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Finding a High-Quality Initial Solution for the RRTs Algorithms in 2D Environments

Published online by Cambridge University Press:  21 February 2019

Jiankun Wang Open the ORCID record for Jiankun Wang [Opens in a new window] ,
Wenzheng Chi ,
Mingjie Shao  and
Max Q.-H. Meng
Jiankun Wang
Affiliation:
Department of Electronic Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China. E-mails: jkwang@ee.cuhk.edu.hk, mjshao@ee.cuhk.edu.hk
Wenzheng Chi
Affiliation:
Robotics and Microsystems Center, School of Mechanical and Electric Engineering, Soochow University, Suzhou, China. E-mail: wzchi@suda.edu.cn
Mingjie Shao
Affiliation:
Department of Electronic Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China. E-mails: jkwang@ee.cuhk.edu.hk, mjshao@ee.cuhk.edu.hk
Max Q.-H. Meng*
Affiliation:
Department of Electronic Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China. E-mails: jkwang@ee.cuhk.edu.hk, mjshao@ee.cuhk.edu.hk
*
*Corresponding author. E-mail: max.meng@cuhk.edu.hk
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Summary

In this paper, we propose a bioinspired path planning algorithm for finding a high-quality initial solution based on the pipeline of the Rapidly exploring Random Tree (RRT) method by modifying the sampling process. The modification mainly includes controlling the sampling space and using the probabilistic sampling with the two-dimensional Gaussian mixture model. Inspired by the tropism of plants, we use a Gaussian mixture model to imitate the tree’s growth in nature. In a 2D environment, we can get an approximate moving point’s probabilistic distribution, and the initial path can be found much quickly guided by the probabilistic heuristic. At the same time, only a small number of nodes are generated, which can reduce the memory usage. As a meta-algorithm, it can be applicable to other RRT methods and the performance of underlying algorithm is improved dramatically. We also prove that the probabilistic completeness and the asymptotic optimality depend on the original algorithm (other RRTs). We demonstrate the application of our algorithm in different simulated 2D environments. On these scenarios, our algorithm outperforms the RRT and the RRT* methods on finding the initial solution. When embedded into post-processing algorithms like the Informed RRT*, it also promotes the convergence speed and saves the memory usage.

Keywords

Path planningMotion planningRoboticsMobile robot

Information

Type
Articles
Information
Robotica , Volume 37 , Issue 10 , October 2019 , pp. 1677 - 1694
Copyright
© Cambridge University Press 2019 

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