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12 December 2024 Research on dynamic path planning of UUV based on improved ant colony algorithm and dynamic collision avoidance strategy
Peng Geng, Rui Wu, Guanglong Zeng
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Proceedings Volume 13439, Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024); 134390O (2024) https://doi.org/10.1117/12.3055378
Event: Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), 2024, Xiamen, China
Abstract
Traditional ant colony algorithms often fall into local optima and deadlocks, leading to unsolvable or suboptimal solutions in the path planning of unmanned underwater vehicles (UUVs). Furthermore, they lack the ability to dynamically avoid collisions. To address these issues, this paper proposes a dynamic path planning method that combines global path planning using an improved ant colony algorithm with a local dynamic collision avoidance strategy. First, the traditional ant colony algorithm is optimized to prevent local optima and deadlocks by employing a roulette wheel selection method and enhancing memory-based backtracking strategies. Subsequently, a Kalman filter-based trajectory prediction method is introduced to handle irregularly moving obstacles, along with specific collision avoidance strategies for different types of collisions. The proposed method ultimately achieves dynamic path planning for UUVs. Simulation experiments demonstrate that the improved ant colony algorithm achieves a convergence speed approximately 16.7% faster than the traditional algorithm and can effectively perform path planning in dynamic environments, resulting in an optimal collision-free path.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Peng Geng, Rui Wu, and Guanglong Zeng "Research on dynamic path planning of UUV based on improved ant colony algorithm and dynamic collision avoidance strategy", Proc. SPIE 13439, Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), 134390O (12 December 2024); https://doi.org/10.1117/12.3055378
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KEYWORDS
Collision avoidance
Computer simulations
Algorithm development
Mathematical optimization
Signal filtering
Covariance
Detection and tracking algorithms
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