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Self-learning RRT* Algorithm for Mobile Robot Motion Planning in Complex Environments

  • Xu Zhang
  • Felix Lütteke
  • Christian Ziegler
  • Jörg Franke
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 302)

Abstract

RRT* is a practical and efficient incremental sampling-based motion planning algorithm. However, its searching ability is quite inefficient in some cases, due to relying on uniform random sampling like other RRT-based algorithms without taking the environment information and prior knowledge into account, which particularly leads to many sampling failures or generation of useless nodes in complex environments. In this paper, we propose an extension of RRT* based on a self-learning strategy and a hybrid-biased sampling scheme to improve the planning efficiency. By taking advantage of the prior knowledge accumulation and cost estimation, the searching tree has higher probability and success rate to extend in difficult areas. We also demonstrate the performance of our algorithm by building some simulation environments for our mobile robot and conclude with the results compared with RRT*.

Keywords

Motion planning RRT* Biased sampling Mobile robot 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Xu Zhang
    • 1
  • Felix Lütteke
    • 1
  • Christian Ziegler
    • 1
  • Jörg Franke
    • 1
  1. 1.Institute for Factory Automation and Production Systems (FAPS)University of Erlangen-NurembergErlangenGermany

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