Title: Optimising path planning and obstacle avoidance algorithms for electrical robots using multimodal information learning techniques

Authors: Yang Qiu; Bo Zhou; Lingxiao Chen

Addresses: ZheJiang T.S. Power Transmission and Transformation Co., Ltd., Wenzhou 325000, Zhejiang, China ' ZheJiang T.S. Power Transmission and Transformation Co., Ltd., Wenzhou 325000, Zhejiang, China ' ZheJiang T.S. Power Transmission and Transformation Co., Ltd., Wenzhou 325000, Zhejiang, China

Abstract: In response to the problems of poor adaptability to complex environments, low success rate of obstacle avoidance, and low accuracy of path planning in traditional path planning and obstacle avoidance algorithms, this paper uses multimodal information learning technology to optimise the path planning and obstacle avoidance algorithms of electric robots. Compared with traditional obstacle avoidance algorithms, optimising obstacle avoidance algorithms using deep learning techniques in multimodal information learning and constructing obstacle avoidance algorithms based on vision and dynamic programming can effectively improve the success rate of obstacle avoidance for electric robots. The average pathfinding time of the six groups studied in this article is 58.59 seconds, which is 4.94 seconds and 3.21 seconds lower than the average values of the ant algorithm and A * algorithm, respectively; in a dynamic obstacle environment, the obstacle avoidance success rate of the algorithm studied in this paper is 96.67%.

Keywords: obstacle avoidance algorithm; path planning; multimodal information learning technology; electrified robot; Q-learning algorithm; ant colony optimisation; ACO.

DOI: 10.1504/IJIIDS.2025.147413

International Journal of Intelligent Information and Database Systems, 2025 Vol.17 No.3/4, pp.320 - 339

Received: 04 Mar 2024
Accepted: 20 Jun 2024
Published online: 15 Jul 2025 *

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