Robot Motion Planning: Edition en anglaisOne of the ultimate goals in Robotics is to create autonomous robots. Such robots will accept high-level descriptions of tasks and will execute them without further human intervention. The input descriptions will specify what the user wants done rather than how to do it. The robots will be any kind of versatile mechanical device equipped with actuators and sensors under the control of a computing system. Making progress toward autonomous robots is of major practical inter est in a wide variety of application domains including manufacturing, construction, waste management, space exploration, undersea work, as sistance for the disabled, and medical surgery. It is also of great technical interest, especially for Computer Science, because it raises challenging and rich computational issues from which new concepts of broad useful ness are likely to emerge. Developing the technologies necessary for autonomous robots is a formidable undertaking with deep interweaved ramifications in auto mated reasoning, perception and control. It raises many important prob lems. One of them - motion planning - is the central theme of this book. It can be loosely stated as follows: How can a robot decide what motions to perform in order to achieve goal arrangements of physical objects? This capability is eminently necessary since, by definition, a robot accomplishes tasks by moving in the real world. The minimum one would expect from an autonomous robot is the ability to plan its x Preface own motions. |
Contents
Introduction and Overview | 1 |
Configuration Space of a Rigid Object | 58 |
Obstacles in Configuration Space | 105 |
Roadmap Methods | 153 |
Exact Cell Decomposition | 200 |
Approximate Cell Decomposition | 248 |
Potential Field Methods | 295 |
Multiple Moving Objects | 356 |
Movable Objects | 533 |
Prospects | 587 |
Basic Mathematics | 590 |
Computational Complexity | 599 |
Graph Searching | 603 |
SweepLine Algorithm | 609 |
615 | |
643 | |
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Common terms and phrases
adjacent algebraic algorithm angle assume axis backprojection boundary bounded C-obstacle region called cell decomposition Cfree Chapter commanded velocity computed cone configuration q configuration space connectivity graph consider consists constraints constructed contained convex polygons coordinates corresponding critical curves Cvalid decomposed defined denote described dimension endpoints example exists finite number force free path free space freeway goal configuration grasp Hence illustrates intersection interval Latombe line segment local minimum Lozano-Pérez M-channel MIXED cell motion command motion planning motion planning problem movable object moving nodes nonholonomic obstacles orientation path planning planner planning method polygonal region polynomial potential field potential function preimage projection qgoal rectangloid represented repulsive potential resp rotation Section semi-algebraic semi-algebraic sets sequence Sharir shown in Figure strategy subset tangent tangent space termination condition tion topological space topology Tq(C translate two-dimensional vector vertex visibility graph Voronoi diagram workspace
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Page 641 - Force Feedback Control of Manipulator Fine Motions," ASME Journal of Dynamic Systems, Measurement, and Control, June 1977, pp.
Page 615 - A geometrical approach to planning manipulation tasks — the case of discrete placements and grasps.
Page 633 - DV 1992. Robot Planning. AI Magazine, AAAI Press, 13(2):55-79. [36] Mishra, B., Schwartz, JT, and Sharir, M. 1987. On the Existence and Synthesis of Multifinger Positive Grips. Algorithmica, 2:541-558. [37] Natarajan, BK 1988. On Planning...