Cooperative Air-Ground Vehicle Routing using Chance-Constrained Optimization

Abstract

This paper studies a cooperative air-ground routing problem, in which an unmanned aerial vehicle (UAV) and a ground vehicle coordinate with one another to visit a set of targets. The UAV, subject to limits on storage capacity and maximum flight distance, must rendezvous with the ground vehicle periodically, to upload the collected data and recharge. To solve such a cooperative routing problem, we formulate a chance-constrained mixed-integer linear program (CC-MILP), where the chance constraint captures uncertainties in the model, such as uncertain speed or travel time of the two vehicles. Consequently, the obtained solution is less conservative compared to those found using robust methods, since only the worst case realization of uncertainties is considered in the later methods. Also novel to our approach, is that air ground rendezvous locations are not restricted to occur only at targets. Instead, the two vehicles are free to meet at any point on the UAV's path, which significantly increases the feasible solution space and leads to better solutions. We corroborate the effectiveness of the proposed approach through a set of numerical simulations on randomly generated instances.