期刊
FLUCTUATION AND NOISE LETTERS
卷 20, 期 1, 页码 -出版社
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0219477521500024
关键词
Ant colony optimization; cooperative path planning; Cauchy mutant operator and unmanned aerial vehicle
资金
- China National Nature Science Foundation [61374165]
This paper addresses the cooperative path planning of multi-UAVs in a dynamic environment by combining various optimization algorithms to ensure the shortest route is chosen while avoiding collision. The proposed method is more effective and efficient compared to classic approaches.
In a dynamic environment with wind forces and tornadoes, eliminating fluctuations and noise is critical to get the optimal results. Avoiding collision and simultaneous arrival of multiple unmanned aerial vehicles (multi-UAVs) is also a great problem. This paper addresses the cooperative path planning of multi-UAVs with in a dynamic environment. To deal with the aforementioned issues, we combine the maximum-minimum ant colony optimization (MMACO) and Cauchy Mutant (CM) operators to make a bio-inspired optimization algorithm. Our proposed algorithm eliminates the limitations of classical ant colony optimization (ACO) and MMACO, which has the issues of the slow convergence speed and a chance of falling into local optimum. This paper chooses the CM operator to enhance the MMACO algorithm by comparing and examining the varying tendency of fitness function of the local optimum position and the global optimum position when taking care of multi-UAVs path planning problems. It also makes sure that the algorithm picks the shortest route possible while avoiding collision. Additionally, the proposed method is more effective and efficient when compared to the classic MMACO. Finally, the simulation experiment results are performed under the dynamic environment containing wind forces and tornadoes.
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