Optimal scheduling for aerial recovery of multiple unmanned aerial vehicles using genetic algorithm

Abstract

The ability to deploy multiple unmanned aerial vehicles expands their application range, but aerial recovery of unmanned aerial vehicles presents many unique challenges owing to the number of unmanned aerial vehicles and the limited recovery time. In this paper, scheduling the aerial recovery of multiple unmanned aerial vehicles by one mothership is posed as a combinatorial optimization problem. A mathematical model with recovery time windows of the unmanned aerial vehicles is developed to formulate this problem. Furthermore, a genetic algorithm is proposed for finding the optimal recovery sequence. The algorithm adopts the path representation of chromosomes to simplify the encoding process and the genetic operations. It also resolves decoding difficulties by iteration, and thus can efficiently generate a recovery timetable for the unmanned aerial vehicles. Simulation results in stochastic scenarios validate the performance of the proposed algorithm compared with the random search algorithm and the greedy algorithm.