关键词: 航迹规划, 蜣螂优化算法, 新型混沌映射, 新型柯西-洛伦兹游走策略, 新型柯西逆累积分布函数游走策略

Abstract: To address the problem of search blind spots in UAV 3D trajectory planning to identify threats or no-fly areas and to improve the global trajectory planning capability, the traditional dung beetle intelligent optimization algorithm has good global search capability, but its performance is affected by the initialization parameter settings, which can lead to problems such as blind spots in the local search, and non-communication between populations, etc. For this reason, a multi-strategy improved dung beetle optimization algorithm is proposed. The initialization parameters, dung beetle ball-rolling behavior, small dung beetle foraging behavior and dung beetle stealing behavior are improved by using a novel chaotic mapping, a novel Cauchy-Lorenz wandering strategy, an improved triangular wandering strategy, and a novel Cauchy's inverse cumulative distribution function wandering strategy, respectively; the dung beetles of each population are crossed by using an improved longitudinal and transversal crossover strategy; and the ability of the UAV to identify threat areas and the global trajectory planning is enhanced by the improvement of the multi-strategy. The results show the superiority of the improved dung beetle algorithm in UAV trajectory planning; compared with the traditional dung beetle intelligent optimization algorithm, the total cost of the improved optimization algorithm is only 57.88% of the cost of the traditional algorithm, and the total cost is reduced by 35.59%; compared with the total cost of the sand cat swarm algorithm, the particle swarm algorithm, the hippopotamus algorithm, and the gray wolf algorithm the total cost is reduced by 38.37%, 38.80%, 44.17%, respectively, 41.80%.

Key words: trajectory planning, dung beetle optimization algorithm, novel chaotic mapping, novel Cauchy-Lorenz wandering strategy, novel cauchy inverse cumulative distribution function wandering strategy