A Task Assignment Algorithm for Multiple Aerial Vehicles to Attack Targets With Dynamic Values

A good task assignment is an important guarantee to achieve great combat effectiveness. This paper investigates the task assignment problem, where the value of the targets is time changing in the battlefield, and presents a solution approach that is a combination of two algorithms: the multidestination route planning algorithm based on dynamic programming and the multisubgroup ant colony algorithm (MSACO). The two algorithms coordinately solve the task assignment problem. The route planning algorithm can obtain available routes between any two targets and provide reasonable routing information for MSACO. Then, the ant colony algorithm is applied to solve the task assignment problem. To solve the task assignment problem in the battlefield environment, several key technologies are introduced to improve the traditional ant colony algorithm, which include the subgroup selection strategy, the dynamic candidate aggregate policy, the state transferring policy, and the information-element updating mechanism. Simulation results show that the proposed approach can produce a reasonable and available plan for all the test cases in short computational time.

I. Introduction

Multiple aerial vehicles can coordinately prevent people from entering dangerous areas and keep operating for a long time and can also help achieve much more effectiveness than a single vehicle [1]–[8]. In recent decades, because of the wide application for aerial vehicles in wars, particularly for unmanned aerial vehicles, corporate task assignment for multiple aerial vehicles has gained increasing attention, and more practical applications are also presented [9]–[25]. When multiple aerial vehicles work together, their task and route should be properly planned [4]. This paper investigates the situation where the value of the targets is dynamically changing and develops an efficient approach to solve the task assignment problem.