatic, the dynamic, the elastostatic, and elastodynamic performance. In doing this, the mathematics behind the concepts addressed is briefly outlined to make the chapter self-contained. We survey some of the tools and criteria used in the mechanical design and performance evaluation of robots. Our focus is limited to robots that are (a) primarily intended for manipulation tasks and (b) supplied with serial kinematic chains. The kinematics of parallel robots is addressed in detail in Chap. 12. Wheeled robots, walking robots, multifingered hands, and other similar specialized structures are studied in their own chapters.

This chapter focuses on redundancy resolution schemes, i.e., the techniques for exploiting the redundant degrees of freedom in the solution of the inverse kinematics problem. This is obviously an issue of major relevance for motion planning and control purposes. In particular, task-oriented kinematics and the basic methods for its inversion at the velocity (first-order differential) level are first recalled, with a discussion of the main techniques for handling kinematic singularities. Next, different first-order methods to solve kinematicredundancyredundancykinematic kinematic redundancy are arranged in two main categories, namely those based on the optimization of suitable performance criteria and those relying on the augmentation of the task space. Redundancy resolution methods at the acceleration (second-order differential) level are then considered in order to take into account dynamics issues, e.g., torque minimization. Conditions under which a cyclic task motion results in a cyclic joint motion are also discussed; this is a major issue, e.g., for industrial applications in which a redundant manipulator is used to execute a repetitive task. The special class of hyperredundant manipulators is analyzed in detail. Suggestions for further reading are given in a final section.

This parallelrobotclosed chains chapter presents an introduction to the kinematics and dynamics of parallel mechanisms, also referred to as parallel robots. As opposed to classical serial manipulators, the kinematic architecture of parallel robots includes closed-loop kinematic chains. As a consequence, their analysis differs considerably from that of their serial counterparts. This chapter aims to presenting the fundamental formulations and techniques used in their analysis.

Design issues, dynamic modeling, trajectory planning, and feedback control problems are presented for robot manipulators having components with mechanical flexibility, either concentrated at the joints or distributed along the links. The chapter is divided accordingly in two main parts. Similarities or differences between the two types of flexibility are pointed out wherever appropriate. For robots with flexible joints, the dynamic model is derived in detail by following a Lagrangian approach and possible simplified versions are discussed. The problem of computing the nominal torques that produce a desired robot motion is then solved. Regulation and trajectory tracking tasks are addressed by means of linear and nonlinear feedback control designs. For robots with flexible links, relevant factors that lead to the consideration of distributed flexibility are analyzed. Dynamic models are presented, based on the treatment of flexibility through lumped elements, transfer matrices, or assumed modes. Several specific issues are then highlighted, including the selection of sensors, the model order used for control design, and the generation of effective commands that reduce or eliminate residual vibrations in rest-to-rest maneuvers. Feedback control alternatives are finally discussed. In each of the two parts of this chapter, a section is devoted to the illustration of the original references and to further readings on the subject.

This chapter discusses how to determine the kinematic parameters and the inertial parameters of robot manipulators. Both instances of model identification are cast into a common framework of least-squares parameter estimation, and are shown to have common numerical issues relating to the identifiability of parameters, adequacy of the measurement sets, and numerical robustness. These discussions are generic to any parameter estimation problem, and can be applied in other contexts. For kinematic calibration, the main aim is to identify the geometric Denavit–Hartenberg (DH) parameters, although joint-based parameters relating to the sensing and transmission elements can also be identified. Endpoint sensing or endpoint constraints can provide equivalent calibration equations. By casting all calibration methods as closed-loop calibration, the calibration index categorizes methods in terms of how many equations per pose are generated. Inertial parameters may be estimated through the execution of a trajectory while sensing one or more components of force/torque at a joint. Load estimation of a handheld object is simplest because of full mobility and full wrist force-torque sensing. For link inertial parameter estimation, restricted mobility of links nearer the base as well as sensing only the joint torque means that not all inertial parameters can be identified. Those that can be identified are those that affect joint torque, although they may appear in complicated linear combinations.

Multifingered robot hands have a potential capability for achieving dexterous manipulation of objects by using rolling and sliding motions. This chapter addresses design, actuation, sensing and control of multifingered robot hands. From the design viewpoint, they have a strong constraint in actuator implementation due to the space limitation in each joint. After briefly introducing the overview of anthropomorphic end-effector and its dexterity in Sect. 15.1, various approaches for actuation are provided with their advantages and disadvantages in Sect. 15.2. The key classification is (1) remote actuation or build-in actuation and (2) the relationship between the number of joints and the number of actuator. In Sect. 15.3, actuator and sensors used for multifingered hands are described. In Sect. 15.4, modeling and control are introduced by considering both dynamic effects and friction. Applications and trends are given in Sect. 15.5. Finally, this chapter is closed with conclusions and further reading.

In this chapter, we introduce legged robots. After introducing the history of legged robot research in Sect. 16.1, we start to discuss hopping robots and analyze a simple passive walker as a typical cycling walking robot in Sect. 16.2; the Poincaré map is one of the most important tools to analyze its dynamics and stability. In Sect. 16.3, the dynamics and control of general biped robots are discussed. The key is the forward dynamics subject to the unilateral constraint between the feet and the ground. Its formal treatment leads to walking trajectory generation and various control methods. As a practical scheme to control biped robots, we discuss the zero-moment point (ZMP) in Sect. 16.4, including its definition, physical meaning, measurement, calculation, and usage. In Sect. 16.5, we move to multilegged robots. In this field, the most important subject is the relationship between gaits and stability. We also introduce the landmark robots in this field. In Sect. 16.6, we overview the divergence of the legged robots. We see leg–wheel hybrid robots, leg–arm hybrid robots, tethered walking robots, and wall-climbing robots. To compare these legged robots with different configurations, we use some useful performance indices such as the Froude number and the specific resistance, which are introduced in Sect. 16.7. We conclude the chapter and address future trends in Sect. 16.8.

The purpose of this chapter is to introduce, analyze, and compare the models of wheeled mobile robots (WMR) wheeledmobile robot (WMR) and to present several realizations and commonly encountered designs. The mobility of WMR is discussed on the basis of the kinematic constraints resulting from the pure rolling conditions at the contact points between the wheels and the ground. According to this discussion it is shown that, whatever the number and the types of the wheels, all WMR belong to only five generic classes. Different types of models are derived and compared: the posture model versus the configuration model, the kinematic model versus the dynamic model. The structural properties of these models are discussed and compared. These models as well as their properties constitute the background necessary for model-based control design. Practical robot structures are classified according to the number of wheels, and features are introduced focusing on commonly adopted designs. Omnimobile robots and articulated robots realizations are described in more detail.

