Multi-Target Assignment and Path Planning for Groups of UAVs

Abstract

Uninhabited autonomous vehicles (UAVs) have many useful military applications, including reconnaissance, search-and-destroy, and search-and-rescue missions in hazardous environments such as battlefields or disaster areas. Recently, there has been considerable interest in the possibility of using large teams (swarms) of UAVs functioning cooperatively to accomplish a large number of tasks (e.g., finding and attacking targets). However, this requires the assignment of multiple spatially distributed tasks to each UAV along with a feasible path that minimizes effort and avoids threats.

In this work, we consider an extended environment with M UAVs, N targets and P threats. The goal is to assign all the targets to the UAVs so as to minimize the maximum path length, divide work equitably among the UAVs, and limit the threat faced by each UAV. We use a four stage approach to address this problem. First, a Voronoi tessellation around the threats is used to create a graph of potential paths and waypoints. The segments of this graph are then systematically removed by a threat/cost-based thresholding process to obtain a feasible set of path elements. In the second stage, this reduced graph is searched to identify short paths between tasks and from UAVs to tasks. In the third stage, initial paths for UAVs are constructed using a semi-greedy heuristic that divides tasks equally among UAVs. Finally, in the fourth stage, this initial assignment is refined using spatially constrained exchange of sub-paths among UAVs. A direct method for obtaining paths of approximately equal length is also considered.

This work was supported by the AFRL/VA and AFOSR Collaborative Center of Control Science (Grant F33615–01–2–3154).