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Model-Based Path Planning

  • Artur Wolek
  • Craig A. Woolsey
Chapter
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 474)

Abstract

Model-based path planning for autonomous vehicles may incorporate knowledge of the dynamics, the environment, the planning objective, and available resources. In this chapter, we first review the most commonly used dynamic models for autonomous ground , surface, underwater, and air vehicles. We then discuss five common approaches to path planning—optimal control , level set methods, coarse planning with path smoothing , motion primitives, and random sampling—along with a qualitative comparison. The chapter includes a brief interlude on optimal path planning for kinematic car models. The aim of this chapter is to provide a high-level introduction to the field and to suggest relevant topics for further reading.

Keywords

Path Planning Feasible Path Motion Primitive Path Planning Problem Underwater Glider 
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.

Notes

Acknowledgements

We thank Thomas Battista, Mazen Farhood, David Grymin, James McMahon, and Michael Otte for reviewing a draft of this chapter and providing helpful comments. The first author would also like to acknowledge support from the American Society for Engineering Education’s NRL Postdoctoral Fellowship program. The second author gratefully acknowledges the support of the ONR under Grant Nos. N00014-14-1-0651 and N00014-16-1-2749.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.American Society for Engineering EducationWashington D.C.USA
  2. 2.Virginia TechBlacksburgUSA

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