The field of microrobotics covers the robotic manipulation of objects with dimensions in the millimeter to micron range as well as the design and fabrication of autonomous robotic agents that fall within this size range. Nanorobotics is defined in the same way only for dimensions smaller than a micron. With the ability to position and orient objects with micron- and nanometer-scale dimensions, manipulation at each of these scales is a promising way to enable the assembly of micro- and nanosystems, including micro- and nanorobots. This chapter overviews the state of the art of both micro- and nanorobotics, outlines scaling effects, actuation, and sensing and fabrication at these scales, and focuses on micro- and nanorobotic manipulation systems and their application in microassembly, biotechnology, and the construction and characterization of micro and nanoelectromechanical systems (MEMS/NEMS) micro and nanoelectromechanical systems (MEMS/NEMS). Material science, biotechnology, and micro- and nanoelectronics will also benefit from advances in these areas of robotics.

This chapter provides an overview of force and tactile sensing, with the primary emphasis placed on tactile sensing. We begin by presenting some basic considerations in choosing a tactile sensor and then review a wide variety of sensor types, including proximity, kinematic, force, dynamic, contact, skin deflection, thermal, and pressure sensors. We also review various transduction methods, appropriate for each general sensor type. We consider the information that these various types of sensors provide in terms of whether they are most useful for manipulation, surface exploration or being responsive to contacts from external agents. Concerning the interpretation of tactile information, we describe the general problems and present two short illustrative examples. The first involves intrinsic tactile sensing, i.e., estimating contact locations and forces from force sensors. The second involves contact pressure sensing, i.e., estimating surface normal and shear stress distributions from an array of sensors in an elastic skin. We conclude with a brief discussion of the challenges that remain to be solved in packaging and manufacturing damage-tolerant tactile sensors.

This chapter examines how certain properties of the world can be exploited in order for a robot or other device to develop a model of its own motion or pose (position and orientation) relative to an external frame of reference. Although this is a critical problem for many autonomous robotic systems, the problem of establishing and maintaining an orientation or position estimate of a mobile agent has a long history in terrestrial navigation.

Sonar or ultrasonic sensing uses the propagation of acoustic energy at higher frequencies than normal hearing to extract information from the environment. This chapter presents the fundamentals and physics of sonar sensing for object localization, landmark measurement and classification in robotics applications. The source of sonar artifacts is explained and how they can be dealt with. Different ultrasonic transducer technologies are outlined with their main characteristics highlighted. Sonar systems are described that range in sophistication from low-cost threshold-based ranging modules to multitransducer multipulse configurations with associated signal processing requirements capable of accurate range and bearing measurement, interference rejection, motion compensation, and CTFM (continuous-transmission frequency modulation)target classification. Continuous-transmission frequency-modulated (CTFM) systems are introduced and their ability to improve target sensitivity in the presence of noise is discussed. Various sonar ring designs that provide rapid surrounding environmental coverage are described in conjunction with mapping results. Finally the chapter ends with a discussion of biomimetic sonar, which draws inspiration from animals such as bats and dolphins.

Range sensors are devices that capture the three-dimensional (3-D) structure of the world from the viewpoint of the sensor, usually measuring the depth to the nearest surfaces. These measurements could be at a single point, across a scanning plane, or a full image with depth measurements at every point. The benefits of this range data is that a robot can be reasonably certain where the real world is, relative to the sensor, thus allowing the robot to more reliably find navigable routes, avoid obstacles, grasp objects, act on industrial parts, etc. This chapter introduces the main representations for range data (point sets, triangulated surfaces, voxels), the main methods for extracting usable features from the range data (planes, lines, triangulated surfaces), the main sensors for acquiring it (Sect. 22.1 – stereo and laser triangulation and ranging systems), how multiple observations of the scene, e.g., as if from a moving robot, can be registered (Sect. 22.2), and several indoor and outdoor robot applications where range data greatly simplifies the task (Sect. 22.3).

In this chapter, we describe methods to be applied on a robot equipped with one or more camera sensors. Our goal is to present representations and models for both three-dimensional (3-D) motion and structure estimation as well as recognition. We do not delve into estimation and inference issues since these are extensively treated in other chapters. The same applies to the fusion with other sensors, which we heavily encourage but do not describe here. In the first part we describe the main methods in 3-D inference from two-dimensional (2-D) images. We are at the point where we could propose a recipe, at least for a small spatial extent. If we are able to track a few visual features in our images, we are able to estimate the self-motion of the robot as well as its pose with respect to any known landmark. Having solutions for minimal case problems, the obvious way here is to apply random sample consensus. If no known 3-D landmark is given then the trajectory of the camera exhibits drift. From the trajectory of the camera, time windows over several frames are selected and a 3-D dense depth map is obtained through solving the stereo problem. Large-scale reconstructions based on camera only do raise challenges with respect to drift and loop closing. In the second part we deal with recognition as appealed to robotics. The main challenge here is to detect an instance of an object and recognize or categorize it. Since in robotics applications an object of interest always resides in a cluttered environment any algorithm has to be insensitive to missing parts of the object of interest and outliers. The dominant paradigm is based on matching the appearance of pictures. Features are detected and quantized into visual words. Similarity is based on the difference between histograms of such visual words. Recognition has a long way to go but robotics provides the opportunity to explore an object and be active in the recognition process.

This chapter introduces visual servo control, using computer vision data in the servo loop to control the motion of a robot. We first describe the basic techniques that are by now well established in the field. We give a general overview of the formulation of the visual servo control problem, and describe the two archetypal visual servo control schemes: image-based and position-based visual servo control. We then discuss performance and stability issues that pertain to these two schemes, motivating advanced techniques. Of the many advanced techniques that have been developed, we discuss 2.5-D, hybrid, partitioned, and switched approaches. Having covered a variety of control schemes, we conclude by turning briefly to the problems of target tracking and controlling motion directly in the joint space.

Multisensor data fusion multisensordata fusionis the process of combining observations from a number of different sensors to provide a robust and complete description of an environment or process of interest. Data fusion finds wide application in many areas of robotics such as object recognition, environment mapping, and localization. This chapter has three parts: methods, architectures, and applications. Most current data fusion methods employ probabilistic descriptions of observations and processes and use Bayesʼ rule to combine this information. This chapter surveys the main probabilistic modeling and fusion techniques including grid-based models, Kalman filtering, and sequential Monte Carlo techniques. This chapter also briefly reviews a number of nonprobabilistic data fusion methods. Data fusion systems are often complex combinations of sensor devices, processing, and fusion algorithms. This chapter provides an overview of key principles in data fusion architectures from both a hardware and algorithmic viewpoint. The applications of data fusion are pervasive in robotics and underly the core problem of sensing, estimation, and perception. We highlight two example applications that bring out these features. The first describes a navigation or self-tracking application for an autonomous vehicle. The second describes an application in mapping and environment modeling. The essential algorithmic tools of data fusion are reasonably well established. However, the development and use of these tools in realistic robotics applications is still developing.

