ORIGINAL RESEARCH article
Front. Neurorobot.
Volume 19 - 2025 | doi: 10.3389/fnbot.2025.1574044
This article is part of the Research TopicAdvancements in Neural Learning Control for Enhanced Multi-Robot CoordinationView all 4 articles
Path planning of industrial robots based on the Adaptive field cooperative sampling algorithm
Provisionally accepted
- Qingdao University of Science and Technology, Qingdao, China
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For the low efficiency and poor generalization ability of path planning algorithm of industrial robots, this work proposes an adaptive field co-sampling algorithm (AFCS). This algorithm deeply integrates the traditional rapidly random search tree algorithm (RRT) with the improved artificial potential field algorithm (APF). Firstly, the environment complexity function is proposed to make full use of environment information and improve its generalization ability of the traditional RRT algorithm. Then an optimal sampling strategy is proposed to make the improvement of the efficiency and optimal direction of RRT algorithm. Finally, this article designs a collaborative extension strategy, which introduces the improved APF into the traditional RRT algorithm to determine the new nodes, so as to so as to improve the orientation and expansion efficiency of the algorithm. The proposed AFCS algorithm completes simulation experiments in two environments with different complexity. Compared with the traditional RRT, RRT* and tRRT algorithm, the results show that the AFCS algorithm has achieved great improvement in environmental adaptability, stability and efficiency. At last, ROKAE industrial robot is taken as the object to build a simulation environment for the path planning, which further verifies the practicability of the algorithm.
Keywords: Industrial robot, path planning, RRT, APF, AFCs
Received: 10 Feb 2025; Accepted: 25 Aug 2025.
Copyright: © 2025 Dang, Bo, Sha, Ming, Juan, tao, Yuan and Wei. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Li Qing Dang, Qingdao University of Science and Technology, Qingdao, China
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