Informatics in Control, Automation and Robotics

Robot manipulators, as general-purpose machines, can be used to perform various tasks. Though, adaptations to specific scenarios require of some technical efforts. In particular, the descriptions of the task result in a robot program which must be modified whenever changes are introduced. Another source of variations are undesired changes due to the entropic properties of systems; in effect, robots must be re-calibrated with certain frequency to produce the desired results. To ensure adaptability, cognitive robotists aim to design systems capable of learning and decision making. Moreover, control techniques such as visual-servoing allow robust control under inaccuracies in the estimates of the system’s parameters. This paper reports the design of a platform called CRR, which combines the computational cognition paradigm for decision making and learning, with the visual-servoing control technique for the automation of manipulative tasks.

Improved Newton solvers, with or without line search, for continuous-time algebraic Riccati equations are discussed. The basic theory and algorithms are briefly presented. Algorithmic details, the computational steps, and convergence tests are described. The main results of an extensive performance investigation of the Newton solvers are compared with those obtained using the widely-used MATLAB solver, care. Randomly generated systems with orders till 2,000, as well as the systems from the large COMPl\(_e\)ib collection of examples, are considered. Significantly improved accuracy, in terms of normalized and relative residuals, and often greater efficiency than for care have been obtained. The results strongly recommend the use of such algorithms, especially for improving the solutions computed by other solvers.

The approach presented in this work focuses on the full state feedback algorithm designed to control the angular velocity of the PMSM and to provide true sine wave of the 3-level neutral point clamped inverter output voltages. Artificial neural network based feedforward path was introduced into control system in order to improve dynamic behaviour of the PMSM during load changing and to reduce the effect of load torque changes. It was shown that gains of the designed controller and feedforward path are non-stationary and depends on the angular velocity. The simulation results demonstrate the advantages of the proposed approach with comparison to state feedback control system without feedforward path.

The potential of an adaptive dynamic programming (ADP)-based control strategy for learning the human gait dynamics in real-time and generating control torque for a prosthetic ankle joint is investigated in this paper. This is motivated by the desire for control strategies which can adapt in real-time to any gait variations in a noisy environment while optimizing some gait related performance indices. The overall amputated leg–prosthetic foot system is represented by a link-segment model with the kinematic patterns for the model are derived from human gait data. Then a learning-based control strategy including an ADP-based controller and augmented learning rules is implemented to generate torque which drives the prosthetic ankle joint along the designed kinematic patterns. Numerical results show that with the proposed learning rules, the ADP-based controller is able to maintain stable gait with robust tracking and reduced performance indices in spite of measurement/actuator noises and variations in walking speed. Promising results achieved in this paper serve as the starting point for the development of intelligent ankle prostheses, which is a challenge due to the lack of adequate mathematical models, the variations in the gait in response to the walking terrain, sensor noises and actuator noises, and unknown intent of users.

Vehicle platoon systems can be considered as good alternative solutions to traffic problems encountered in urban environment. Indeed, they allow to propose new public transportation models based on size adaptable trains. In other fields, such as military operation theaters and agricultural fields, platoon systems can also solve specific problems. The goal of this paper is to propose an agent based decision process that allow to handle the control of vehicles platoon. The decision process is composed of 5 interconnected layers each dealing with one specific aspect of the decision process including the perception interpretation, the spatial configuration choice and the control of the vehicles. This model has been tested in simulation by taking into account several geometrical configurations.

The problem of measuring road vehicle’s weight-in-motion (WIM) is important for overload enforcement, road maintenance planning and cargo fleet managing, control of the legal use of the transport infrastructure, road surface protection from the early destruction and for the safety on the roads. The fibre-optic sensors (FOS) functionality is based on the changes in the transparency of the optical cable due to the deformation of the optical fibre under the weight of the crossing vehicle. It is necessary for WIM measurements to estimate the impact area of a wheel on the working surface of the sensor called tyre footprint. Recorded signals from a truck passing over a group of FOS with various speeds and known weight are used as an input data. The results of the several laboratory and field experiments with FOS, e.g. load characteristics according to the temperature, contact surface width and loading speed impact, are provided here. The method of decomposition of input signal into symmetric and asymmetric components provides the chance to approximate geometric size of tyre surface footprint as well as calculate the weight on each wheel separately. The examples of the estimation of a truck tyre surface footprint using FOS signals, some sources of errors and limitations of possible application for WIM are discussed in this article.

This paper proposes a new control method based on the SMC (sliding mode control) with integral action for traction control of EVs (electric vehicles). The proposed method is the combination of the SMC and the integral action, and it’s able to improve the maneuverability, the stability and the low energy consumption of EVs. The effectiveness of the method is demonstrated via numerical examples.

In this paper, we present a new solution to build a reactive trajectory controller for object manipulation in Human Robot Interaction (HRI) context. Using an online trajectory generator, the controller build a time-optimal trajectory from the actual state to a target situation every control cycle. A human aware motion planner provides a trajectory for the robot to follow or a point to reach. The main functions of the controller are its capacity to track a target, to follow a trajectory with respect to a task frame, or to switch to a new trajectory each time the motion planner provides a new trajectory. The controller chooses a strategy from different control modes depending on the situation. Visual servoing by trajectory generation and control is presented as one application of the approach. To illustrate the potential of the approach, some manipulation results are presented.