This chapter serves as an introduction to Part D by giving an overview of motion generation and control strategies in the context of robotic manipulation tasks. Automatic control ranging from the abstract, high-level task specification down to fine-grained feedback at the task interface are considered. Some of the important issues include modeling of the interfaces between the robot and the environment at the different time scales of motion and incorporating sensing and feedback. Manipulation planning is introduced as an extension to the basic motion planning problem, which can be modeled as a hybrid system of continuous configuration spaces arising from the act of grasping and moving parts in the environment. The important example of assembly motion is discussed through the analysis of contact states and compliant motion control. Finally, methods aimed at integrating global planning with state feedback control are summarized.

Robotic manipulators use contact forces to grasp and manipulate objects in their environments. Fixtures rely on contacts to immobilize workpieces. Mobile robots and humanoids use wheels or feet to generate the contact forces that allow them to locomote. Modeling of the contactinterfacecontact interface, therefore, is fundamental to analysis, design, planning, and control of many robotic tasks. This chapter presents an overview of the modeling of contact interfaces, with a particular focus on their use in manipulation tasks, including graspless or nonprehensilenonprehensile manipulation manipulation modes such as pushing. Analysis and design of grasps and fixtures also depends on contact modeling, and these are discussed in more detail in Chap. 28. Sections 27.2–27.5 focus on rigid-body models of contact. Section 27.2 describes the kinematic constraints caused by contact, and Sect. 27.3 describes the contact forces that may arise with Coulomb friction. Section 27.4 provides examples of analysis of multicontact manipulation tasks with rigid bodies and Coulomb friction. Section 27.5 extends the analysis to manipulation by pushing. Section 27.6 introduces modeling of contact interfaces, kinematic duality, and pressure distribution. Section 27.7 describes the concept of the friction limit surface and illustrates it with an example demonstrating the construction of a limit surface for a soft contact. Finally, Sect. 27.8 discusses how these more accurate models can be used in fixture analysis and design.

This graspingchapter introduces fundamental models of grasp analysis. The overall model is a coupling of models that define contact behavior with widely used models of rigid-body kinematics and dynamics. The contact model essentially boils down to the selection of components of contact force and moment that are transmitted through each contact. Mathematical properties of the complete model naturally give rise to five primary grasp types whose physical interpretations provide insight for grasp and manipulation planning. After introducing the basic model and types of grasps, this chapter focuses on the most important grasp characteristic: complete restraint. A grasp with complete restraint prevents loss of contact and thus is very secure. Two primary restraint properties are form closure and force closure. A form closure grasp guarantees maintenance of contact as long as the links of the hand and the object are well approximated as rigid and as long as the joint actuators are sufficiently strong. As will be seen, the primary difference between form closure and force closure grasps is the latterʼs reliance on contact friction. This translates into requiring fewer contacts to achieve force closure than form closure.

This chapter is devoted to cooperative manipulation of a common object by means of two or more robotic arms. The chapter opens with a historical overview of the research on cooperative manipulation, ranging from early 1970s to very recent years. Kinematics and dynamics of robotic arms cooperatively manipulating a tightly grasped rigid object are presented in depth. As for the kinematics and statics, the chosen approach is based on the so-called symmetric formulation; fundamentals of dynamics and reduced-order models for closed kinematic chains are discussed as well. A few special topics, such as the definition of geometrically meaningful cooperative task space variables, the problem of load distribution, and the definition of manipulability ellipsoids, are included to give the reader a complete picture of modeling and evaluation methodologies for cooperative manipulators. Then, the chapter presents the main strategies for controlling both the motion of the cooperative system and the interaction forces between the manipulators and the grasped object; in detail, fundamentals of hybrid force/position control, proportional–derivative (PD)-type force/position schemes, feedback linearization techniques, and impedance control approaches are given. In the last section further reading on advanced topics related to control of cooperative robots is suggested; in detail, advanced nonlinear control strategies are briefly discussed (i.e., intelligent control approaches, synchronization control, decentralized control); also, fundamental results on modeling and control of cooperative systems possessing some degree of flexibility are briefly outlined.

The word haptics, haptic believed to be derived from the Greek word haptesthai, means related to the sense of touch. In the psychology and neuroscience literature, haptics is the study of human touch sensing, specifically via kinesthetickinesthetic (force/position) and cutaneouscutaneous (tactile) receptors, associated with perception and manipulation. In the robotics and virtual reality literature, haptics is broadly defined as real and simulated touch interactions between robots, humans, and real, remote, or simulated environments, in various combinations. This chapter focuses on the use of specialized robotic devices and their corresponding control, known as haptic interfaces, hapticinterfaceinterfacehaptic that allow human operators to experience the sense of touch in remote (teleoperated) or simulated (virtual) environments.

In this chapter we present an overview of the field of telerobotics with a focus on control aspects. Motivated by an historical prespective and some challenging applications of this research area a classification of control architectures is given, including an introduction to the different strategies. An emphasis is taken on bilateral control and force feedback, which is a vital research field today. Finally we suggest some literature for a closer engagement with the topic of telerobotics.

Telerobots, remotely controlled robots, are widely used to explore undersea terrains and outer space, to defuse bombs, and to clean up hazardous waste. Until 1994, telerobots were accessible only to trained and trusted experts through dedicated communication channels. This chapter describes networked telerobots, a new class of telerobots controllable over networks such as the Internet, that are accessible to the general public. This chapter will describe relevant network technology, the history of networked telerobots within the broader field of teleoperation, properties of networked telerobots, how to build a networked robot, example systems, and topics for future research.

Although autonomous robotic systems perform remarkably in structured environments (e.g., factories), integrated human–robotic systems are superior to any autonomous robotic systems in unstructured environments that demand significant adaptation. The technology associated with exoskeleton systems and human power augmentation can be divided into lower-extremity exoskeletons and upper-extremity exoskeletons. The reason for this was twofold; firstly, one could envision a great many applications for either a stand-alone lower- or upper-extremity exoskeleton in the immediate future. Secondly, and more importantly for the division, is that these exoskeletons are in their early stages, and further research still needs to be conducted to ensure that the upper-extremity exoskeleton and lower-extremity exoskeleton can function well independently before one can venture an attempt to integrate them. This chapter first gives a description of the upper-extremity exoskeleton efforts and then will proceed with the more detailed description of the lower-extremity exoskeleton.

This chapter may be seen as a follow up to Chap. 17, devoted to the classification and modeling of wheeledmobile robot (WMR) basic wheeled mobile robot (WMR) structures, and a natural complement to Chap. 35, which surveys motion planning methods for WMRs. A typical output of these methods is a feasible (or admissible) reference state trajectory for a given mobile robot, and a question which then arises is how to make the physical mobile robot track this reference trajectory via the control of the actuators with which the vehicle is equipped. The object of the present chapter is to bring elements of the answer to this question based on simple and effective control strategies. A first approach would consist in applying open-loop steering control laws like those developed in Chap. 35. However, it is well known that this type of control is not robust to modeling errors (the sources of which are numerous) and that it cannot guarantee that the mobile robot will move along the desired trajectory as planned. This is why the methods here presented are based on feedback control. feedbackcontrolcontrolfeedback Their implementation supposes that one is able to measure the variables involved in the control loop (typically the position and orientation of the mobile robot with respect to either a fixed frame or a path that the vehicle should follow). Throughout this chapter we will assume that these measurements are available continuously in time and that they are not corrupted by noise. In a general manner, robustness considerations will not be discussed in detail, one reason being that, beyond imposed space limitations, a large part of the presented approaches are based on linear control theory. The feedback control laws then inherit the strong robustness properties associated with stable linear systems. Results can also be subsequently refined by using complementary, eventually more elaborate, automatic control techniques.

