電力制約が変動する環境下での深層強化学習によるUAV被覆経路計画
UAV Coverage Path Planning under Varying Power Constraints using Deep Reinforcement Learning
カメラ搭載UAVの被覆経路計画を、地図状入力を用いたDDQNで学習し、飛行可能時間の変動と禁飛行区域を考慮しつつ電力と被覆の両立を図る手法を提案した。
著者: Mirco Theile, Harald Bayerlein, Richard Nai, David Gesbert, Marco Caccamo
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Coverage path planning (CPP) is the task of designing a trajectory that enables a mobile agent to travel over every point of an area of interest. We propose a new method to control an unmanned aerial vehicle (UAV) carrying a camera on a CPP mission with random start positions and multiple options for landing positions in an environment containing no-fly zones. While numerous approaches have been proposed to solve similar CPP problems, we leverage end-to-end reinforcement learning (RL) to learn a control policy that generalizes over varying power constraints for the UAV. Despite recent improvements in battery technology, the maximum flying range of small UAVs is still a severe constraint, which is exacerbated by variations in the UAV's power consumption that are hard to predict. By using map-like input channels to feed spatial information through convolutional network layers to the agent, we are able to train a double deep Q-network (DDQN) to make control decisions for the UAV, balancing limited power budget and coverage goal. The proposed method can be applied to a wide variety of environments and harmonizes complex goal structures with system constraints.
