Multi-UAV path planning with minimum information delay

被引:0
作者
Chen Y. [1 ]
Zhong S. [1 ]
Chen Z. [1 ]
机构
[1] (1. Institute of Robotics and Intelligent Systems, Wuhan University of Science and Technology
[2] 2. Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology
来源
Zhongguo Guanxing Jishu Xuebao/Journal of Chinese Inertial Technology | 2024年 / 32卷 / 05期
关键词
improved multi-objective grey wolf optimizer; information delay; obstacle avoidance; path planning; probabilistic road map algorithm;
D O I
10.13695/j.cnki.12-1222/o3.2024.05.013
中图分类号
学科分类号
摘要
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. © 2024 Editorial Department of Journal of Chinese Inertial Technology. All rights reserved.
引用
收藏
页码:521 / 530
页数:9
相关论文
共 19 条
[1]  
He W, Hu Y, Li W., Heterogeneous UAV cooperative reconnaissance path planning based on improved Harris hawks algorithm, Journal of Chinese Inertial Technology, 31, pp. 717-723, (2023)
[2]  
Zheng K, Yin D, Yin S, Et al., Multi-stage mission plan method of multi-UAVs deployed in different bases based on improved A* algorithm, Journal of Chinese Inertial Technology, 30, pp. 248-256, (2022)
[3]  
Zhang X, Hu Y, Li W, Et al., Multi-UAV fire fighting mission planning based on improved artificial bee colony algorithm, Journal of Chinese Inertial Technology, 28, pp. 528-536, (2020)
[4]  
Wu Z, Yang Z, Yang C, Et al., Joint deployment and trajectory optimization in UAV-assisted vehicular edge computing networks, Journal of Communications and Networks, 24, 1, pp. 47-58, (2021)
[5]  
Karamuz E, Romanowicz R J, Doroszkiewicz J., The use of unmanned aerial vehicles in flood hazard assessment, Journal of Flood Risk Management, 13, 4, (2020)
[6]  
Han Y, Ma W., Automatic monitoring of water pollution based on the combination of UAV and USV, 2021 IEEE 4th International Conference on Electronic Information and Communication Technology (ICEICT), pp. 420-424, (2021)
[7]  
Xu Y, Li J, Zhang F., A UAV-based forest fire patrol path planning strategy, Forests, 13, 11, (2022)
[8]  
Ren Q, Yao Y, Yang G, Et al., Multi-objective path planning for UAV in the urban environment based on CDNSGA-II, 2019 IEEE International Conference on Service-Oriented System Engineering (SOSE), pp. 350-3505, (2019)
[9]  
Li X, Xu J., Positioning optimization for sum-rate maximization in UAV-enabled interference channel, IEEE Signal Processing Letters, 26, 10, pp. 1466-1470, (2019)
[10]  
Zhu X, Qi F, Feng Y., Deep-learning-based multiple beamforming for 5G UAV IoT networks, IEEE Network, 34, 5, pp. 32-38, (2020)