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Motion Planning

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Springer Handbook of Robotics

Abstract

This chapter first provides a formulation of the geometric path planning problem in Sect. 5.1 and then introduces sampling-based planning in Sect. 5.2. 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. 5.3. These approaches provide theoretical guarantees and for simple planning instances they outperform sampling-based planners. Section 5.4 considers problems that involve differential constraints, while Sect. 5.5 overviews several other extensions of the basic problem formulation and proposed solutions. Finally, Sect. 5.7 addresses some important and more advanced topics related to motion planning.

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Abbreviations

PRM:

probabilistic roadmap method

RLG:

random loop generator

RRT:

rapid random tree

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Authors and Affiliations

  1. Department of Computer Science, MS 132, Rice University, 6100 Main Street, 77005, Houston, TX, USA

    Lydia E. Kavraki Prof

  2. Department of Computer Science, University of Illinois, 201 N. Goodwin Ave, 3318 Siebel Center, 61801, Urbana, IL, USA

    Steven M. LaValle Prof

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  1. Lydia E. Kavraki Prof
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  2. Steven M. LaValle Prof
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Correspondence to Lydia E. Kavraki Prof or Steven M. LaValle Prof .

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  1. PRISMA Lab, Dipartimento di Informatica e Sistemistica, Universitá degli Studi di Napoli Federico II, 80125, Napoli, Italy, Via Claudio 21

    Bruno Siciliano Prof.

  2. Artificial Intelligence Laboratory, Department of Computer Science, Stanford University, 94305-9010, Stanford, CA, USA

    Oussama Khatib Prof.

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Kavraki, L.E., LaValle, S.M. (2008). Motion Planning. In: Siciliano, B., Khatib, O. (eds) Springer Handbook of Robotics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30301-5_6

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