This chapter describes motion planning and obstacle avoidance for mobile robots. We will see how both areas do not share the same modeling background. From the very beginning of motion planning, the research has been dominated by computer science. Researchers aim at devising well-grounded algorithms with well-understood completeness and exactness properties. The introduction of nonholonomic constraints has forced these algorithms to be revisited via the introduction of differential geometry approaches. Such a combination has been made possible for certain classes of systems, so-called small-time controllable ones. The underlying hypothesis of motion planning algorithms remains the knowledge of a global and accurate map of the environment. More than that, the considered system is a formal system of equations that does not account for the entire physical system: uncertainties in the world or system modeling are not considered. Such hypotheses are too strong in practice. This is why other complementary researchers have adopted a parallel, more pragmatic but realistic approach to deal with obstacle avoidance. The problem here is not to deal with complicated systems like a car with multiple trailers. The considered systems are much simpler with respect to their geometric shape. The problem considers sensor-based motion to face the physical issues of a real system navigating in a real world better than motion planning algorithms: how to navigate toward a goal in a cluttered environment when the obstacles to avoid are discovered in real time? This is the question obstacle avoidance addresses.

In this chapter we describe popular ways to represent the environment of a mobile robot. For indoor environments, which are often stored using two-dimensional representations, we discuss occupancy grids, line maps, topological maps, and landmark-based representations. Each of these techniques has its own advantages and disadvantages. Whilst occupancy grid maps allow for quick access and can efficiently be updated, line maps are more compact. Also landmark-based maps can efficiently be updated and maintained, however, they do not readily support navigation tasks such as path planning like topological representations do. Additionally, we discuss approaches suited for outdoor terrain modeling. In outdoor environments, the flat-surface assumption underling many mapping techniques for indoor environments is no longer valid. A very popular approach in this context are elevation and variants maps, which store the surface of the terrain over a regularly spaced grid. Alternatives to such maps are point clouds, meshes, or three-dimensional grids, which provide a greater flexibility but have higher storage demands.

This chapter provides a comprehensive introduction in to the simultaneous localization and mapping problem, better known in its abbreviated form as SLAM. SLAM addresses the problem of a robot navigating an unknown environment. While navigating the environment, the robot seeks to acquire a map thereof, and at the same time it wishes to localize itself using its map. The use of SLAM problems can be motivated in two different ways: one might be interested in detailed environment models, or one might seek to maintain an accurate sense of a mobile robotʼs location. SLAM serves both of these purposes. We review three major paradigms of algorithms from which a huge number of recently published methods are derived. First comes the traditional approach, which relies on the extended Kalman filter (EKF) extendedKalman filter (EKF) for representing the robotʼs best estimate. The second paradigm draws its intuition from the fact that the SLAM problem can be viewed as a sparse graph of constraints, and it applies nonlinear optimization for recovering the map and the robotʼs locations. Finally, we survey the particle filter paradigm, which applies nonparametric density estimation and efficient factorization methods to the SLAM problem. This chapter discusses extensions of these basic methods. It elucidates variants of the SLAM problem and proposes a taxonomy for the field. Relevant research is referenced extensively, and open research problems are discussed.

Nature is filled with examples of autonomous creatures capable of dealing with the diversity, unpredictability, and rapidly changing conditions of the real world. Such creatures must make decisions and take actions based on incomplete perceptionperception, time constraints, limited knowledge about the world, cognition, cognitionreasoning reasoningand physical capabilities, in uncontrolled conditions and with very limited cues about the intent of others. Consequently, one way of evaluating intelligence is based on the creatureʼs ability to make the most of what it has available to handle the complexities of the real world. The main objective of this chapter is to clarify behavior-based systemsbehavior-basedsystemssystembehavior-basedand their use in single- and multi-robot autonomous control problems multi-robot systemsand applications. The chapter is organized as follows. Section 38.1 overviews robot control, introducing behavior-based systems in relation to other established approaches to robot control. Section 38.2 follows by outlining the basic principles of behavior-based systems that make them distinct from other types of robot control architectures. The concept of basis behaviors, the means of modularizing behavior-based systems, is presented in Sect. 38.3. Section 38.4 describes how behaviors are used as building blocks for creating representations for use by behavior-based systems, enabling the robot to reason about the world and about itself in that world. Section 38.5 presents several different classes of learning methods for behavior-based systems, validated on single-robot and multi-robot systems. Section 38.6 provides an overview of various robotics problems and application domains that have successfully been addressed with behavior-based control. Finally, Sect. 38.7 concludes the chapter.

This chapter is organized according to a number of broad classes of problem where modular robotic systems can prove beneficial. For each such problem, the benefits of modularity are described, along with the ways that particular systems or proposed systems have explored those benefits. In particular, we discuss locomotion in Sect. 39.1, manipulation in Sect. 39.2, modular robot geometry in Sect. 39.3, and robust systems in Sect. 39.4. The systems under consideration in general have some level of independent computation on each module, and this discussion will focus on systems in which modules maintain some sort of kinematic constraint between them during operation. Compared to the types of multirobot teams described in Chap. 40, the systems of interest here are generally much more tightly coupled, both physically and conceptually. That is, we are primarily concerned with systems which, though they have many processors and independent actuators, have a single goal or small set of goals which can only be achieved collectively, rather than a set of goals which can be apportioned to single (or a small number of) robots within the team.

Within the context of multiple mobile robot systems, this chapter explores the current state of the art. After a brief introduction, we first examine architectures for multirobot cooperation, exploring the alternative approaches that have been developed. Next, we explore communications issues and their impact on multirobot teams in Sect. 40.3, followed by a discussion of swarm robot systems in Sect. 40.4. While swarm systems typically assume large numbers of homogeneous robots, other types of multirobot systems include heterogeneous robots. We therefore next discuss heterogeneity in cooperative robot teams in Sect. 40.5. Once robot teams allow for individual heterogeneity, issues of task allocation become important; Sect. 40.6 therefore discusses common approaches to task allocation. Section 40.7 discusses the challenges of multirobot learning, and some representative approaches. We outline some of the typical application domains which serve as test beds for multirobot systems research in Sect. 40.8. Finally, we conclude in Sect. 40.9 with some summary remarks and suggestions for further reading.

