空地异构无人系统侦察任务规划方法

Reconnaissance Mission Planning Method for Air-ground Heterogeneous Unmanned Systems

相对空中同构无人系统,空地异构无人系统的运动能力、资源载荷、作战场景等异构性质会导致约束条件增多,使求解模型计算量显著增加,协同作战任务的建模和大规模问题的高效求解是需要解决的关键问题。以无人系统完成任务的时间、路径代价、侦察收益为目标函数,同时考虑无人平台续航能力等约束条件,合理构建了空地异构无人系统侦察任务的多目标规划模型;针对具有多威胁区的城市作战环境,考虑无人平台任务路径的安全性和时效性,分别提出了无人机和无人车改进A^(*)算法路径规划策略。针对蛇优化算法(snake optimizer,SO)优化效果不稳定、容易陷入局部最优解的问题,结合粒子群算法和遗传算法提出了改进蛇优化算法(improved snake optimizer,IMSO);通过Python语言进行了仿真验证和与现有算法的对比分析,验证了模型的可行性和算法的优越性。不同算法在由小到大的3种任务载荷设置下独求解10次,IMSO的平均目标函数值分别为SO的100.11%、108.99%和110.01%,可以看出IMSO能多次跳出局部最优,算法的稳定性、最终适应度值均好于SO,在较大规模问题的求解上更具有优越性。

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.