用强化学习控制无人机，在林下环境实现精准巡检路径跟踪。
Aerial Inspection Behaviors via RL-based Quadrotor Control for Under-canopy Forest Environments
- 端到端强化学习控制，直接输出电机转速以跟踪位置和航向。
- 结合旅行商与RRT*规划，生成安全高效的巡检路径序列。
- 适合复杂森林环境的自主巡检任务，尤其适用于林下空间约束场景。
本文针对林下森林环境中无人机自主巡检任务，提出一种基于深度强化学习（RL）的低层四旋翼控制器，集成于完整的自主导航系统中。该方法构建了一个端到端控制策略（从状态映射至电机转速），可同时实现位置与偏航角参考轨迹的精确跟踪，对目标巡检行为及点到点导航至关重要。为保障长距离任务中端到端强化学习控制器的安全可靠部署，系统采用上层导航引导层，包含旅行商问题（TSP）规划器和快速探索随机树星（RRT*）规划器：在已知森林地图和用户指定的巡检区域基础上，TSP规划器确定最优访问顺序；在相邻目标区域间，由RRT*生成符合底层强化学习控制策略跟踪能力的无碰撞路径。通过五个典型巡检场景验证，基于强化学习的电机级稳定控制器配合导航引导层，可有效作为林下巡检任务的低层执行模块。
原文摘要 · Abstract (English)
This paper addresses the problem of using a deep Reinforcement Learning (RL)-based low-level Quadrotor controller within an autonomous Quadrotor navigation stack for aerial inspection missions in under-canopy forest environments. Specifically, the article presents an end-to-end (mapping states to RPMs) Quadrotor control policy that achieves inspection view-pose tracking (simultaneous position and yaw reference tracking), which is crucial for various target inspection behaviors and point-to-point navigation in forests. To ensure safe and reliable deployment of the end-to-end RL controller in long-range missions, this article utilizes a higher navigation guidance layer comprising of a Traveling Salesman Problem planner (TSP) and a Rapidly-exploring Random Tree Star (RRT*) planner. Over a known map of a forest and a set of user-specified inspection regions, the TSP planner finds the optimal visitation sequence. Between two target regions, collision-free paths that respect the tracking limitations of the lower end-to-end RL policy are generated by an RRT* planner. Through five target inspection scenarios, this article demonstrates that an RL-based motor-level stabilizing controller, supported by a navigation guidance layer, can be used effectively as the low-level inspection execution module for under-canopy forest inspection missions.
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