This chapter discusses networked robots, multiple robots operating together coordinating and cooperating by networked communication to accomplish a specified task. This chapter presents an overview of the field with an emphasis on recent results and research challenges. Multiple robots enable new capabilities and the communication network enables new approaches and solutions that are difficult with just perception and control. Communication enables new control and perception capabilities in the system (e.g., access to information outside the perception range of the robot system). Conversely, control enables solutions for problems that are difficult without mobility (e.g., localization). Section 41.1 defines the field, examines the benefits of networking in robot coordination, and discusses applications. Section 41.2 highlights a few projects focused on networked robotics and discusses the application potential of the field. Section 41.3 discusses the research challenges at the intersection of control, communication, and perception. Section 41.4 defines a model for the control of a networked system which is used in Sects. 41.5–41.8 to examine specific research issues and opportunities facilitated by the interplay between communication, control, and perception.

Most robots today can trace their origin to early industrial robot designs. Much of the technology that makes robots more human-friendly and adaptable for different applications has emerged from manufacturers of industrial robots. Industrial robots are by far the largest commercial application of robotics technology today. All the important foundations for robot control were initially developed with industrial applications in mind. These applications deserve special attention in order to understand the origin of robotics science and to appreciate many unsolved problems that still prevent the wider use of robots in manufacturing. In this chapter we present a brief history and descriptions of typical industrial robotics applications. We show how robots with different mechanisms fit different applications. Even though robots are well established in large-scale manufacturing, particularly in automobile and related component assembly, there are still many challenging problems to solve. The range of feasible applications could significantly increase if robots were easier to install, to integrate with other manufacturing processes, and to program, particularly with adaptive sensing and automatic error recovery. We outline some of these remaining challenges for researchers.

This chapter deals with the main underwater robotic topics. First, a brief introduction showing the constantly expanding role of marine robotics in oceanic engineering is given; this section also contains some historical backgrounds. Most of the following sections strongly overlap with the corresponding chapters presented in this handbook; hence, to avoid useless repetitions, only those aspects peculiar to the underwater environment are discussed, assuming that the reader is already familiar with concepts such as fault detection systems when discussing the corresponding underwater implementation. The modeling section is presented by focusing on a coefficient-based approach capturing the most relevant underwater dynamic effects. Two sections dealing with the description of the sensor and the actuating systems are then given. Autonomous underwater vehicles require the implementation of mission control system as well as guidance and control algorithms. Underwater localization is also discussed. Underwater manipulation is then briefly approached. Fault detection and fault tolerance, together with the coordination control of multiple underwater vehicles, conclude the theoretical part of the chapter. Two final sections, reporting some successful applications and discussing future perspectives, conclude the chapter.

A wide array of potential applications exist for robots that have the level of mobility offered by flight. The aerial roboticsmilitary applications of aerial robotics have been recognized ever since the beginnings of powered flight, and they have already been realized to sometimes spectacular effect in surveillance, targeting, and even strike missions. The range of civilian applications is even greater and includes remote sensing, disaster response, image acquisition, surveillance, transportation, and delivery of goods. This chapter first presents a brief history of aerial robotics. It then continues by describing the range of possible and actual applications of aerial robotics. The list of current challenges to aerial robotics is then described. Building from basic notions of flight, propulsion, and available sensor technology, the chapter then moves on to describe some of the current research efforts aimed at addressing the various challenges faced by aerial robots. The challenges faced by aerial robots span several and distinct fields, including state regulations, man–machine interface design issues, navigation, safety/reliability, collision prevention, and take-off/landing techniques. The size of aerial robots can considerably influence their flight dynamics, and small aerial robots can end up looking considerably different from their larger counterparts. Similar to their manned counterparts, aerial robots may enjoy diverse propulsion systems and operate over large speed ranges. Aerial robots must be equipped with reliable position and actuation equipment so as to be capable of controlled flight, and this constitutes a nontrivial requirement prior to doing research or development in this field. However, many universities, research centers, and industries have now met this requirement and are actively working on the challenges presented above. The largest obstacle to the commercial development of aerial robots is, however, the necessity to comply with and support a regulatory regulationenvironment which is only beginning to address these rapidly developing systems.

In the space community, any unmanned spacecraft can be called a robotic spacecraft. However, space robots are considered to be more capable devices that can facilitate manipulation, assembling, or servicing functions in orbit as assistants to astronauts, or to extend the areas and abilities of exploration on remote planets as surrogates for human explorers. In this chapter, a concise digest of the historical overview and technical advances of two distinct types of space robotic systems, orbital robots and surface robots, is provided. In particular, Sect. 45.1 describes orbital robots, and Sect. 45.2 describes surface robots. In Sect. 45.3, the mathematical modeling of the dynamics and control using reference equations are discussed. Finally, advanced topics for future space exploration missions are addressed in Sect. 45.4. Key issues in space robots and systems are characterized as follows. Manipulation – Although manipulation is a basic technology in robotics, microgravity in the orbital environment requires special attention to the motion dynamics of manipulator arms and objects being handled. Reaction dynamics that affect the base body, impact dynamics when the robotic hand contacts an object to be handled, and vibration dynamics due to structural flexibility are included in this issue. Mobility – The ability to locomote is particularly important in exploration robots (rovers) that travel on the surface of a remote planet. These surfaces are natural and rough, and thus challenging to traverse. Sensing and perception, traction mechanics, and vehicle dynamics, control, and navigation are all mobile robotics technologies that must be demonstrated in a natural untouched environment. Teleoperation and autonomy – There is a significant time delay between a robotic system at a work site and a human operator in an operation room on the Earth. In earlier orbital robotics demonstrations, the latency was typically 5 s, but can be several tens of minutes, or even hours for planetary missions. Telerobotics technology is therefore an indispensable ingredient in space robotics, and the introduction of autonomy is a reasonable consequence. Extreme environments – In addition to the microgravity environment that affects the manipulator dynamics or the natural and rough terrain that affects surface mobility, there are a number of issues related to extreme space environments that are challenging and must be solved in order to enable practical engineering applications. Such issues include extremely high or low temperatures, high vacuum or high pressure, corrosive atmospheres, ionizing radiation, and very fine dust.

In agriculture and forestry, robotics has made a substantial impact. Farmers are conscious of their need for automatic vehicle guidance to minimize damage to the growing zone of their soil. Automatic sensing, handling, and processing of produce are now commonplace, while there is substantial instrumentation and mechanization of livestock procedures. In forestry, legged harvesters have not yet seen great success in their application, but the automation of trimming and forwarding with simultaneous localization and mapping techniques will change the industry in the future. Some impressive developments in walking forestry harvesters are presented, including machines targeted towards the difficult terrain of the Scandinavian forests. More-conventional cut-to-length harvesters are also highly automated, while operations such as delimbing must be carried out at speed. Before complete autonomous harvesting becomes possible, some of the localization and mapping techniques that are described must come to fruition. The combination of machine vision with global positioning by satellite (GPS) allows a tractor to follow a row of crops, performing a headland turn at the end of the row. The history of a series of projects is outlined, leading to the present outcome that is in the process of being commercialized. Another project that is based on machine vision relates to the location of macadamia nuts. To select which trees should be propagated, it is necessary to attribute fallen nuts to the correct tree. Color sorting and grading of produce is not a matter of sensing alone, but involves a measure of produce handling that puts it at the fringe of robotics. Automated milking parlours have proved their worth. However success has eluded some other projects described here, such as automated sheep-shearing and an automated abattoir. Another project is presented that literally sorts the sheep from the goats, using a swinging gate to separate different species using machine vision so that feral species are excluded from watering holes in the dry Australian outback. Although robotics is making rapid inroads into these areas, they are still a fruitful source of application projects, some sufficiently demanding to require the development of new theoretical techniques.

