Double-layer path planning based on evolutionary ant colony algorithm and conflict prediction resolution

YU Kaiying; XU Bin

doi:10.12299/jsues.24-0087

Volume 39 Issue 2

Jun. 2025

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YU Kaiying, XU Bin. Double-layer path planning based on evolutionary ant colony algorithm and conflict prediction resolution[J]. Journal of Shanghai University of Engineering Science, 2025, 39(2): 236-242. doi: 10.12299/jsues.24-0087

Citation:

YU Kaiying, XU Bin. Double-layer path planning based on evolutionary ant colony algorithm and conflict prediction resolution[J]. Journal of Shanghai University of Engineering Science, 2025, 39(2): 236-242. doi: 10.12299/jsues.24-0087

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Double-layer path planning based on evolutionary ant colony algorithm and conflict prediction resolution

doi: 10.12299/jsues.24-0087

YU Kaiying,
XU Bin^,

School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

Received Date: 2024-03-25
Available Online: 2025-09-30
Publish Date: 2025-06-30

Abstract

Abstract

Aiming at the problems of low search efficiency and difficulty in resolving path conflicts when the ant colony algorithm was used to the cooperative path planning problem of multiple mobile robots, a dual-layer cooperative path planning method was proposed. In the static layer, the crossover mechanism of genetic algorithm was integrated into ant colony algorithm, and the evolutionary path crossover strategy was established to improve the quality of planning path.Concurrently, the calculation method of pheromone increment differentiation was adopted to accelerate the convergence speed of ant colony algorithm. In the dynamic layer, collision prediction was conducted based on a three-dimensional space-time map, and a priority conflict-free strategy was employed to effectively resolve path conflict. Simulation results show that the proposed dual-layer dynamic cooperative path planning method can improve the comprehensive performance of the algorithm and solve the cooperative path planning problem, which verifying the feasibility and effectiveness of the proposed algorithm.
- multi-robot,
- cooperative path planning,
- ant colony algorithm,
- evolutionary thought,
- conflict resolution

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References(12)

References

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