In some monitoring tasks that require high real-time performance,a method for multi-UAV path planning with minimum information delay is proposed to enhance the efficiency of collaborative work among multiple UAVs.Firstly,the maximum information delay is introduced to describe the timeliness of monitoring information.The constraints of UAV energy and no-fly zones are all taken in account and a multi-UAV path planning model with UAV information delay and total fight distance as optimization objectives is established.Subsequently,an improved multi-objective grey wolf optimizer is proposed,which incorporates a crossover operator to enhance global search ability and a large-scale neighborhood algorithm to improve local search ability.Finally,the probabilistic roadmap algorithm is used to perform local obstacle avoidance optimization on the obtained flight plan in order to obtain optimal planned path.The simulation and physical experimental results demonstrate that the proposed algorithm not only achieves favorable planned path but also,compared to the NSGA-II,reduces the total flight path distance by 3.34%and 5.09%respectively,and decreases the maximum delay time by 11.02%and 15.66%.This validates the feasibility and effectiveness of the proposed algorithm.
information delayimproved multi-objective grey wolf optimizerpath planningobstacle avoidanceprobabilistic road map algorithm
维普
万方数据
