Springer Handbook of Robotics

Table of Contents

1. Frontmatter

2. 1. Robotics and the Handbook

   Bruno Siciliano, Oussama Khatib

   Abstract

   Robots! Robots on Mars and in oceans, in hospitals and homes, in factories and schools; robots fighting fires, making goods and products, saving time and lives. Robots today are making a considerable impact on many aspects of modern life, from industrial manufacturing to healthcare, transportation, and exploration of the deep space and sea. Tomorrow, robots will be as pervasive and personal as today’s personal computers. This chapter retraces the evolution of this fascinating field from the ancient to the modern times through a number of milestones: from the first automated mechanical artifact (1400 BC) through the establishment of the robot concept in the 1920s, the realization of the first industrial robots in the 1960s, the definition of robotics science and the birth of an active research community in the 1980s, and the expansion towards the challenges of the human world of the twenty-first century. Robotics in its long journey has inspired this handbook which is organized in three layers: the foundations of robotics science; the consolidated methodologies and technologies of robot design, sensing and perception, manipulation and interfaces, mobile and distributed robotics; the advanced applications of field and service robotics, as well as of human-centered and life-like robotics.

3. Robotics Foundations

   1. Frontmatter

   2. 2. Kinematics

      Kenneth J. Waldron, James Schmiedeler

      Abstract

      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.

   3. 3. Dynamics

      Roy Featherstone, David E. Orin

      Abstract

      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

      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

   4. 4. Mechanism and Actuation

      Victor Scheinman, J. Michael McCarthy, Jae-Bok Song

      Abstract

      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 (Sect. 4.1) discuss characteristics of the mechanisms and actuation that affect the performance of a robot. Sections 4.2–4.6 discuss the basic features of a robot manipulator and their relationship to the mathematical model that is used to characterize its performance. Sections 4.7 and 4.8 focus on the details of the structure and actuation of the robot and how they combine to yield various types of robots. The final Sect. 4.9 relates these design features to various performance metrics.

   5. 5. Sensing and Estimation

      Henrik I. Christensen, Gregory D. Hager

      Abstract

      Sensing and 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, 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.

   6. 6. Model Identification

      John Hollerbach, Wisama Khalil, Maxime Gautier

      Abstract

      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.

   7. 7. Motion Planning

      Lydia E. Kavraki, Steven M. LaValle

      Abstract

      This chapter first provides a formulation of the geometric path planning problem in Sect. 7.2 and then introduces sampling-based planning in Sect. 7.3. 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. 7.4. These approaches provide theoretical guarantees and for simple planning instances they outperform sampling-based planners. Section 7.5 considers problems that involve differential constraints, while Sect. 7.6 overviews several other extensions of the basic problem formulation and proposed solutions. Finally, Sect. 7.8 addresses some important and more advanced topics related to motion planning.

   8. 8. Motion Control

      Wan Kyun Chung, Li-Chen Fu, Torsten Kröger

      Abstract

      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. 8.1. The joint and operational space control approaches, two different viewpoints on control of robot manipulators, are compared in Sect. 8.2. Independent joint control and proportional–integral–derivative (PID) control, widely adopted in the field of industrial robots, are presented in Sects. 8.3 and 8.4, respectively. Tracking control, based on feedback linearization, is introduced in Sect. 8.5. The computed-torque control and its variants are described in Sect. 8.6. Adaptive control is introduced in Sect. 8.7 to solve the problem of structural uncertainty, whereas the optimality and robustness issues are covered in Sect. 8.8. To compute suitable set point signals as input values for these motion controllers, Sect. 8.9 introduces reference trajectory planning concepts. Since most controllers of robot manipulators are implemented by using microprocessors, the issues of digital implementation are discussed in Sect. 8.10. Finally, learning control, one popular approach to intelligent control, is illustrated in Sect. 8.11.

   9. 9. Force Control

      Luigi Villani, Joris De Schutter

      Abstract

      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.

   10. 10. Redundant Robots

       Stefano Chiaverini, Giuseppe Oriolo, Anthony A. Maciejewski

       Abstract

       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 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 when a redundant manipulator is used to execute a repetitive task, e. g., in industrial applications. The use of kinematic redundancy for fault tolerance is analyzed in detail. Suggestions for further reading are given in a final section.

   11. 11. Robots with Flexible Elements

       Alessandro De Luca, Wayne J. Book

       Abstract

       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 into 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.

   12. 12. Robotic Systems Architectures and Programming

       David Kortenkamp, Reid Simmons, Davide Brugali

       Abstract

       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. 13), executives, and task planners (Chap. 14) – 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.

   13. 13. Behavior-Based Systems

       François Michaud, Monica Nicolescu

       Abstract

       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 perception, time constraints, limited knowledge about the world, cognition, reasoning and 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 creature’s 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 explain behavior-based systems and their use in autonomous control problems and applications. The chapter is organized as follows. Section 13.1 overviews robot control, introducing behavior-based systems in relation to other established approaches to robot control. Section 13.2 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. 13.3. Section 13.4 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 13.5 presents several different classes of learning methods for behavior-based systems, validated on single-robot and multi-robot systems. Section 13.6 provides an overview of various robotics problems and application domains that have successfully been addressed or are currently being studied with behavior-based control. Finally, Sect. 13.7 concludes the chapter.

   14. 14. AI Reasoning Methods for Robotics

       Michael Beetz, Raja Chatila, Joachim Hertzberg, Federico Pecora

       Abstract

       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. Moreover, recent knowledge about the robot’s environment cannot be given a priori, but needs to be updated from sensor data, involving challenging problems of symbol grounding and knowledge base change.

       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. The chapter first gives a motivation by example, to what extent symbolic reasoning has the potential of helping robots perform in the first place. Then (Sect. 14.2), we sketch the landscape of representation languages available for the endeavor. After that (Sect. 14.3), we present approaches and results for several types of practical, robotics-related reasoning tasks, with an emphasis on temporal and spatial reasoning. Plan-based robot control is described in some more detail in Sect. 14.4. Section 14.5 concludes.

   15. 15. Robot Learning

       Jan Peters, Daniel D. Lee, Jens Kober, Duy Nguyen-Tuong, J. Andrew Bagnell, Stefan Schaal

       Abstract

       Machine learning offers to robotics a framework and set of tools for the design of sophisticated and hard-to-engineer behaviors; conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in robot learning. The relationship between disciplines has sufficient promise to be likened to that between physics and mathematics. In this chapter, we attempt to strengthen the links between the two research communities by providing a survey of work in robot learning for learning control and behavior generation in robots. We highlight both key challenges in robot learning as well as notable successes. We discuss how contributions tamed the complexity of the domain and study the role of algorithms, representations, and prior knowledge in achieving these successes. As a result, a particular focus of our chapter lies on model learning for control and robot reinforcement learning. We demonstrate how machine learning approaches may be profitably applied, and we note throughout open questions and the tremendous potential for future research.