林冠下森林環境におけるRLベースのクアッドローター制御による空中点検行動
Aerial Inspection Behaviors via RL-based Quadrotor Control for Under-canopy Forest Environments
深層強化学習を用いたクアッドローターの低レベル制御ポリシーを提案し、林冠下の森林での点検視点追跡とナビゲーションを実現する。上位のTSP/RRT*プランナーと組み合わせて長距離ミッションを安全に実行する。
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著者: Fausto Mauricio Lagos Suarez, Akshit Saradagi, Vidya Sumathy, Viswa Narayanan Sankaranarayanan, George Nikolakopoulos
分類: cs.RO, cs.AI, math.OC
原文アブストラクト
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.
