Deep Reinforcement Learning-Based Approach for a Single Vehicle Persistent Surveillance Problem with Fuel Constraints
This addresses a domain-specific problem for unmanned aerial vehicle operations, with incremental improvements over existing heuristics.
The paper tackled the problem of optimizing a single unmanned aerial vehicle's persistent surveillance mission with fuel constraints to minimize the maximum time between target visits, presenting a deep reinforcement learning algorithm that showed effectiveness in numerical experiments compared to greedy heuristics.
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.