基于细菌觅食算法的多异构无人机任务规划

Multi-heterogeneous UAV task planning based on bacterial foraging algorithm

针对多无人机任务规划问题,以细菌觅食算法为基础,融合遗传算法的交叉变异操作,进行任务分配。为了提高算法的收敛能力,动态自适应调节算法的游动步长、繁殖次数和迁徙概率。基于Lyapunov导航向量场和避障向量场构建融合向量场,模拟真实静态和动态障碍物环境,在任务分配阶段完成航迹规划;基于合同网拍卖算法,进行无人机坠毁后的任务重分配。仿真结果显示,改进算法满足任务规划需求,在考虑静态和动态障碍物的环境下,能够高效的完成多异构无人机的任务分配以及重分配且总代价最小。

Aiming at the problem of multi unmanned aerial vehicle(UAV)task planning,based on the bacterial foraging algorithm,the cross-mutation operation of genetic algorithm is combined to perform task assignment.In order to improve the convergence ability of the algorithm,the swimming step length,reproduction times and migration probability of the algorithm are dynamically and adaptively adjusted.A fusion vector field based on the Lyapunov navigation vector field and obstacle avoidance vector field,the real static and dynamic obstacle environment is simulated,and the trajectory planning is completed in the task allocation stage;at the same time,based on the contract network auction algorithm,the task is redistributed after UAV attacked.The simulation results show that,considering static and dynamic obstacles,the task allocation and redistribution of multi-heterogeneous UAVs can be efficiently completed with minimal total cost.