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Motion Planning

  • Lydia E. Kavraki
  • Steven M. LaValle
Part of the Springer Handbooks book series (SHB)

Abstract

This chapter first provides a formulation of the geometric path planning problem in Sect. 7.2 and then introduces sampling-based planning in Sect. 7.3. Sampling-based planners are general techniques applicable to a wide set of problems and have been successful in dealing with hard planning instances. For specific, often simpler, planning instances, alternative approaches exist and are presented in Sect. 7.4. These approaches provide theoretical guarantees and for simple planning instances they outperform sampling-based planners. Section 7.5 considers problems that involve differential constraints, while Sect. 7.6 overviews several other extensions of the basic problem formulation and proposed solutions. Finally, Sect. 7.8 addresses some important and more advanced topics related to motion planning.

Keywords

Motion Planning Planning Algorithm Cell Decomposition Differential Constraint Belief Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
1-D

one-dimensional

2-D

two-dimensional

3-D

three-dimensional

PRM

probabilistic roadmap method

RDT

rapidly exploring dense tree

RLG

random loop generator

RRT

rapidly exploring random tree

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceRice UniversityHoustonUSA
  2. 2.Department of Computer ScienceUniversity of IllinoisUrbanaUSA

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