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Volume 9 Issue 2
Feb.  2022

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2022  >  9(2): 283-294
B. H. Li and B. D. Chen, “An adaptive rapidly-exploring random tree,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 2, pp. 283–294, Feb. 2022. doi: 10.1109/JAS.2021.1004252
Citation: B. H. Li and B. D. Chen, “An adaptive rapidly-exploring random tree,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 2, pp. 283–294, Feb. 2022. doi: 10.1109/JAS.2021.1004252
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An Adaptive Rapidly-Exploring Random Tree

doi: 10.1109/JAS.2021.1004252

  • Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an 710049, China

Funds:  This work was supported in part by the National Science Foundation of China (61976175, 91648208) and the Key Project of Natural Science Basic Research Plan in Shaanxi Province of China (2019JZ-05)
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    Abstract
  • Sampling-based planning algorithms play an important role in high degree-of-freedom motion planning (MP) problems, in which rapidly-exploring random tree (RRT) and the faster bidirectional RRT (named RRT-Connect) algorithms have achieved good results in many planning tasks. However, sampling-based methods have the inherent defect of having difficultly in solving planning problems with narrow passages. Therefore, several algorithms have been proposed to overcome these drawbacks. As one of the improved algorithms, Rapidly-exploring random vines (RRV) can achieve better results, but it may perform worse in cluttered environments and has a certain environmental selectivity. In this paper, we present a new improved planning method based on RRT-Connect and RRV, named adaptive RRT-Connect (ARRT-Connect), which deals well with the narrow passage environments while retaining the ability of RRT algorithms to plan paths in other environments. The proposed planner is shown to be adaptable to a variety of environments and can accomplish path planning in a short time.

     

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    Highlights

    • Fast RRT algorithm for narrow passage: This paper proposes an improved RRT algorithm which can better deal with the planning task with narrow passage environment. The algorithm improves the planning speed and success rate of RRT algorithm for narrow passage task
    • Rapid planning of common environment: Although the new algorithm increases the amount of computation because of dealing with narrow channel environment, it can still deal with common planning tasks quickly without great performance loss
    • Simple algorithm flow: The new algorithm improves the sampling process of the original RRT algorithm, simplifies the local environment judgment process of RRV algorithm, and uses dual tree search to enhance the stability of the algorithm. The whole process of the algorithm is simple and unified, easy to understand and implement

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