Fast‐RRT*: An Improved Motion Planner for Mobile Robot in Two‐Dimensional Space

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Abstract
Path planning for mobile robot aims to solve the problem of creating a collision‐free path from the start state to the goal state in the given space, which is a key supporting technology for unmanned work. In order to solve the problems of the asymptotic optimal rapidly extended random trees star (RRT*) algorithm, such as its slow convergence rate, the low efficiency of planning and the high cost of path, an improved motion planner (Fast‐RRT*) was proposed based on hybrid sampling strategy and choose parent based on backtracking. Firstly, the goal bias strategy and constraint sampling are combined in the sampling stage to reduce the blindness of sampling. Secondly, to obtain a path with lower cost than the RRT* algorithm, the ancestor of the nearest node is considered until the initial state in the process of choose parent for new node. To ensure the feasibility of the path, the path is smoothed by cubic B‐spline curve. The effectiveness of Fast‐RRT* algorithm was verified based on MATLAB and V‐rep platform. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Funding Information
  • Shandong Academy of Sciences (2017BSHZ008)
  • National Natural Science Foundation of China (61701270)

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