基于改进粒子群算法的多UAV协同侦察任务规划

Cooperative reconnaissance mission planning for multiple UAVs based on improved PSO algorithm

针对多无人机(UAV)协同侦察的任务规划问题,充分考虑侦察目标的侦察分辨率和时间窗约束,建立了数学模型;提出了一种改进的粒子群算法,使得粒子群能够较均匀地在问题空间内搜索,避免陷入局部极值,在保持传统PSO算法快速收敛的同时,加强了算法局部搜索能力。基于该模型和优化算法,制定了合理的多UAV协同侦察任务计划,使得多UAV协同侦察任务在满足任务要求、平台性能和战场约束的条件下具有最小代价和最优作战效能。

For multiple UAVs cooperative reconnaissance mission planning problem,the mission planning model was established in fully considering the reconnaissance resolution of the target and time window restraint.An improved particle swarm optimization（PSO） algorithm is proposed,which can make the particle swarm optimization evenly search in the question space,to maintain the rapid convergence of PSO and strengthen the partial search capability of the algorithm.The multiple UAVs cooperative reconnaissance mission plan was established based on the model and algorithm above.In order to satisfy the mission requirements,it has lowest cost and optimal effectiveness in the conditions of platform performance and battlefield constraints.