面向山地环境的无人机区域侦察覆盖路径规划研究

Research on Terrain-Constrained UAV Area Reconnaissance Coverage Path Planning

摘要:

无人机在复杂山地环境中进行区域侦察时,因山体遮挡及地形起伏等特性,常伴有覆盖质量差和效率低的问题；为此,提出了一种高效的区域覆盖路径规划方法;从对山地环境建模出发,构建三维覆盖与遮挡模型,引入基于贪婪策略的视点生成方法,用以筛选最优视点集；提出自适应混沌蜣螂优化算法(ACDBO)求取路径点次序,在蜣螂优化算法中引入混沌映射初始化种群策略、非线性边界收敛因子、参数自适应调整策略,提高算法的全局寻优能力和收敛速度,优化目标考虑了路径长度与偏转角等；仿真实验结果表明,该方法得到的视点数目及折线路径长度优于传统方法,且ACDBO算法较原始算法及传统算法性能更优,规划的航迹质量更好,能够高效的完成侦察覆盖任务。

Abstract:

In the realm of unmanned aerial vehicle (UAV)-enabled area reconnaissance within complex mountainous terrain, challenges such as poor coverage quality and low efficiency often accompany characteristics such as terrain occlusion and undulating topography. Consequently, an efficient region coverage path planning methodology is proposed. Commencing with the modeling of mountainous environments, a three-dimensional coverage and occlusion model is constructed. A greedy strategy-based viewpoint generation approach is introduced to optimize the selection of the most suitable viewpoints. an Adaptive Chaotic Dung Beetle Optimizer (ACDBO) algorithm is proposed to ascertain the sequence of path points. Within the ACDBO algorithm, strategies including chaotic mapping for population initialization, a nonlinear boundary convergence factor, and parameter adaptive adjustment are integrated to enhance the algorithm"s global optimization capability and convergence speed. Optimization objectives incorporate considerations such as path length and deviation angle. Simulation experiments demonstrate that this methodology yields superior numbers of viewpoints and polyline path lengths compared to conventional approaches. Moreover, the ACDBO algorithm surpasses both the original algorithm and traditional methodologies, exhibiting enhanced performance and superior trajectory quality, thus facilitating the efficient completion of reconnaissance coverage tasks.