Min-Max Task Assignment and Sequencing with Heterogeneous Unmanned Vehicles

Abstract

This article addresses a planning problem for a team of heterogeneous, unmanned surface vehicles whose time costs are attributable to either transiting or task execution costs. Given a set of target regions and a team of unmanned vehicles, the Heterogeneous Multi-vehicle Planning Problem (HMPP) aims to find a tour for each vehicle such that each target is visited at least once by some vehicle and the maximum mission cost of any unmanned vehicle is minimized. The mission cost incurred by each unmanned vehicle in this work includes its travel costs as well as the costs involved in performing the tasks in the regions visited by the vehicle. This problem is a generalization of the single vehicle Traveling Salesman Problem and is NP-Hard. We develop a fast approximation algorithm that provides a feasible solution with a bound on the cost of solution found and improve on it further through variable neighborhood search heuristics. We also present numerical results to corroborate the performance of the proposed approaches.