燃料制約下での単一機による持続的監視問題への深層強化学習アプローチ
Deep Reinforcement Learning-Based Approach for a Single Vehicle Persistent Surveillance Problem with Fuel Constraints
燃料制約のある単一の無人航空機が、補給所で燃料補給を繰り返しながら複数の目標を巡回する持続的監視問題に対し、深層強化学習で訪問順序を最適化する手法を提案し、貪欲法より有効であることを示した。
著者: Manav Mishra, Hritik Bana, Saswata Sarkar, Sujeevraja Sanjeevi, PB Sujit, Kaarthik Sundar
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
This article presents a deep reinforcement learning-based approach to tackle a persistent surveillance mission requiring a single unmanned aerial vehicle initially stationed at a depot with fuel or time-of-flight constraints to repeatedly visit a set of targets with equal priority. Owing to the vehicle's fuel or time-of-flight constraints, the vehicle must be regularly refueled, or its battery must be recharged at the depot. The objective of the problem is to determine an optimal sequence of visits to the targets that minimizes the maximum time elapsed between successive visits to any target while ensuring that the vehicle never runs out of fuel or charge. We present a deep reinforcement learning algorithm to solve this problem and present the results of numerical experiments that corroborate the effectiveness of this approach in comparison with common-sense greedy heuristics.
