Title: An adaptive environment informed rapidly-exploring random tree for citrus picking manipulator path planning

Authors: Yu Tang; Wei Xiao; Zhiping Tan; Jiajun Zhuang; Weilin Chen; Jingya Yi; Huangsheng Huang; Mingwei Fang

Addresses: Academy of Interdisciplinary Studies, Guangdong Polytechnic Normal University, Guangzhou 510665, China ' Academy of Interdisciplinary Studies, Guangdong Polytechnic Normal University, Guangzhou 510665, China ' Academy of Interdisciplinary Studies, Guangdong Polytechnic Normal University, Guangzhou 510665, China ' Academy of Contemporary Agriculture Engineering Innovations, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China ' School of Mechatronics Engineering and Automation, Foshan University, Foshan 528000, China ' Engineering Research Center for Intelligent Robotics, Jihua Laboratory, Foshan 528200, China ' Academy of Interdisciplinary Studies, Guangdong Polytechnic Normal University, Guangzhou 510665, China ' Academy of Interdisciplinary Studies, Guangdong Polytechnic Normal University, Guangzhou 510665, China

Abstract: In unstructured environments, citrus-picking manipulators frequently encounter various obstacles, such as leaves and branches, during motion, leading to high costs and low efficiency in path planning. To address this problem, an adaptive environment-informed rapidly-exploring random tree (AEI-RRT) path planning algorithm is proposed. First, an adaptive target bias strategy is incorporated into the informed rapidly-exploring random tree (RRT) algorithm to improve the node search's ability to adapt to the environment. Second, a hybrid sampling space is employed to reduce node randomness and avoid falling into local optima. Third, local thinking is applied to adjust the dynamic step size, reduce path cost, and improve search efficiency. Finally, the AEI-RRT algorithm is compared with three well-known RRT variant algorithms. The experimental results demonstrated that the proposed AEI-RRT algorithm offers significant advantages in terms of search time efficiency, search nodes, and path length. Furthermore, the proposed AEI-RRT algorithm is integrated into the joint space. The results indicate that the algorithm effectively plans an optimal path.

Keywords: path planning; picking manipulator; informed RRT; adaptive environment strategy.

DOI: 10.1504/IJBIC.2025.145531

International Journal of Bio-Inspired Computation, 2025 Vol.25 No.2, pp.113 - 123

Received: 22 Sep 2024
Accepted: 22 Nov 2024
Published online: 02 Apr 2025 *

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