Open Access

ARTICLE

Path Planning of Multi-Axis Robotic Arm Based on Improved RRT*

Juanling Liang¹, Wenguang Luo^1,2,*, Yongxin Qin¹

1 School of Automation, Guangxi University of Science and Technology, Liuzhou, 545006, China
2 Key Laboratory of AI and Information Processing of Education Department of Guangxi, Hechi University, Hechi, 546300, China

* Corresponding Author: Wenguang Luo. Email:

(This article belongs to the Special Issue: Intelligent Manufacturing, Robotics and Control Engineering)

Computers, Materials & Continua 2024, 81(1), 1009-1027. https://doi.org/10.32604/cmc.2024.055883

Received 09 July 2024; Accepted 02 September 2024; Issue published 15 October 2024

Abstract

An improved RRT* algorithm, referred to as the AGP-RRT* algorithm, is proposed to address the problems of poor directionality, long generated paths, and slow convergence speed in multi-axis robotic arm path planning. First, an adaptive biased probabilistic sampling strategy is adopted to dynamically adjust the target deviation threshold and optimize the selection of random sampling points and the direction of generating new nodes in order to reduce the search space and improve the search efficiency. Second, a gravitationally adjustable step size strategy is used to guide the search process and dynamically adjust the step-size to accelerate the search speed of the algorithm. Finally, the planning path is processed by pruning, removing redundant points and path smoothing fitting using cubic B-spline curves to improve the flexibility of the robotic arm. Through the six-axis robotic arm path planning simulation experiments on the MATLAB platform, the results show that the AGP-RRT* algorithm reduces 87.34% in terms of the average running time and 40.39% in terms of the average path cost; Meanwhile, under two sets of complex environments A and B, the average running time of the AGP-RRT* algorithm is shortened by 94.56% vs. 95.37%, and the average path cost is reduced by 55.28% vs. 47.82%, which proves the effectiveness of the AGP-RRT* algorithm in improving the efficiency of multi-axis robotic arm path planning.

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Keywords

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