An Adaptive Rolling Path Planning Method for Planet Rover in Uncertain Environment

  • Authors:

  • Jinze Song;Bin Dai;Huihai Cui;Enzhong Shan;Hangen He

  • Affiliations:

  • College of Mechatronic Engineering and Automation, National University of Defence Technology, Hunan, P.R. China 410073;College of Mechatronic Engineering and Automation, National University of Defence Technology, Hunan, P.R. China 410073;College of Mechatronic Engineering and Automation, National University of Defence Technology, Hunan, P.R. China 410073;College of Mechatronic Engineering and Automation, National University of Defence Technology, Hunan, P.R. China 410073;College of Mechatronic Engineering and Automation, National University of Defence Technology, Hunan, P.R. China 410073

  • Venue:
  • ICIRA '09 Proceedings of the 2nd International Conference on Intelligent Robotics and Applications
  • Year:
  • 2009

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Abstract

Though path planning methods based on rolling windows have been successfully applied to planet rover in uncertain environment, the efficiency still remains unsatisfactory due to the single-step advancement strategy. In this paper, a novel path planning approach called Rapidly-exploring Random Tree (RRT) is introduced to efficiently find local feasible paths in the region of rolling windows. The planning step-size in each rolling window can be adaptive adjusted according to the local environment it's moving in, which is recorded by historical information perceived by the sensors. Combined with the goal-oriented heuristic strategy, the global collision-free solution path can be generated by successively connecting the local feasible paths. A number of infield experiments demonstrate the effectiveness of the proposed method.