UAV path planning based on improved ant colony algorithm

Guangxing Li; Yuan Li

doi:10.1117/12.2678893

8 May 2023 UAV path planning based on improved ant colony algorithm

Guangxing Li, Yuan Li

Proceedings Volume 12635, Second International Conference on Algorithms, Microchips, and Network Applications (AMNA 2023); 126350B (2023) https://doi.org/10.1117/12.2678893
Event: International Conference on Algorithms, Microchips, and Network Applications 2023, 2023, Zhengzhou, China

Abstract

The traditional ant colony algorithm is inefficient to search, easy to fall into algorithm stagnation and local optimization problems in UAV path planning. To ensure that the UAV can avoid obstacles and fly safely, the ant colony algorithm is improved and optimized. First, the target planning area of the UAV is modeled in three dimensions using raster method. Secondly, the update rules of pheromones are improved, and the weight factors of pheromones and heuristics are adjusted. An unmanned aerial path planning algorithm based on improved ant colony algorithm is proposed to plan a safe and optimal path for the unmanned aerial vehicle. Finally, the simulation results show that the improved algorithm has a better flight path than the traditional algorithm.

Citation Download Citation

Guangxing Li and Yuan Li "UAV path planning based on improved ant colony algorithm", Proc. SPIE 12635, Second International Conference on Algorithms, Microchips, and Network Applications (AMNA 2023), 126350B (8 May 2023); https://doi.org/10.1117/12.2678893

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