ICUAS'23 Paper Abstract

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Paper WeA3.3

Ramasamy, Subramanian (University of Illinois at Chicago), Bhounsule, Pranav (University of Illinois at Chicago), Mondal, Mohammad Safwan (University of Illinois at Chicago), Humann, James D. (DEVCOM Army Research Laboratory), Reddinger, Jean-Paul (DEVCOM Army Research Laboratory), Dotterweich, James (DEVCOM Army Research Laboratory), Childers, Marshal (DEVCOM Army Research Laboratory)

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

Scheduled for presentation during the Regular Session "Path Planning I" (WeA3), Wednesday, June 7, 2023, 11:40−12:00, Room 464

2023 International Conference on Unmanned Aircraft Systems (ICUAS), June 6-9, 2023, Lazarski University, Warsaw, Poland

This information is tentative and subject to change. Compiled on March 29, 2024

Keywords Path Planning, Energy Efficient UAS, Air Vehicle Operations

Abstract

Fast moving but power hungry unmanned aerial vehicles (UAVs) can recharge on slow-moving unmanned ground vehicles (UGVs) to cooperatively perform tasks over wide areas. Such a cooperation can be achieved efficiently by solving a path planning problem. On top of solving a path planning problem, the problem of routing an heterogeneous set of vehicles in an optimal fashion is quite challenging. In order to solve the computationally expensive path-planning problem in a reasonable time, we created a two-level optimization approach with heuristics. At the outer level, the UGV route is parameterized by considering which set of locations to visit in the scenario and the UGV wait times to recharge UAVs and at the inner level, the UAV route is solved by formulating and solving a vehicle routing problem with capacity constraints, time windows, and dropped visits. The UGV free parameters need to be optimized judiciously in order to create high quality solutions. We explore two methods for tuning the free UGV parameters: (1) a Genetic Algorithm (GA), and (2) Asynchronous Multi-agent architecture (A-teams). The A-teams uses multiple agents to create, improve, and destroy solutions. The parallel asynchronous architecture enables A-teams to quickly optimize the parameters. Our results on test cases show that the A-teams produces similar solutions as GA but is 2-3 times faster.

 

 

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