When trying to stabilise a dynamical system under the assumption that every communication among the sensors, the actuators and the controller is carried out via a shared communication channel, network-induced constraints come into play. Among such constraints, we address: variable transfer intervals, time varying, large communication delays; non-simultaneous access to the network. In this paper, we devise a method for using an output-feedback dynamic controller whose design is carried out without taking into account the presence of the network. The stability of the resulting nonlinear networked control system is assessed. In order to corroborate the validity of the presented approach, the results of three experiments are presented. Each experiment is carried out using an Ethernet network as the communication medium. One of the experiments involves a real plant, while the remaining have been carried out with simulated plants.

This article proposes a novel real time bug like algorithm for performing a dynamic smooth path planning scheme for an articulated vehicle under limited and sensory reconstructed surrounding static environment. In the general case, collision avoidance techniques can be performed by altering the articulated steering angle to drive the front and rear parts of the articulated vehicle away from the obstacles. In the presented approach factors such as the real dynamics of the articulated vehicle, the initial and the goal configuration (displacement and orientation), minimum and total travel distance between the current and the goal points, and the geometry of the operational space are taken under consideration to calculate the update on the future way points for the articulated vehicle. In the sequel the produced path planning is iteratively smoothed online by the utilization of Bezier lines before producing the necessary rate of change for the vehicle’s articulated angle. The efficiency of the proposed scheme is being evaluated by multiple simulation studies that simulate the movement of the articulated vehicle in open and constrained spaces with the existence of multiple obstacles.

Simultaneous Localization and Mapping (SLAM) is a central problem for autonomous mobile robotics. Monocular SLAM is one of the ways to tackle the problem, where the only input information are the images from a moving camera. Current approaches for this problem have achieved a good balance between accuracy and density of the map, however, they are not suited for large scale. In this paper, we present a dynamic mapping strategy where the metric map is divided into regions with highly connected observations, resulting in a topological structure which permits the efficient augmentation and optimization of the map. For that, a graph representation where the nodes represent keyframes, and their connections are a measure of their overlapping, is continuously rearranged. The experiments show that this hybrid metric-topological approach outperforms the efficiency and scalability of previous approaches.

In this paper, we proposeFushimi, S. a novel virtual trainingSasaki, T. system capable of shorteningNyioh, Y. the training periodTerashima, K. of unskilled crane operators. First, a simulator representing the motion behavior of load and boom during transfer operation in crane’s cockpit is newly built. Second, referring to such the sway suppression skill taught in crane driving school, sway suppression control input is theoretically derived. Thirdly, a learning support method with ideal operation of joystick motion or visual sensory information to facilitate acquisition of the sway-suppression skill for unskilled operators is proposed. Finally, a lot of experiments were performed to validate the effectiveness of the proposed learning support method.

Simultaneous localization and mapping is a situation in which a mobile robot travels through an environment and concurrently makes a momentary map of the environment and uses that map to localize. The simultaneous localization and mapping is currently one of the most challenging problems in the field of autonomous mobile robots and providing a solution to SLAM may open doors to the world of truly autonomous robots. This paper provides a novel approach to Simultaneous Localization and Mapping problem based on estimation approach. The new approach is called Unscented HybridSLAM filter which addresses the linearization process of an autonomous mobile robot utilizing the second order Sterling polynomial interpolation specifically used for Unscented HybridSLAM algorithm. Using computer simulations, Unscented HybridSLAM and the associated theoretical interpolation is examined for a double-loop scenario and the efficacy of the Unscented HybridSLAM is validated.

Pose-based features have demonstrated to outperform low-levelappearance features in human action recognition. New RGB-D cameras provide locations of human joints with which geometric correspondences can be easily calculated. In this article, a new geometric correspondence between joints called Trisarea feature is presented. It is defined as the area of the triangle formed by three joints. Relevant triangles describing human pose are identified and it is shown how the variation over time of the selected Trisarea features constitutes a descriptor of human action. Experimental results show a comparison with other methods and demonstrate how this Trisarea-based representation can be applied to human action recognition.

We investigate the problem of estimating the local geometric scene structure of urban street canyons captured from an egocentric viewpoint with a small-baseline stereo camera setup. We model the facades of buildings as planar surfaces and estimate their parameters based on a dense disparity map as only input. After demonstrating the importance of considering the stereo reconstruction uncertainties, we present two approaches to solve this model-fitting problem. The first approach is based on robust planar segmentation using random sampling, the second approach transforms the disparity into an elevation map from which the main building orientations can be obtained. We evaluate both approaches on a set of challenging inner city scenes and show how visual odometry can be incorporated to keep track of the estimated geometry in real-time.

