OptiRoute: A Heuristic-assisted Deep Reinforcement Learning Framework for UAV-UGV Collaborative Route Planning

TL;DR

This paper introduces OptiRoute, a heuristics-assisted deep reinforcement learning framework that optimizes UAV-UGV cooperative routes, significantly improving mission efficiency over traditional genetic algorithm approaches.

Contribution

The paper presents a novel heuristics-assisted RL framework for UAV-UGV routing, combining reinforcement learning with constraint programming for enhanced route optimization.

Findings

RL reduces mission time compared to GA

RL minimizes UAV-UGV idle time

RL decreases energy consumption for UAV and UGV

Abstract

Unmanned aerial vehicles (UAVs) are capable of surveying expansive areas, but their operational range is constrained by limited battery capacity. The deployment of mobile recharging stations using unmanned ground vehicles (UGVs) significantly extends the endurance and effectiveness of UAVs. However, optimizing the routes of both UAVs and UGVs, known as the UAV-UGV cooperative routing problem, poses substantial challenges, particularly with respect to the selection of recharging locations. Here in this paper, we leverage reinforcement learning (RL) for the purpose of identifying optimal recharging locations while employing constraint programming to determine cooperative routes for the UAV and UGV. Our proposed framework is then benchmarked against a baseline solution that employs Genetic Algorithms (GA) to select rendezvous points. Our findings reveal that RL surpasses GA in terms of reducing overall mission time, minimizing UAV-UGV idle time, and mitigating energy consumption for both the UAV and UGV. These results underscore the efficacy of incorporating heuristics to assist RL, a method we refer to as heuristics-assisted RL, in generating high-quality solutions for intricate routing problems.