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

The article addresses the control problem associated with tracking a trajectory which is predefined by the desired position and orientation of the end-effector tip (stage of nominal control). The problem is compounded by the assumption that variations in system parameters may occur thereby requiring that the nominal control be complemented with a controller (stage of perturbed control) which would regulate the disturbance produced by the variation in system parameters. A new formulation of the robot model for control synthesis is presented. The new formulation facilitates the synthesis of regulators for parameter variations using a robust servomechanism approach or a force feedback approach. The two approaches are illustrated using examples. The first example uses robust regulation to reject step input disturbances. The second example simulates the operation of peg-in-hole insertion using force feedback with an LQ controller.

Contribution to synthesis of manipulator control

In this paper a new method of modeling nonlinear friction as a function of both position and velocity is introduced. The proposed empirical model uses spectral analysis to identify the main sources of position dependent friction in complex mechanisms. In addition, the model describes the contribution to the overall friction of every moving part of the mechanism. In the paper, the proposed spectral-based modeling technique is used to describe static friction and sliding friction (which includes negative damping friction) of the first joint of PUMA 560 robot. This is the first friction model truly capable of describing nonlinear friction in complex mechanical systems.

Modeling of Nonlinear Friction in Complex Mechanisms Using Spectral Analysis

This study deals with feedback control of system configuration drift resulting from the presence of the nonholonomic constraints, as compared to drift resulting from the kinematic redundancy of manipulators. The authors analyze the origins of this nonholonomic drift and formulate the problem to be solved to control it. They present an approach for globally optimal kinematic control of nonholonomic drift in robotic systems that is based on the utilization of task functions. The approach is applied to 3-D rolling manipulation systems to control nonholonomic drift of finger/object contact locations. >

http://ieeexplore.ieee.org/iel2/629/5875/00225106.pdf

Kinematic control of non-holonomic drift and its application to 3-D rolling manipulation-task gradient technique

In recent years several research groups have introduced the concept of modular and reconfigurable robotics (MRR) as a means for flexible automation. This concept allows for the execution of many complex tasks that cannot be performed by fixed configuration manipulators. Nevertheless, reconfigurable robots introduce a level of complexity to the problem of design of controllers that can handle a wide range of robot configurations with uniform and reliable performance. In parts A and B of this paper, we develop an intelligent control architecture that can be easily used in the presence of dynamic parameter uncertainty and unmodeled disturbances. The proposed architecture has several levels of hierarchy built on top of a conventional PID controller. Systematic design steps of the proposed intelligent control architecture are presented in Part A of this publication.

http://ieeexplore.ieee.org/iel5/8692/27534/01226746.pdf

Hierarchical intelligent control of modular manipulators Part A: neurofuzzy control design

The robustness performance of the fuzzy inference mechanism is investigated in terms of maximum deviation of the fuzzy and crisp output as a result of deviation in the input membership grades. A parameterized formulation of fuzzy reasoning helps us adjust the robustness by varying the inference parameters. This feature will improve the generalization capability of the fuzzy logic models as illustrated in an example.

Andrew A. Goldenberg

Papers

Contribution to synthesis of manipulator control

Modeling of Nonlinear Friction in Complex Mechanisms Using Spectral Analysis

Kinematic control of non-holonomic drift and its application to 3-D rolling manipulation-task gradient technique

Hierarchical intelligent control of modular manipulators Part A: neurofuzzy control design

On the robustness of fuzzy inference mechanism