Abstract
For unmanned aerial vehicle (UAV) cooperative search mission under communication distance limitation environment, considering that the computation scale of rolling time domain optimization increases exponentially with the change of predicted steps and the problem that searching performance metrics are difficult to be selected, the distributed receding horizon non-dominated sorting genetic algorithm (DRH-NSGA) is proposed, and the distributed receding horizon (DRH) process is optimized by non-dominated sorting genetic algorithm (NSGA),and the cooperative search problem in dynamic environment is solved. Firstly, the motion and sensor models of UAV are established. Considering the possibility of interference or communication interruption during UAV communication, the communication interruption probability is added based on the communication distance constraint. In this paper, the environment awareness map is constructed, considering the motion characteristics of time-sensitive targets, and based on the updating of target probability by using Bayesian theory, the updating of time-sensitive target prediction transition probability is added to improve the accuracy of moving target existence probability map. Finally, the search performance index is designed reasonably, and the RH framework is used to optimize the search path. The simulation results show that the distributed cooperative search algorithm based on DRH-NSGA can shorten the single step planning time while ensuring the convergence speed and ensure the real-time decision-making of UAV search. At the same time, it can effectively take into account the search of moving targets after covering the search area.
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Si, J.S., Cheng, J., Hao, M.R., Liu, Z.C. (2023). Distributed Cooperative Search Based on DRH-NSGA Under Communication Constraint. In: Yan, L., Duan, H., Deng, Y. (eds) Advances in Guidance, Navigation and Control. ICGNC 2022. Lecture Notes in Electrical Engineering, vol 845. Springer, Singapore. https://doi.org/10.1007/978-981-19-6613-2_108
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DOI: https://doi.org/10.1007/978-981-19-6613-2_108
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