通过基于强化学习的四旋翼控制实现空中巡检行为：在树冠下森林环境中的应用
Aerial Inspection Behaviors via RL-based Quadrotor Control for Under-canopy Forest Environments
AI总结：
本文提出了一种基于深度强化学习的四旋翼控制器，用于在树冠下森林环境中进行自主巡检任务，通过端到端控制策略实现巡检视角姿态跟踪，并结合旅行商问题规划器和快速随机树星规划器确保长距离任务的安全可靠部署。
摘要：
本文针对在树冠下森林环境中使用基于深度强化学习（RL）的低级四旋翼控制器进行空中巡检任务的问题进行了研究。具体而言，本文提出了一种端到端（将状态映射到RPMs）的四旋翼控制策略，实现了巡检视角姿态跟踪（同时位置和偏航参考跟踪），这对于各种目标巡检行为和森林中的点对点导航至关重要。为确保在长距离任务中端到端RL控制器的安全可靠部署，本文利用了一个包含旅行商问题规划器（TSP）和快速随机树星规划器（RRT*）的更高导航指导层。在已知的森林地图和一组用户指定的巡检区域上，TSP规划器找到最优访问序列。在两个目标区域之间，RRT*规划器生成符合下层端到端RL策略跟踪限制的碰撞自由路径。通过五个目标巡检场景，本文证明了基于强化学习的电机级稳定控制器，结合导航指导层，可以有效用作树冠下森林巡检任务的低级巡检执行模块。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