This chapter introduces various construction automation concepts that have been developed over the constructionautomationpast few decades and presents examples of construction robots that are in current use (as of 2006) and/or in various stages of research and development. Section 47.1 presents an overview of the construction industry, which includes descriptions of the industry, the types of construction, and the typical construction project. The industry overview also discusses the concept of automation versus robotics in construction and breaks down the concept of robotics in construction into several levels of autonomy. Section 47.1 also presents traditional and advanced concepts for sensing systems in construction, which enable the use of robots and various forms of automation. Section 47.2 discusses some of the economic aspects of implementing robotics in construction, and Sect. 47.3 presents examples of robots from various construction applications. Section 47.4 discusses unsolved technical problems in construction robotics, which include interoperability, connection systems, tolerances, and power and communications. Finally, Sect. 47.5 discusses future directions in construction robotics and Sect. 47.6 gives some conclusions and suggests resources for further reading.

Robotics researchers have worked hard to realize a long-awaited vision: machines carrying people from burning buildings or tunneling through collapsed rock falls to reach trapped miners. In this chapter we review progress. Researchers still have many challenges ahead of them but there has been remarkable progress in some areas. Hazardous environments present special challenges for the accomplishment of desired tasks depending on the nature and magnitude of the hazards. Hazards may be present in the form of radiological or toxicity dangers to potential explosions. Technology that specialized engineering companies can develop and sell without active help from researchers marks the frontier of feasibility. Just inside this border lie teleoperated robots explosiveordnance disposal (EOD)ordnance disposalexplosive (EOD) for explosive ordnance disposal (EOD) and for underwater underwaterengineering work. Even with the typical tenfold reduction in manipulation performance imposed by the limits of todayʼs telepresencetelepresenceand teleoperation teleoperationtechnology, robots usually offer a more cost-effective solution. Most hazardous applications lie far beyond the frontier, although researchers managed to establish some limited inroads by the turn of the 21st century. Fire fighting, rescue operations, removing high-level nuclear contamination, reactor decommissioning, tunneling through rock falls, and most landmine landmineand unexploded ordnance unexploded ordnance (UXO)problems still present many unsolved problems.

Mining is the process of extracting mineral resources from the Earth for commercial value. It is an ancient human activity which can be traced back to Palaeolithic times (43000 years ago), where for example the mineral hematite was mined to produce the red pigment ochre. The importance of many mined minerals is reflected in the names of the major milestones in human civilizations: the stone, copper, bronze, and iron ages. Much later coal provided the energy that was critical to the industrial revolution and still underpins modern society, creating 38% of world energy generation today. Ancient mines used human and later animal labor and broke rock using stone tools, heat, and water, and later iron tools. Todayʼs mines are heavily mechanized with large diesel and electrically miningvehiclevehiclemining powered vehicles, and rock is broken with explosives or rock cutting machines (Fig. 49.1). Fig. 49.1Evolution of mining technology

In order to summarize the status of rescue robotics, this chapter will cover the basic characteristics of disasters and their impact on robotic design, describe the robots actually used in disasters to date, promising robot designs (e.g., snakes, legged locomotion) and concepts (e.g., robot teams or swarms, sensor networks), methods of evaluation in benchmarks for rescue robotics, and conclude with a discussion of the fundamental problems and open issues facing rescue robotics, and their evolution from an interesting idea to widespread adoption. The Chapter will concentrate on the rescue phase, not recovery, with the understanding that capabilities for rescue can be applied to, and extended for, the recovery phase. The use of robots in the prevention and preparedness phases of disaster management are outside the scope of this chapter.

This chapter describes the emerging robotics application field of intelligent vehicles – motor vehicles that have autonomous functions and capabilities. The chapter is organized as follows. Section 51.1 provides a motivation of why the development of intelligent vehicles is important, a brief history of the field, and the potential benefits of the technology. Section 51.2 describes the enabling technologies for intelligent vehicles to sense vehicle, environment and driver state, work with digital maps and satellite navigation, and communicate with intelligent transportation infrastructure. Section 51.3 describes the challenges and solutions associated with road scene understanding – a key capability for all intelligent vehicles. Section 51.4 describes advanced driver assistance systems, which use robotics and sensing technologies described earlier to create new safety and convenience systems for motor vehicles, such as collision avoidance, lane keeping, and parking assistance. Section 51.5 describes driver monitoring technologies that are being developed to mitigate driver fatigue, inattention, and impairment. Section 51.6 describes fully autonomous intelligent vehicles systems that have been developed and deployed. The Chapter is concluded in Sect. 51.7 with a discussion of future prospects, while Sect. 51.8 provides references to further reading and additional resources.

The growth of medical robotics since the mid-1980s has been striking. From a few initial efforts in stereotactic brain surgery, orthopaedics, endoscopic surgery, microsurgery, and other areas, the field has expanded to include commercially marketed, clinically deployed systems, and a robust and exponentially expanding research community. This chapter will discuss some major themes and illustrate them with examples from current and past research. Further reading providing a more comprehensive review of this rapidly expanding field is suggested in Sect. 52.4. Medical robots may be classified in many ways: by manipulator design (e.g., kinematics, actuation); by level of autonomy (e.g., preprogrammed versus teleoperation versus constrained cooperative control), by targeted anatomy or technique (e.g., cardiac, intravascular, percutaneous, laparoscopic, microsurgical); or intended operating environment (e.g., in-scanner, conventional operating room). In this chapter, we have chosen to focus on the role of medical robots within the context of larger computer-integrated systems including presurgical planning, intraoperative execution, and postoperative assessment and follow-up. First, we introduce basic concepts of computer-integrated surgery, discuss critical factors affecting the eventual deployment and acceptance of medical robots, and introduce the basic system paradigms of surgical computer-assisted planning, registration, execution, monitoring, and assessment (CAD/CAM) and surgical assistance. In subsequent sections, we provide an overview of the technology of medical robot systems and discuss examples of our basic system paradigms, with brief additional discussion topics of remote telesurgery and robotic surgical simulators. We conclude with some thoughts on future research directions and provide suggested further reading.

