Motion Planning by Sampling in Subspaces of Progressively Increasing Dimension

Marios Xanthidis, Joel M. Esposito, Ioannis Rekleitis, Jason M. O'Kane
Journal of Intelligent and Robotic Systems
vol. 100
pp. 777–789

Abstract This paper introduces an enhancement to traditional sampling-based planners, resulting in efficiency increases for high-dimensional holonomic systems such as hyper-redundant manipulators, snake-like robots, and humanoids. Despite the performance advantages of modern sampling-based motion planners, solving high dimensional planning problems in near real-time remains a considerable challenge. The proposed enhancement to popular sampling-based planning algorithms is aimed at circumventing the exponential dependence on dimensionality, by progressively exploring lower dimensional volumes of the configuration space. Extensive experiments comparing the enhanced and traditional version of RRT, RRT-Connect, and Bidirectional T-RRT on both a planar hyper-redundant manipulator and the Baxter humanoid robot show significant acceleration, up to two orders of magnitude, on computing a solution. We also explore important implementation issues in the sampling process and discuss the limitations of this method.

  author = {Marios Xanthidis and Joel M. Esposito and Ioannis Rekleitis
            and Jason M. O'Kane},
  journal = {Journal of Intelligent and Robotic Systems},
  pages = {777--789},
  title = {Motion Planning by Sampling in Subspaces of Progressively
           Increasing Dimension},
  volume = {100},
  year = {2020}

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