In a cooperation task by multiple robots, it may happen that a working robot can not detect a target object for handling due to a sensor occlusion. In this situation, if another cooperative robot observes the working robot with the target object and detects their positions and orientations, it will be possible for the working robot to complete the handling task. Such behavior is a kind of indirect cooperation. This study proposes a method for such an indirect cooperation based on an observation by the partner robot. The observing robot obtains corresponding points of Scale-Invariant Feature Transformation (SIFT) on the working robot with hand and the target object from multiple captured images. The 3-D position of the target object and hand motion of the working robot can be detected by applying stereo vision theory to the points. The working robot is then able to get the relation between its hand and the target object indirectly from the observing robot. We describe each process to establish the indirect cooperation. Fundamental experiments confirmed the validity of presented method.

A judicious choice of the state-space realization is required in order to account for the assumed smoothness of the state-space matrices with respect to the design parameters. The direct parameterization of poles and residues may be not appropriate, due to their possible non-smooth behavior with respect to design parameters. This is avoided in the proposed technique, by converting the pole-residue description to a Sylvester description which is computed for each root macromodel. This technique is used in combination with suitable parameterizing schemes for interpolating a set of state-space matrices, and hence the poles and residues indirectly, in order to build accurate parametric macromodels. The key features of the present approach are first the choice of a proper pivot matrix and second, finding a well-conditioned solution of a Sylvester equation. Stability and passivity are guaranteed by construction over the design space of interest. Pertinent numerical examples validate the proposed Sylvester technique for parametric macromodeling.

The Bates stochastic volatility model is widely used in the finance problem and the sequential parameter estimation problem becomes important. By using the exact simulation technique, a particle filter for estimating stochastic volatility is constructed. The system parameters are sequentially estimated with the aid of parallel filtering algorithm with the new resampling procedure. The proposed filtering procedure is also applied to the modeling of the term structure dynamics. Simulation studies for checking the feasibility of the developed scheme are demonstrated.

This work—the development of new control strategies and sensor models—is motivated by the open question which occurred during analysis of the functional morphology of vibrissal sensor systems. The reception of vibrations is a special sense of touch, important for many insects and vertebrates. The latter realize this reception by means of hair-shaped vibrissae, to acquire tactile information about their environments. The vibrissa receptors are in a permanent state of adaption to filter the perception of tactile stimuli. This behavior now may be mimicked by an artificial sensor system. The sensor system is modeled as a spring-mass-damper system with relative degree two and the system parameters are supposed to be unknown, due to the complexity of biological systems. Using a simple linear model of a sensory system adaptive controllers are considered which compensate unknown permanent ground excitations. The working principle of each controller (feedback law including adaptor) is shown in numerical simulations which prove that these controllers in fact work successfully and effectively. Moreover, practical implementation of these controllers to a demonstrator in form of an electrical oscillating circuit results in various successful experiments which confirm the theoretical results.

Implementation of logic control algorithms in embedded systems is limited by space and response time. Use of a single look-up table (LUT) for multiple-output Boolean function is almost always excluded due to the LUT size. The paper deals with implementation in a form of cascade of smaller LUTs with response time given by a series of few look-ups. Cascade length and memory required to store LUTs can be varied and it is shown that an optimal trade-offs can be reached. Changes in logic control can be implemented easily by re-loading data into LUTs. The presented method is thus useful for logic control in embedded systems or in microcontroller software.

Many manufacturing processes involve using a multitude of intermediate products to make a few final products. The decision regarding which intermediate products go to make which final product involves engineering the value of the final products with respect to several properties and the amounts. Even when each property “mixes linearly,” this decision is complex in cases where no mathematical function is known for the relationship between the value of a product and its properties. The complexity of a tool to support this decision making is further aggravated in cases where an intermediate product cannot contribute to more than one final product, e.g. due to mechanical limitations, process constraints, logistics restrictions, or traceability considerations. For this situation, an interactive decision-support tool is developed, and applied to the sensitive example of flour mills, where up to 80 intermediate products, 6 final products, and 6 properties are involved during continuous mixing of flours. The tool allows the head miller to flexibly specify the feasible space in the dimensions of decision variables, properties, and amounts. For any change in this specification, the tool computes and presents without prohibitive time lag a convenient overview of all relevant non-inferior solutions, to facilitate selection of a particular solution. The head miller makes specifications and selects a solution for one final product at a time, usually starting with the most valuable product, but can iterate back to any product at will. Better and more reliable mixing decisions are achieved with the support of the tool.

Process planning and process design to identify stable process areas is nowadays characterized by time-consuming correction loops, where the number of iterations and the effort involved are mostly from the experience and knowledge of process designer. This requires on the one hand additional planning steps as deriving process parameters and secondly an evaluation of the achieved product quality. By using the macro simulation model introduced in this paper, the computational complexity to obtain significant process knowledge is decreased and thus made accessible more easily. Detailed tool-workpiece engagement is calculated through the presented model, which co-relates to mechanical and thermal stresses on the tool. Based on the calculations the process can be designed by reducing the tool load in the course of the process. This way, the tool life of the used milling cutters can be significantly increased resulting in an increase of process robustness and efficiency, thereby reducing used resources.