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.

/pdf/robot-modeling-and-control-2n3memrp6g.pdf

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

An architecture for intelligent control of a robot that interacts with a dynamically changing environment is proposed. The problems that are encountered in the implementations of low-level controllers for such robotics applications and their inherent limitations are outlined. An intelligent monitor is proposed to overcome these problems. Since this intelligent interface can change the desired trajectory and thereby affect the stability of the low-level controller, a stability analysis of the integrated controller and the interface is provided. This conceptual architecture is an attempt to emulate humans in employing common-sense reasoning in dynamic decision making and control. It is strongly believed that this approach will enable the controller to generate control actions in a more 'humanlike' manner than the current implementations of learning control. >

An intelligent controller for robot contact and non-contact task control

In this paper, we present a technique for planning and stabilization of motion in a class of multivariable nonlinear (nonholonomic) systems, and apply this technique to the free-flying manipulators. The controller architecture designed in the paper is based on the radial basis function (RBF) network approximation of an optimal control program for any desired motion. An additional control level for sampled feedback compensation of the approximation errors is also proposed. The presented RBF-based control technique overcomes certain problems associated with other control approaches recently proposed for nonholonomic systems. An example and numerical simulations are provided for illustration. >

RBF network architecture for motion planning and attitude stabilization of nonholonomic spacecraft/manipulator systems

The objective of path planning is to find a sequence of states that a system has to visit in order to attain the goal state. Because of their real-time efficiency, potential field methods present a powerful heuristic to guide thissearch. However, potential field approaches can not guarantee goal attainability. They are often referred to as"local methods" and are used in conjunction with a global path planning method to ensure completeness of thepath planning algorithm. The present work introduces a novel methodology for path planning which combines thereal-time efficiency of potential field methods with goal-attainability characteristic of global methods (such as A*).The algorithm of this work is: i) free from local minima; ii) capable of considering arbitrary-shaped obstacles (nogeometric approximation is required); iii) computationally less complex than previous search methods; and iv) ableto handle obstacle avoidance and goal attainability at the same time. At the first step a new probabilistic scheme,based on absorbing Markov chains, is presented for global planning inside structured environments, such as office,etc. The potential field method is then reformulated for adaptive path planning among modeled and new obstacles.Keywords : Mobile robots, Motion planning, Potential field, Adaptive planning, Markov Chains, HarmonicFunctions, BIE

Adaptive and less-complex path-planning behavior for mobile robots

A terrain adaptive tracked vehicle (or mobile robot) with variable configurations was developed with an enhanced maneuverability to travel over a variety of rough and unpredictable surfaces. The active terrain adaptability of the vehicle is achieved through a pair of flippers, i.e. track configuration-controlling mechanisms. The mechanism can change and control track’s configurations to adapt all terrains, provide the tracks with a continuously invariable spring force to tension tracks, and adjust the center of gravity of the vehicle to prevent it turning over from slopes or obstacles. This technology has been used in the development of an innovative Micro-rover for the space exploration and a series of EOD mobile robots. On the other hand, its derivative, the Dual Mode Vehicle (DMV) was invented and might be beneficial to switch intermittently between wheels and tracks without manual conversion. If the terrain is unknown or unstructured, or a security mission in an urban setting that involves flat terrain (street) and stairs, a wheeled vehicle may be unsuitable for some of tasks and a tracked vehicle may be too slow for most of tasks.

Development of a Terrain Adaptive Tracked Vehicle and Its Derivative-Dual Mode Vehicle *

This work proposes a design of the car motion controller and an application of this controller to parallel parking of a passenger car. The approach is based on the combination of feedback and feedforward control. The paper presents the results of the controller design and analysis, including the feedback controller stability analysis, and system analysis of the parking problem. The general case of parallel parking considered in this work is emulated using the proposed controller.

Andrew A. Goldenberg

Papers

An intelligent controller for robot contact and non-contact task control

RBF network architecture for motion planning and attitude stabilization of nonholonomic spacecraft/manipulator systems

Adaptive and less-complex path-planning behavior for mobile robots

Development of a Terrain Adaptive Tracked Vehicle and Its Derivative-Dual Mode Vehicle *

An architecture for control of car motion and application to parking