Planning algorithms /
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,...
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| Format: | Book |
| Language: | English |
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Cambridge ; New York :
Cambridge University Press,
2006
New York : 2006 Cambridge ; New York : 2006 |
| Subjects: |
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| 100 | 1 | |a LaValle, Steven Michael, |d 1968- |1 http://viaf.org/viaf/34761867 | |
| 100 | 1 | |a LaValle, Steven Michael, |d 1968- | |
| 245 | 1 | 0 | |a Planning algorithms / |c Steven M. Lavalle |
| 260 | |a Cambridge ; |a New York : |b Cambridge University Press, |c 2006 | ||
| 260 | |a New York : |b Cambridge University Press, |c 2006 | ||
| 264 | 1 | |a Cambridge ; |a New York : |b Cambridge University Press, |c 2006 | |
| 300 | |a xvi, 826 p. : |b ill. ; |c 26 cm | ||
| 300 | |a xvi, 826 p. : |b ill. ; |c 27 cm | ||
| 300 | |a xvi, 826 pages : |b illustrations ; |c 27 cm | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 338 | |a volume |b nc |2 rdacarrier | ||
| 500 | |a This WorldCat-derived record is shareable under Open Data Commons ODC-BY, with attribution to OCLC |5 CTY | ||
| 504 | |a Includes bibliographical references (p. 767-810) and index | ||
| 504 | |a Includes bibliographical references (pages 767-810) and index | ||
| 504 | |a Includes bibliographical references and index | ||
| 505 | 0 | |a Discrete planning -- Geometric represenatations and transformations -- The configuration space -- Sampling-based motion planning -- Combinatorial motion planning -- Extensions of basic motion planning -- Feedback motion planning -- Basic decision theory -- Sequential decision theory -- Sensors and information spaces -- Planning under sensing uncertainty -- Differential models -- Sampling-based planning under differential constraints -- System theory and analytical techniques | |
| 505 | 0 | 0 | |g 1 |t Introduction -- |g 2. |t Discrete planning -- |g 3. |t Geometric representations and transformations -- |g 4. |t The configurations space -- |g 5. |t Sampling-based motion planning -- |g 6. |t Combinatorial motion planning -- |g 7. |t Extensions of basic motion planning -- |g 8. |t Feedback motion planning -- |g 9. |t Basic decision theory -- |g 10. |t Sequential decision theory -- |g 11. |t Sensors and information spaces -- |g 12. |t Planning under sensing uncertainty -- |g 13. |t Differential models -- |g 14. |t Sampling-based planning under differential constraints -- |g 15. |t System theory and analytical techniques. |
| 505 | 0 | 0 | |g 1 |t Introduction -- |g 2. |t Discrete planning -- |g 3. |t Geometric representations and transformations -- |g 4. |t configurations space -- |g 5. |t Sampling-based motion planning -- |g 6. |t Combinatorial motion planning -- |g 7. |t Extensions of basic motion planning -- |g 8. |t Feedback motion planning -- |g 9. |t Basic decision theory -- |g 10. |t Sequential decision theory -- |g 11. |t Sensors and information spaces -- |g 12. |t Planning under sensing uncertainty -- |g 13. |t Differential models -- |g 14. |t Sampling-based planning under differential constraints -- |g 15. |t System theory and analytical techniques. |
| 505 | 0 | 0 | |g I |t Introductory Material |g 1 -- |g 1.1 |t Planning to plan |g 3 -- |g 1.2 |t Motivational examples and applications |g 4 -- |g 1.3 |t Basic ingredients of planning |g 14 -- |g 1.4 |t Algorithms, planners, and plans |g 16 -- |g 2 |t Discrete Planning |g 23 -- |g 2.1 |t Introduction to discrete feasible planning |g 24 -- |g 2.2 |t Searching for feasible plans |g 27 -- |g 2.3 |t Discrete optimal planning |g 36 -- |g 2.4 |t Using logic to formulate discrete planning |g 48 -- |g 2.5 |t Logic-based planning methods |g 53 -- |g II |t Motion Planning |g 63 -- |g 3 |t Geometric Representations and Transformations |g 66 -- |g 3.1 |t Geometric modeling |g 66 -- |g 3.2 |t Rigid-body transformations |g 76 -- |g 3.3 |t Transforming kinematic chains of bodies |g 83 -- |g 3.4 |t Transforming kinematic trees |g 93 -- |g 3.5 |t Nonrigid transformations |g 99 -- |g 4 |t The Configuration Space |g 105 -- |g 4.1 |t Basic topological concepts |g 105 -- |g 4.2 |t Defining the configuration space |g 120 -- |g 4.3 |t Configuration space obstacles |g 129 -- |g 4.4 |t Closed kinematic chains |g 139 -- |g 5 |t Sampling-Based Motion Planning |g 153 -- |g 5.1 |t Distance and volume in C-space |g 154 -- |g 5.2 |t Sampling theory |g 161 -- |g 5.3 |t Collision detection |g 173 -- |g 5.4 |t Incremental sampling and searching |g 180 -- |g 5.5 |t Rapidly exploring dense trees |g 189 -- |g 5.6 |t Roadmap methods for multiple queries |g 196 -- |g 6 |t Combinatorial Motion Planning |g 206 -- |g 6.2 |t Polygonal obstacle regions |g 208 -- |g 6.3 |t Cell decompositions |g 218 -- |g 6.4 |t Computational algebraic geometry |g 232 -- |g 6.5 |t Complexity of motion planning |g 247 -- |g 7 |t Extensions of Basic Motion Planning |g 257 -- |g 7.1 |t Time-varying problems |g 257 -- |g 7.2 |t Multiple robots |g 263 -- |g 7.3 |t Mixing discrete and continuous spaces |g 270 -- |g 7.4 |t Planning for closed kinematic chains |g 279 -- |g 7.5 |t Folding problems in robotics and biology |g 287 -- |g 7.6 |t Coverage planning |g 292 -- |g 7.7 |t Optimal motion planning |g 295 -- |g 8 |t Feedback Motion Planning |g 304 -- |g 8.1 |t Motivation |g 304 -- |g 8.2 |t Discrete state spaces |g 306 -- |g 8.3 |t Vector fields and integral curves |g 314 -- |g 8.4 |t Complete methods for continuous spaces |g 328 -- |g 8.5 |t Sampling-based methods for continuous spaces |g 340 -- |g III |t Decision-Theoretic Planning |g 357 -- |g 9 |t Basic Decision Theory |g 360 -- |g 9.1 |t Preliminary concepts |g 361 -- |g 9.2 |t A game against nature |g 368 -- |g 9.3 |t Two-player zero-sum games |g 378 -- |g 9.4 |t Nonzero-sum games |g 386 -- |g 9.5 |t Decision theory under scrutiny |g 393 -- |g 10 |t Sequential Decision Theory |g 408 -- |g 10.1 |t Introducing sequential games against nature |g 408 -- |g 10.2 |t Algorithms for computing feedback plans |g 419 -- |g 10.3 |t Infinite-horizon problems |g 430 -- |g 10.4 |t Reinforcement learning |g 435 -- |g 10.5 |t Sequential game theory |g 442 -- |g 10.6 |t Continuous state spaces |g 455 -- |g 11 |t Sensors and Information Spaces |g 462 -- |g 11.1 |t Discrete state spaces |g 463 -- |g 11.2 |t Derived information spaces |g 472 -- |g 11.3 |t Examples for discrete state spaces |g 480 -- |g 11.4 |t Continuous state spaces |g 487 -- |g 11.5 |t Examples for continuous state spaces |g 494 -- |g 11.6 |t Computing probabilistic information states |g 507 -- |g 11.7 |t Information spaces in game theory |g 512 -- |g 12 |t Planning Under Sensing Uncertainty |g 522 -- |g 12.1 |t General methods |g 523 -- |g 12.2 |t Localization |g 528 -- |g 12.3 |t Environment uncertainty and mapping |g 540 -- |g 12.4 |t Visibility-based pursuit-evasion |g 564 -- |g 12.5 |t Manipulation planning with sensing uncertainty |g 570 -- |g IV |t Planning Under Differential Constraints |g 587 -- |g 13 |t Differential Models |g 590 -- |g 13.1 |t Velocity constraints on the configuration space |g 590 -- |g 13.2 |t Phase space representation of dynamical systems |g 606 -- |g 13.3 |t Basic Newton-Euler mechanics |g 615 -- |g 13.4 |t Advanced mechanics concepts |g 630 -- |g 13.5 |t Multiple decision makers |g 645 -- |g 14 |t Sampling-Based Planning Under Differential Constraints |g 651 -- |g 14.2 |t Reachability and completeness |g 660 -- |g 14.3 |t Sampling-based motion planning revisited |g 670 -- |g 14.4 |t Incremental sampling and searching methods |g 678 -- |g 14.5 |t Feedback planning under differential constraints |g 693 -- |g 14.6 |t Decoupled planning approaches |g 696 -- |g 14.7 |t Gradient-based trajectory optimization |g 707 -- |g 15 |t System Theory and Analytical Techniques |g 712 -- |g 15.1 |t Basic system properties |g 712 -- |g 15.2 |t Continuous-time dynamic programming |g 720 -- |g 15.3 |t Optimal paths for some wheeled vehicles |g 728 -- |g 15.4 |t Nonholonomic system theory |g 736 -- |g 15.5 |t Steering methods for nonholonomic systems |g 753 |
| 520 | |a 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 | ||
| 520 | 1 | |a "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 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." "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."--BOOK JACKET | |
| 520 | 1 | |a "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 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." "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."--Jacket | |
| 520 | 8 | |a 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 | |
| 541 | |3 Eisenhower copy: |c Purchased with funds from the |a Peter M. Fannon Endowment Fund; |d FY2008 |5 MdBJ. | ||
| 583 | 1 | |a Legacy |c 2018 |5 UoY | |
| 590 | |a *lsa 20060911 | ||
| 590 | |a Acquired for the Penn Libraries with assistance from the Class of 1932 Fund | ||
| 650 | 0 | |a Algorithms | |
| 650 | 0 | |a Robots |x Motion |x Planning |0 https://id.loc.gov/authorities/subjects/sh2002006228 | |
| 650 | 0 | |a Robots |x Motion |x Planning | |
| 650 | 6 | |a Algorithmes | |
| 650 | 6 | |a Robots |x Mouvements |x Planification | |
| 650 | 7 | |a Algorithms |2 fast | |
| 650 | 7 | |a Algorithmus |2 gnd | |
| 650 | 7 | |a Automatische Handlungsplanung |2 gnd | |
| 650 | 7 | |a Bewegung |2 gnd | |
| 650 | 7 | |a Inteligência artificial (planejamento) |2 larpcal | |
| 650 | 7 | |a Inteligência artificial |2 larpcal | |
| 650 | 7 | |a Regelungstheorie |2 gnd | |
| 650 | 7 | |a Robots |x Motion |2 fast | |
| 650 | 7 | |a Robots |x Motion |x Planning |2 fast | |
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