Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

Preface. 1. Introduction. 2. Rigid Motions and Homogeneous Transformations. 3. Forward and Inverse Kinematics. 4. Velocity Kinematics-The Jacobian. 5. Path and Trajectory Planning. 6. Independent Joint Control. 7. Dynamics. 8. Multivariable Control. 9. Force Control. 10. Geometric Nonlinear Control. 11. Computer Vision. 12. Vision-Based Control. Appendix A: Trigonometry. Appendix B: Linear Algebra. Appendix C: Dynamical Systems. Appendix D: Lyapunov Stability. Index.

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Robot Modeling and Control

Paper: The internal model principle of control theory

Human skin is a remarkable organ. It consists of an integrated, stretchable network of sensors that relay information about tactile and thermal stimuli to the brain, allowing us to maneuver within our environment safely and effectively. Interest in large-area networks of electronic devices inspired by human skin is motivated by the promise of creating autonomous intelligent robots and biomimetic prosthetics, among other applications. The development of electronic networks comprised of flexible, stretchable, and robust devices that are compatible with large-area implementation and integrated with multiple functionalities is a testament to the progress in developing an electronic skin (e-skin) akin to human skin. E-skins are already capable of providing augmented performance over their organic counterpart, both in superior spatial resolution and thermal sensitivity. They could be further improved through the incorporation of additional functionalities (e.g., chemical and biological sensing) and desired properties (e.g., biodegradability and self-powering). Continued rapid progress in this area is promising for the development of a fully integrated e-skin in the near future.

25th Anniversary Article: The Evolution of Electronic Skin (E-Skin): A Brief History, Design Considerations, and Recent Progress

This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Adaptive Control

For pt.I see ibid., p.203-8 (1991). Implementation issues in the context of intelligent dexterous manipulations of objects using robot hands are discussed. A brief overview of manipulation process modeling and an algorithmic procedure for the determination of finger manipulations for a given desired displacement of the object are provided. For purposes of implementation of this model, it is necessary to use, strategically, information at multiple levels of abstraction. The authors highlight this issue and discuss the integrated use of qualitative and quantitative knowledge of the robot grasping situation in order to perform model-based dexterous manipulation. Six relevant issues to be considered in the implementation of dexterous manipulation have been identified, and solutions are suggested, using either qualitative or traditional quantitative analysis methods according to the suitability of either method to the context of the problem. >

Intelligent re-location based manipulation of objects using robot hands. II. Process modelling and implementation issues

Earlier work [1] shows that using nonlinear feedback in the joint space, global practical stability and uniform attractivity of the closed loop robot system can be obtained. Furthermore, the closed loop system maintains robustness with respect to bounded parameter uncertainty, and remains stable after collision with the environment, thus returning to the desired trajectory rapidly. However, most robot tasks are usually specified in task space. This paper extents the earlier work to task space feedback. It is shown that using task space feedback the same results can be achieved, however, it is more effective to handle the trajectory planning and controller design directly in terms of task space. Numerical simulations are presented for illustration.

http://ieeexplore.ieee.org/iel5/5784/15433/00754287.pdf

Robust Control Of Roboyusing Cartesian Space Feedback

For pt.A, see ibid., p.2-7 (2003). In part A of this paper, we developed an intelligent neurofuzzy architecture that can be easily used in the presence of dynamic parameter uncertainty and unmodeled disturbances to control modular and reconfigurable manipulators. The proposed architecture has several levels of hierarchy built on top of a conventional PID controller. The present part B of the paper discussed systematic guidelines to design the skill module of the neurofuzzy control. Such module is used to update the adaptive control parameters of the neurofuzzy architecture when the robotic arm is reconfigured. Furthermore, in this part B of the paper, we present experiments that where conducted on a modular and reconfigurable robot. Some of the most notably significant experimental results are reported.

https://ieeexplore.ieee.org/iel5/8692/27534/01226803.pdf

Hierarchical intelligent control of modular manipulators Part B: Reconfigurability and experimental validation

This paper demonstrates a new approach to achieving decoupled dynamic behavior in multifingered robotic hands. The approach is based on a new concept termed the Grasp Admittance Center. This concept is a generalized version of the Compliance Center concept, well known within the robotics literature for over a decade. The proposed admittance center concept provides a framework for simultaneously achieving four useful features in robotic grasps2: (i) decoupled3 force-motion relationship ? a relationship between a motion imposed on the object and the reaction force that the object exerts to resist the motion; (ii) decoupled time-response ? the motion response of the system to a force disturbance; and (iii) stability ? the ability of the grasp to be in equilibrium despite disturbances. (iv) the task of assigning grasp dynamic behavior becomes transparent. These four features play important roles in the functioning of robotic hands, particularly when engaged in constrained manipulation tasks. Achieving these features in robotic grasps requires that the grasp impedance parameters4 satisfy certain analytical conditions. These conditions are collectively referred to in this work as the admittance center concept. Of the four advantages, conceptualizing the decoupled time response is difficult while the remaining 3 advantages are easily comprehendable. In view of this, the present work describes experiments wherein a carefully built passive compliant device emulates the dynamic behavior of multifingered grasps.

Andrew A. Goldenberg

Papers

Intelligent re-location based manipulation of objects using robot hands. II. Process modelling and implementation issues

Robust Control Of Roboyusing Cartesian Space Feedback

Hierarchical intelligent control of modular manipulators Part B: Reconfigurability and experimental validation

Experimental Demonstration of the Grasp Admittance Center Concept

Design of Position/Force Controller Based on the Principle of Consistency