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Unmanned aerial vehicles (UAVs) are capable of surveying expansive areas, buttheir operational range is constrained by limited battery capacity. Thedeployment 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 cooperativerouting problem, poses substantial challenges, particularly with respect to theselection of recharging locations. Here in this paper, we leveragereinforcement learning (RL) for the purpose of identifying optimal recharginglocations while employing constraint programming to determine cooperativeroutes for the UAV and UGV. Our proposed framework is then benchmarked againsta baseline solution that employs Genetic Algorithms (GA) to select rendezvouspoints. Our findings reveal that RL surpasses GA in terms of reducing overallmission time, minimizing UAV-UGV idle time, and mitigating energy consumptionfor both the UAV and UGV. These results underscore the efficacy ofincorporating heuristics to assist RL, a method we refer to asheuristics-assisted RL, in generating high-quality solutions for intricaterouting problems.

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