Reconnaissance Mission Planning Method for Air-ground Heterogeneous Unmanned Systems

Authors

Guohui Zhang, Department of Information and Communication, Academy of Army Armored Force, Beijing 100072, ChinaFollow
Ya'nan Zhang, Department of Information and Communication, Academy of Army Armored Force, Beijing 100072, ChinaFollow
Ang Gao, Joint Operations College, National Defense University, Beijing 100091, China; 3. PLA 93176 Troops, Dalian 116000, China
Aoyu Xu, PLA 93176 Troops, Dalian 116000, China

Abstract

Abstract: Compared with the air-based homogeneous unmanned system, the motion capabilities, resource payloads, and combat scenes in the air-ground heterogeneous unmanned system increase the number of constraint conditions and significantly increase the computational complexity of the solution model. The modeling of collaborative combat missions and the efficient solution of large-scale problems are the key issues. With the time, path cost, and reconnaissance benefit as the objective functions, considering the constraints such as the endurance of unmanned platforms, a multi-objective programming model for the reconnaissance missions of an air-ground heterogeneous unmanned system is constructed. Aiming at the urban combat environments with multiple threat zones, considering the path safety and timeliness of unmanned platform missions, the improved A* algorithm path planning strategies for unmanned aerial vehicles and unmanned ground vehicles are proposed. Aiming at the problem that the optimization effect of snake optimizer (SO) is unstable and easy to fall into local optimal solutions, an improved snake optimizer (IMSO) is proposed by combining the particle swarm algorithm and the genetic algorithm. Simulation verification and comparative analysis with existing algorithms are carried out by using Python language to verify the feasibility of the model and the superiority of the algorithm. Solving 10 tasks independently under three different task loads from small to large, the average objective function values of IMSO are 100.11%, 108.99%, and 110.01% of SO, respectively. It can be seen that IMSO can jump out of local optima multiple times, and the stability and final fitness values of the algorithm are better than SO, and is more superior in solving the larger-scale problems.

Recommended Citation

Zhang, Guohui; Zhang, Ya'nan; Gao, Ang; and Xu, Aoyu (2024) "Reconnaissance Mission Planning Method for Air-ground Heterogeneous Unmanned Systems," Journal of System Simulation: Vol. 36: Iss. 2, Article 18.
DOI: 10.16182/j.issn1004731x.joss.23-0103
Available at: https://dc-china-simulation.researchcommons.org/journal/vol36/iss2/18

First Page

497

Last Page

510

CLC

TP391.9

Recommended Citation

Zhang Guohui, Zhang Ya'nan, Gao Ang, et al. Reconnaissance Mission Planning Method for Airground Heterogeneous Unmanned Systems[J]. Journal of System Simulation, 2024, 36(2): 497-510.

Corresponding Author

Zhang Ya'nan

DOI

10.16182/j.issn1004731x.joss.23-0103