无人机在复杂环境中的平滑覆盖路径规划
Optimal Smooth Coverage Trajectory Planning for Quadrotors in Cluttered Environment
- 先用遗传算法解兴趣点访问顺序，再优化路径平滑性
- 生成满足避障、时间短、轨迹光滑的最优路径
- 适合电力巡检等需精准覆盖的无人机任务
针对无人机在电力线路场景中的典型应用，本文将问题建模为复杂环境中对兴趣点（POIs）的覆盖路径规划。提出一种分两阶段的最优平滑覆盖路径规划算法：前端采用遗传算法（GA）求解旅行商问题（TSP），生成优化的访问点序列；后端则综合考虑轨迹平滑性、耗时与避障约束，将其建模为非线性最小二乘问题进行求解，最终生成满足约束的平滑覆盖轨迹。数值仿真验证了该算法的有效性，确保无人机可在复杂环境中平稳完成所有兴趣点的覆盖任务。
原文摘要 · Abstract (English)
For typical applications of UAVs in power grid scenarios, we construct the problem as planning UAV trajectories for coverage in cluttered environments. In this paper, we propose an optimal smooth coverage trajectory planning algorithm. The algorithm consists of two stages. In the front-end, a Genetic Algorithm (GA) is employed to solve the Traveling Salesman Problem (TSP) for Points of Interest (POIs), generating an initial sequence of optimized visiting points. In the back-end, the sequence is further optimized by considering trajectory smoothness, time consumption, and obstacle avoidance. This is formulated as a nonlinear least squares problem and solved to produce a smooth coverage trajectory that satisfies these constraints. Numerical simulations validate the effectiveness of the proposed algorithm, ensuring UAVs can smoothly cover all POIs in cluttered environments.
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