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Fast moving but power hungry unmanned aerial vehicles (UAVs) can recharge onslow-moving unmanned ground vehicles (UGVs) to survey large areas in aneffective and efficient manner. In order to solve this computationallychallenging problem in a reasonable time, we created a two-level optimizationheuristics. At the outer level, the UGV route is parameterized by few freeparameters and at the inner level, the UAV route is solved by formulating andsolving a vehicle routing problem with capacity constraints, time windows, anddropped visits. The UGV free parameters need to be optimized judiciously inorder to create high quality solutions. We explore two methods for tuning thefree UGV parameters: (1) a genetic algorithm, and (2) Asynchronous Multi-agentarchitecture (Ateams). The A-teams uses multiple agents to create, improve, anddestroy solutions. The parallel asynchronous architecture enables A-teams toquickly optimize the parameters. Our results on test cases show that theA-teams produces similar solutions as genetic algorithm but with a speed up of2-3 times.

Solving Vehicle Routing Problem for unmanned heterogeneous vehicle systems using Asynchronous Multi-Agent Architecture (A-teams)