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Motion Planning and Obstacle Avoidance

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Springer Handbook of Robotics

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Abstract

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. 47.1–47.6) and obstacle avoidance (Sects. 47.7–47.10) 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.

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Abbreviations

3-D:

three-dimensional

DD:

differentially driven

DWA:

dynamic window approach

HSGR:

high safety goal

HSWR:

high safety wide region

IJCAI:

International Joint Conference on Artificial Intelligence

LARC:

Lie algebra rank condition

ND:

nearness diagram navigation

ORM:

obstacle restriction method

PFM:

potential field method

PRM:

probabilistic roadmap method

RRT:

rapidly exploring random tree

RS:

Reeds and Shepp

VFH:

vector field histogram

VO:

velocity obstacle

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Author information

Authors and Affiliations

  1. Department of Computer Science and Systems Engineering, University of Zaragoza, C/María de Luna 1, 50018, Zaragoza, Spain

    Javier Minguez

  2. LAAS-CNRS, 7 Avenue du Colonel Roche, 31077, Toulouse, France

    Florant Lamiraux & Jean-Paul Laumond

Authors
  1. Javier Minguez
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  2. Florant Lamiraux
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  3. Jean-Paul Laumond
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Corresponding author

Correspondence to Javier Minguez .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Naples Federico II, Via Claudio 21, 80125, Naples, Italy

    Bruno Siciliano

  2. Department of Computer Sciences, Artificial Intelligence Laboratory, Stanford University, 450 Serra Mall, CA 94305, Stanford, USA

    Oussama Khatib

Video-References

Video-References

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Sensor-based trajectory deformation and docking for nonholonomic mobile robots available from http://handbookofrobotics.org/view-chapter/47/videodetails/80

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Autonomous robotic smart wheelchair navigation in an urban environment available from http://handbookofrobotics.org/view-chapter/47/videodetails/707

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Sena wheelchair: Autonomous navigation at University of Malaga (2007) available from http://handbookofrobotics.org/view-chapter/47/videodetails/708

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Robotic wheelchair: Autonomous navigation with Google Glass available from http://handbookofrobotics.org/view-chapter/47/videodetails/709

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A ride in the google self driving car available from http://handbookofrobotics.org/view-chapter/47/videodetails/710

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Mobile robot navigation system in outdoor pedestrian environment available from http://handbookofrobotics.org/view-chapter/47/videodetails/711

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Mobile robot autonomous navigation in Gracia district, Barcelona available from http://handbookofrobotics.org/view-chapter/47/videodetails/712

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Autonomous navigation of a mobile vehicle available from http://handbookofrobotics.org/view-chapter/47/videodetails/713

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Autonomous robot cars drive DARPA Urban challenge available from http://handbookofrobotics.org/view-chapter/47/videodetails/714

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Minguez, J., Lamiraux, F., Laumond, JP. (2016). Motion Planning and Obstacle Avoidance. In: Siciliano, B., Khatib, O. (eds) Springer Handbook of Robotics. Springer Handbooks. Springer, Cham. https://doi.org/10.1007/978-3-319-32552-1_47

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  • DOI: https://doi.org/10.1007/978-3-319-32552-1_47

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