Robot motion planning and trajectory optimization algorithm based on graph neural network

Wenhao Gu; Junkai Wu; Lvping Chen

doi:10.1117/12.3037587

27 September 2024 Robot motion planning and trajectory optimization algorithm based on graph neural network

Wenhao Gu, Junkai Wu, Lvping Chen

Proceedings Volume 13275, Sixth International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024); 1327538 (2024) https://doi.org/10.1117/12.3037587
Event: 6th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024), 2024, Wuhan, China

Abstract

The fast development of robot technology has highlighted efficient algorithms in robot motion planning and trajectory optimization. This paper brings forward an algorithm for planning and trajectory optimization of robot motion based on a graph neural network model, with the aim of solving the problem of planning a path in a complex environment. By studying the application of graph neural networks in environment modeling, obstacle representation, and path planning, we develop a method that can effectively handle dynamic obstacles and complex terrain. Experimental results show that our method outperforms traditional A* and RRT algorithms in path length, calculation time, and success rate. We also explore the practical application potential of the algorithm and propose future research directions, including optimizing the model structure, verifying the algorithm's real-world applicability, and improving computational efficiency.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wenhao Gu, Junkai Wu, and Lvping Chen "Robot motion planning and trajectory optimization algorithm based on graph neural network", Proc. SPIE 13275, Sixth International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024), 1327538 (27 September 2024); https://doi.org/10.1117/12.3037587

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