Cooperative Multi-Agent Planning Framework for Fuel Constrained UAV-UGV Routing Problem

TL;DR

This paper presents a multi-agent planning framework for UAV-UGV routing that enhances UAV endurance through refueling, employing heuristic algorithms to efficiently solve complex area surveillance problems under fuel and speed constraints.

Contribution

It introduces a sequential planning approach combining set cover, task allocation, and energy-constrained routing to optimize UAV-UGV cooperation for large-scale surveillance tasks.

Findings

Effective multi-agent strategy demonstrated on 30 scenarios

Heuristic methods enable rapid high-quality solutions

Framework improves UAV operational endurance significantly

Abstract

Unmanned Aerial Vehicles (UAVs), although adept at aerial surveillance, are often constrained by limited battery capacity. By refueling on slow-moving Unmanned Ground Vehicles (UGVs), their operational endurance can be significantly enhanced. This paper explores the computationally complex problem of cooperative UAV-UGV routing for vast area surveillance within the speed and fuel constraints, presenting a sequential multi-agent planning framework for achieving feasible and optimally satisfactory solutions. By considering the UAV fuel limits and utilizing a minimum set cover algorithm, we determine UGV refueling stops, which in turn facilitate UGV route planning at the first step and through a task allocation technique and energy constrained vehicle routing problem modeling with time windows (E-VRPTW) we achieve the UAV route at the second step of the framework. The effectiveness of our multi-agent strategy is demonstrated through the implementation on 30 different task scenarios across 3 different scales. This work offers significant insight into the collaborative advantages of UAV-UGV systems and introduces heuristic approaches to bypass computational challenges and swiftly reach high-quality solutions.