The rehabilitationhome-basedroboticshealth care field of rehabilitation robotics develops robotic systems that assist persons who have a disability with necessary activities, or that provide therapy for persons seeking to improve physical or cognitive function. This chapter will discuss both of these domains and provide descriptions of the major achievements of the field over its short history. Specifically, after providing background information on world demographics (Sect. 53.1.2) and the history (Sect. 53.1.3) of the field, Sect. 53.2 describes physical therapy and training robots, and Sect. 53.3 describes robotic aids for people with disabilities. Section 53.4 then briefly discusses recent advances in smart prostheses and orthoses that are related to rehabilitation robotics. Finally, Sect. 53.5 provides an overview of recent work in diagnosis and monitoring for rehabilitation as well as other health-care issues. At the conclusion of this chapter, the reader will be familiar with the history of rehabilitation robotics and its primary accomplishments, and will understand the challenges the field faces in the future as it seeks to improve health care and the well-being of persons with disabilities. In this chapter, we will describe an application of robotics that may in the future touch many of us in an acutely personal way.

Who would not want to have a robot at home that vacuums the house, cleans the kitchen or the bathroom, loads or unloads the dishwasher, or polishes the shoes? In spite of the hundreds of millions of potential customers and users surprisingly few such robots exist. In this chapter, we first look into what it means not only to develop but also to commercialize a domestic robot. Using domestic cleaning robots as a representative example we look into the task details and its context. We also discuss the economic context and the market situation, and the technical challenges which slow down the triumphal procession of domestic robots. We will then have a look at the latest developments of domestic floor cleaning robots, robotic pool cleaners, and window cleaning robots. The survey of domestic cleaning robotics concludes with an outlook to new technologies that might help to solve some of the problems discussed at the beginning. The subsequent section then gives an account on the state of the art in robotic lawn mowing. The Section Smart appliances briefly surveys the latest developments in ironing robotics, intelligent refrigerators, and digital wardrobes. The Section Smart homes looks into a selection of ongoing and completed research projects in the field of smart environments and smart homes. The section Domestic robotics: It is the business case that matters finally concludes with a contemplation of the market situation for domestic robots, business models, and some crucial insights into the commercialization of service robots.

This chapter provides an overview of the key ingredients that make successful education robots possible. Two very popular outlets for public interaction with robots are the robottournamentrobot tournament and the informal learninginformal learning venue (e.g., the science museum). Section 55.2 provides a survey of the very popular world of robot-themed tournaments, which have already impacted tens of thousands of students across diverse geographic and age group boundaries [55.1,2,3,4,5]. Section 55.5 provides an overview of robotic installations in informal learning spaces. Robotic technology has now proven to have sufficient robustness and engagement to be a principal component of interactive exhibitry for a new generation of hands-on, active-learning museums [55.6]. To make interactive, educational robots successful, a new level of technology robustness and standardization is required, and significant progress has been made on this front in the past decade. Educational robot devices consist of both hardware (preassembled or as kits or components) and software (both as source code and programming environments). Section 55.3 discusses physical robot platforms that have achieved notable success, while Sect. 55.4 describes both low-level controllers that interface those platforms to high-level computation, as well as the top-level programming environments themselves. Finally, an important class of tool in the study and execution of educational robotic systems is the ability to evaluate the efficacy of a robot system formally in an educational context. Numerous tools from human–computer interaction, cognitive psychology, and education have demonstrated their usefulness in this regard. Section 55.6 summarizes the manner in which conventional analytical tools may be used to evaluate unconventional educational programs that tap robotic technologies as learning tools across a variety of ages and in both formal and informal learning venues.

Humanoid robots selectively emulate aspects of human form and behavior. Humanoids come in a variety of shapes and sizes, from complete human-size legged robots to isolated robotic heads with human-like sensing and expression. This chapter highlights significant humanoid platforms and achievements, and discusses some of the underlying goals behind this area of robotics. Humanoids tend to require the integration of many of the methods covered in detail within other chapters of this handbook, so this chapter focuses on distinctive aspects of humanoid robotics with liberal cross-referencing. This chapter examines what motivates researchers to pursue humanoid robotics, and provides a taste of the evolution of this field over time. It summarizes work on legged humanoid locomotion, humanoid manipulation, whole-body activities, and approaches to human–robot communication. It concludes with a brief discussion of factors that may influence the future of humanoid robots.

In this chapter, we report on different approaches to dealing with the problem of achieving the best performance under the condition that safety is provided throughout task execution. We also report on intelligent assist devices (IADs) that go beyond conventional notions of robot safety to protect human operators from harm, such as cumulative trauma disorders. However, IADs and other physical human–robot interaction (pHRI) devices are themselves generally powerful enough to cause harm. We argue that the differences between pHRI applications and conventional industrial manipulation requires that safety and reliability standards be rethought, and we offer a preview of the directions currently being undertaken by international standards committees. We chapter discuss the new frontiers of robotic physical interaction with humans, describing motivations and applications of safe pHRI. The state of the art, and the technical challenges to develop new robotic systems for safe and effective collaboration with people, are discussed, subdividing the exposition into hands-off and hands-on pHRI systems. We present an overview of the applicable safety standards and their ongoing development.

This chapter surveys some of the principal research trends in social robotics and its application to human–robot interaction human–robot interaction (HRI)(HRI). Social (or sociable) robots are designed to interact with people in a natural, interpersonal manner – often to achieve social-emotional goals in diverse applications such as education, health, quality of life, entertainment, communication, and collaboration. The long-term goal of creating social robots that are competent and capable partners for people is quite a challenging task. They will need to be able to communicate naturally with people using both verbal and nonverbal signals. They will need to engage us not only on a cognitive level, but on an emotional level as well. They will need a wide range of social-cognitive skills and a theory of other minds to understand human behavior, and to be intuitively understood by people. A deep understanding of human intelligence and behavior across multiple dimensions (i.e., cognitive, affective, physical, social, etc.) is necessary in order to design robots that can successfully play a beneficial role in the daily lives of people. This requires a multidisciplinary approach where the design of social robot technologies and methodologies are informed by robotics, artificial intelligence, psychology, neuroscience, human factors, design, anthropology, and more.

