Multi-UAV Task Assignment With Parameter and Time-Sensitive Uncertainties Using Modified Two-Part Wolf Pack Search Algorithm

Abstract

This paper presents a systematical framework to solve the multiple unmanned aerial vehicles (multi-UAV) cooperative task assignment problem. Based on a combinatorial optimization model, it is solved by a digraph-based method and a novel meta-heuristic optimization method, named modified two-part wolf pack search (MTWPS) algorithm. When the number of UAVs/targets is large, in order to reduce the simulation time, we also present a new solution framework based on an easy-computing objective function. Additionally, the parameter and time-sensitive uncertainty are considered in the extended task assignment problem. For the problem with parameter uncertainty, it is formulated by a robust optimization method and solved by a novel combined algorithm, including the classical interior point method and our MTWPS algorithm. For the problem with time-sensitive uncertainty, it is solved by a practical online hierarchical planning algorithm. Finally, numerical simulations and physical experiments demonstrate that the proposed methods can provide a flyable solution for the UAVs and achieve outstanding performance in comparison with other algorithms. Index Terms-Multiple unmanned aerial vehicles (multi-UAV) cooperative task assignments problem, modified two-part wolf pack search (MTWPS) algorithm, robust optimization method, online hierarchical planning algorithm Yongbo Chen received his B.S. degree in Beijing Institute of Technology in 2012. He is currently working toward a dual doctoral degree at Beijing

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