Motion Planning of the Citrus-Picking Manipulator Based on the TO-RRT Algorithm

Cheng Liu, Qingchun Feng, Zuoliang Tang, Xiangyu Wang, Jinping Geng and Lijia Xu
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Cheng Liu: College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625014, China
Qingchun Feng: Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Zuoliang Tang: College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625014, China
Xiangyu Wang: Institute of System Science and Technology, School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China
Jinping Geng: College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625014, China
Lijia Xu: College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625014, China

Agriculture, 2022, vol. 12, issue 5, 1-23

Abstract: The working environment of a picking robot is complex, and the motion-planning algorithm of the picking manipulator will directly affect the obstacle avoidance effect and picking efficiency of the manipulator. In this study, a time-optimal rapidly-exploring random tree (TO-RRT) algorithm is proposed. First, this algorithm controls the target offset probability of the random tree through the potential field and introduces a node-first search strategy to make the random tree quickly escape from the repulsive potential field. Second, an attractive step size and a “step-size dichotomy” are proposed to improve the directional search ability of the random tree outside the repulsive potential field and solve the problem of an excessively large step size in extreme cases. Finally, a regression superposition algorithm is used to enhance the ability of the random tree to explore unknown space in the repulsive potential field. In this paper, independent experiments were carried out in MATLAB, MoveIt!, and real environments. The path-planning speed was increased by 99.73 % , the path length was decreased by 17.88 % , and the number of collision detections was reduced by 99.08 % . The TO-RRT algorithm can be used to provide key technical support for the subsequent design of picking robots.

Keywords: picking manipulator; motion planning; TO-RRT; step-size dichotomy; regression superposition (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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