UAV path planning based on improved bat algorithm

DOI:	10.11809/bqzbgcxb2023.09.004
Keywords:	UAV path planning; particle swarm algorithm; bat algorithm; optimal success rate strategy; inertia weights
Abstract:	In order to solve the problems that the accuracy of the existing unmanned aerial vehicle (UAV) path programming solution and the speed of the solution are difficult to balance, a UAV path planning algorithm based on improved bat algorithm (OS PSOBA) is proposed. Firstly, the individual optimal factors in the particle swarm algorithm (PSO) are introduced into the global random flight search of the bat algorithm (BA), which broadens the scope of the search and increases the divergence of the BA path search. Secondly, in the BA local path search stage, the model of the fusion of Gaussian distribution and Cauchy distribution is used to constrain local search and new solution generation. In addition, the BAPSO algorithm is proposed, which improves the solution accuracy and solution efficiency of path search. Finally, the dynamic adjustment of the inertial weight of the optimal success rate strategy is introduced into PSOBA, and the improved bat UAV path planning algorithm (OS PSOBA) is proposed, which aims to dynamically adjust the proportion of search factors and further improve the solution accuracy and efficiency of the algorithm. Combined with the actual environment, a simulated flight environment model is built, and OS PSOBA is compared with PSO and BA. Simulation experiments show that OS PSOBA demonstrates the superiority of the algorithm compared to PSO and BA algorithms to accomplish the UAV path planning task quickly and efficiently.
Published:	2023-09-28
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