Solving Vehicle Routing Problem for Unmanned Heterogeneous Vehicle Systems using Asynchronous Multi-Agent Architecture (A-teams)

Subramanian, R.; Mondal, Md Safwan; Reddinger, Jean-Paul; Dotterweich, James; Humann, James; Childers, Marshal A.; Bhounsule, Pranav A.

doi:10.1109/icuas57906.2023.10156585

Cited by 4 publications

(1 citation statement)

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“…In our prior research [22], [23], we introduced a two-tiered optimization structure that streamlined the collaborative routing challenge. This framework determined the UGV route in the primary layer and the UAV route in the secondary layer using heuristic methods.…”

Section: A Related Workmentioning

confidence: 99%

Deep Reinforcement Learning for Green UAV-Assisted Data Collection

Mondal

Mishra

Prasad

et al. 2023

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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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.

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Section: A Related Workmentioning

confidence: 99%