Robot programming by demonstration (PbD) has become a central programming by demonstration (PbD)topic of robotics that spans across general research areas such as human-robot interaction, machine learning, machine vision and motor control. Robot PbD started about 30 years ago, and has grown importantly during the past decade. The rationale for moving from purely preprogrammed robots to very flexible user-based interfaces for training robots to perform a task is three-fold. First and foremost, PbD, also referred to as imitation learning, is a powerful mechanism for reducing the complexity of search spaces for learning. When observing either good or bad examples, one can reduce the search for a possible solution, by either starting the search from the observed good solution (local optima), or conversely, by eliminating from the search space what is known as a bad solution. Imitation learning is, thus, a powerful tool for enhancing and accelerating learning in both animals and artifacts. Second, imitation learning offers an implicit means of training a machine, such that explicit and tedious programming of a task by a human user can be minimized or eliminated (Fig. 59.1). Imitation learning is thus a natural means of interacting with a machine that would be accessible to lay people. Fig. 59.1Left: A robot learns how to make a chess move (namely moving the queen forward) by generalizing across different demonstrations of the task performed in slightly different situations (different starting positions of the hand). The robot records its jointsʼ trajectories and learns to extract what-to-imitate, i.e. that the task constraints are reduced to a subpart of the motion located in a plane defined by the three chess pieces. Right: The robot reproduces the skill in a new context (for different initial position of the chess piece) by finding an appropriate controller that satisfies both the task constraints and constraints relative to its body limitation (how-to-imitate problem), adapted from [59.1] Third, studying and modeling the coupling of perception and action, which is at the core of imitation learning, helps us to understand the mechanisms by which the self-organization of perception and action could arise during development. The reciprocal interaction of perception and action could explain how competence in motor control can be grounded in rich structure of perceptual variables, and vice versa, how the processes of perception can develop as means to create successful actions. PbD promises were thus multiple. On the one hand, one hoped that it would make learning faster, in contrast to tedious reinforcement learning methods or trials-and-error learning. On the other hand, one expected that the methods, being user-friendly, would enhance the application of robots in human daily environments. Recent progresses in the field, which we review in this chapter, show that the field has made a leap forward during the past decade toward these goals. In addition, we anticipate that these promises may be fulfilled very soon. Section 59.1 presents a brief historical overview of robot Programming by Demonstration (PbD), introducing several issues that will be discussed later in this chapter. Section 59.2 reviews engineering approaches to robot PbD with an emphasis on machine learning approaches that provide the robot with the ability to adapt the learned skill to different situations (Sect. 59.2.1). This section discusses also the different types of representation that one may use to encode a skill and presents incremental learning techniques to refine the skill progressively (Sect. 59.2.4). Section 59.2.3 emphasizes the importance to give the teacher an active role during learning and presents different ways in which the user can convey cues to the robot to help it to improve its learning. Section 59.2.4 discusses how PbD can be jointly used with other learning strategies to overcome some limitations of PbD. Section 59.3 reviews works that take a more biological approach to robot PbD and develops models of either the cognitive or neural processes of imitation learning in primates. Finally, Sect. 59.4 lists various open issues in robot PbD that have yet been little explored by the field.

After having stressed the difference between bio-inspired and biomimetic robots, this chapter successively describes bio-inspired morphologies, sensors, and actuators. Then, control architecture that, beyond mere reflexes, implement cognitive abilities like memory or planning, or adaptive processes like learning, evolution and development are described. Finally, the chapter also reports related works on energetic autonomy, collective robotics, and biohybrid robots.

Evolutionary Robotics is a method for automatically generating artificial brains and morphologies of autonomous robots. This approach is useful both for investigating the design space of robotic applications and for testing scientific hypotheses of biological mechanisms and processes. In this chapter we provide an overview of methods and results of Evolutionary Robotics with robots of different shapes, dimensions, and operation features. We consider both simulated and physical robots with special consideration to the transfer between the two worlds.

The lay view of a robot is a mechanical human, and thus robotics has always been inspired by attempts to emulate biology. In this Chapter, we extend this biological motivation from humans to animals more generally, but with a focus on the central nervous systems rather than the bodies of these creatures. In particular, we investigate the sensorimotor loop in the execution of sophisticated behavior. Some of these sections concentrate on cases where vision provides key sensory data. Neuroethology neuroethology is the study of the brain mechanisms underlying animal behavior, and Sect. 62.2 exemplifies the lessons it has to offer robotics by looking at optic flow optic flow in bees, visually guided behavior in frogs, and navigation in rats, turning then to the coordination of behaviors and the role of attention. Brains are composed of diverse subsystems, many of which are relevant to robotics, but we have chosen just two regions of the mammalian brain for detailed analysis. Section 62.3 presents the cerebellum. cerebellum While we can plan and execute actions without a cerebellum, the actions are no longer graceful and become uncoordinated. We reveal how a cerebellum can provide a key ingredient in an adaptive control system, tuning parameters both within and between motor schemas. Section 62.4 turns to the mirror system, mirrorsystemwhich provides shared representations which bridge between the execution of an action and the observation of that action when performed by others. We develop a neurobiological model of how learning may forge mirror neurons for hand movements, provide a Bayesian view of a robot mirror system, and discuss what must be added to a mirror system to support robot imitation. We conclude by emphasizing that, while neuroscience can inspire novel robotic designs, it is also the case that robots can be used as embodied test beds for the analysis of brain models.

Perceptual functions are central to many applications in robotics and for the construction of efficient human–robot interfaces. The study of perception in biological systems has revealed important information-processing principles that have been converted to powerful applications in robotics and computer vision. The chapter first discusses two central theories of object recognition: model- and exemplar-based theories. A review of experimental results from the study of object recognition in biological systems suggests that exemplar-based approaches capture important properties of object recognition in the brain. We then discuss how very similar principles have been realized in highly efficient technical systems for object recognition and detection, including realizations that are based on biologically inspired neural architectures. Principles for the efficient processing of complex shapes can be extended to the representation of complex movements and actions. We illustrate this by first reviewing some properties of the cortical mechanisms of the recognition of complex movements and actions, focusing on principles that are useful for robotics applications. Again, exemplar-based approaches seem to capture important properties of motion recognition in the brain, and at the same time provide a powerful approach for building technical movement recognition systems. Finally, it is shown that the example-based framework is not only useful for recognition, but also provides the basis for powerful synthesis methods. As one example we discuss the synthesis of photorealistic three-dimensional (3-D) models of faces, exploiting correspondencebetween training examples. Related approaches have been developed for spatiotemporal patterns. We review a class of algorithms that permit the accurate modeling of movements and movement styles by interpolation between example trajectories with high relevance for the synthesis of movements, e.g., in humanoid robotics.

The present chapter outlines the main social and ethical issues raised by the ever-faster application of robots to our daily life, and especially to sensitive human areas. Applied to society in numbers and volumes larger than today, robotics is going to trigger widespread social and economic changes, opening new social and ethical problems for which the designers, the end user, the public, and private policy must now be prepared. Starting from a philosophical and sociological review of the depth and extent of the two lemmas of robotics and robot, this section summarizes the recent facts and issues about the relationship between techno-science and ethics. The new applied ethics, called roboethics, is presented. It was put forward in 2001/2002, and publicly discussed in 2004 during the First International Symposium on Roboethics. Some of the issues presented in the chapter are well known to engineers, and less or not known to scholars of humanities, and vice versa. However, because the subject is complex, articulated, and often misrepresented, some of the fundamental concepts relating ethics in science and technology are recalled and clarified. At the conclusion of the chapter is presented a detailed taxonomy of the most significant ethical legal, and societal issues in Robotics. This study is based on the Euron Roboethics Roadmap, and it is the result of three years of discussions and research by and among roboticists and scholars of Humanities. This taxonomy identifies the most evident/urgent/sensitive ethical problems in the main applicative fields of robotics, leaving deeper analysis to further studies.