A MultiObjective Optimization Approach for MultiVehicle Path Planning Problems Considering Human–Robot Interactions

Abstract

There has been unprecedented development in the field of unmanned ground vehicles (UGVs) over the past few years. UGVs have been used in many fields including civilian and military with applications such as military reconnaissance, transportation, and search and research missions. This is due to their increasing capabilities in terms of performance, power, and tackling risky missions. The level of autonomy given to these UGVs is a critical factor to consider. In many applications of multirobotic systems like “searchandrescue” missions, teamwork between human and robots is essential. In this article, given a team of manned ground vehicles (MGVs) and UGVs, the objective is to develop a model that can minimize the number of teams and total distance traveled while considering human–robot interaction (HRI) studies. The human costs of managing a team of UGVs by a MGV are based on human–robot interaction (HRI) studies. In this research, we introduce a combinatorial, multiobjective ground vehicle path planning problem that takes human–robot interactions into consideration. The objective of the problem is to find: ideal number of teams of MGVsUGVs that follow a leader–follower framework where a set of UGVs follow an MGV and path for each team such that the missions are completed efficiently.