Real-Time Motion Planning with Dynamic Obstacles

Authors

  • Jarad Cannon University of New Hampshire
  • Kevin Rose University of New Hampshire
  • Wheeler Ruml University of New Hampshire

DOI:

https://doi.org/10.1609/socs.v3i1.18249

Keywords:

Real-time search, Heuristic search, Planning, Robotics, Dynamic obstacles

Abstract

Robust robot motion planning in dynamic environments requires that actions be selected under real-time constraints. Existing heuristic search methods that can plan high-speed motions do not guarantee real-time performance in dynamic environments. Existing heuristic search methods for real-time planning in dynamic environments fail in the high-dimensional state space required to plan high-speed actions. In this paper, we present extensions to a leading planner for high-dimensional spaces, R*, that allow it to guarantee real-time performance, and extensions to a leading real-time planner, LSS-LRTA*, that allow it to succeed in dynamic motion planning. In an extensive empirical comparison, we show that the new methods are superior to the originals, providing new state-of-the-art search performance on this challenging problem.

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Published

2021-08-20