Motion Planning and Obstacle Avoidance

  • Javier MinguezEmail author
  • Florant Lamiraux
  • Jean-Paul Laumond
Part of the Springer Handbooks book series (SHB)


This chapter describes motion planning and obstacle avoidance for mobile robots. We will see how the two areas do not share the same modeling background. From the very beginning of motion planning, research has been dominated by computer sciences. Researchers aim at devising well-grounded algorithms with well-understood completeness and exactness properties.

The challenge of this chapter is to present both nonholonomic motion planning (Sects. 47.147.6) and obstacle avoidance (Sects. 47.747.10) issues. Section 47.11 reviews recent successful approaches that tend to embrace the whole problem of motion planning and motion control. These approaches benefit from both nonholonomic motion planning and obstacle avoidance methods.


Mobile Robot Motion Planning Obstacle Avoidance Nonholonomic System Nonholonomic Constraint 
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.



differentially driven


dynamic window approach


high safety goal


high safety wide region


International Joint Conference on Artificial Intelligence


Lie algebra rank condition


nearness diagram navigation


obstacle restriction method


potential field method


probabilistic roadmap method


rapidly exploring random tree


Reeds and Shepp


vector field histogram


velocity obstacle


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Javier Minguez
    • 1
    Email author
  • Florant Lamiraux
    • 2
  • Jean-Paul Laumond
    • 2
  1. 1.Department of Computer Science and Systems EngineeringUniversity of ZaragozaZaragozaSpain
  2. 2.LAAS-CNRSToulouseFrance

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