基于 ε-level 蝙蝠算法的无人机三维航迹规划

To address the problem of complex terrain environment and various threats and constraints, this article proposes a path planning algorithm for UAV based on ε-level improved bat algorithm. First, according to the drone target function and constraints, a three-dimensional path planning model of the UAV is established. Second, in response to the precocious phenomenon in handling the high-dimensional constraints problem of the bat algorithm, the adaptive weight coefficient and iteration threshold are designed to balance the exploration and exploitation capabilities of bat algorithms. Furthermore, by integrating an ε-level comparative strategy, the algorithm's capability to handle issues of non-convex and non-linear constraints is enhanced. Additionally, a three-dimensional Dubins curve with variable turning radius is designed to smooth the path and solve the problem of penetrating the terrain of the two trails. Through simulation experiments and compared with BA, PSO, ε-PSO and ε-DE, the algorithm proposed in this paper shows superior performance in terms of exploitation ability, stability and success rate.