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出 处:《计算机测量与控制》2016年第6期181-184,共4页Computer Measurement &Control
摘 要:无人机航迹规划是无人机任务规划中最重要也是最复杂的环节,针对基本粒子群航迹规划算法后期容易陷入局部最优解、算法容易"早熟"、规划出的航迹精度不高等问题,提出了一种以并行方式进行的双种群粒子群航迹规划算法;双种群粒子群算法由两个向相反方向搜索的种群构成,这两个种群协同优化,扩展了搜索范围,克服了基本粒子群算法后期容易陷入局部最优解的问题,提高了航迹的精度;如果无人机在飞行过程中检测到突发威胁,则寻找邻近航迹点作为实时重规划点,规划其到目标点的航迹;通过仿真验证了算法的有效性,并满足了实时性的要求。UAV path planning is the most important and most complex part of UAV mission planning.Because particle swarm algo-rithm is easy to fell into local optimal solution and“premature”,and the precision of planning flight route is low,a parallel implementation of two-swarm particle swarm algorithm for route planning is presented,this algorithm is comprised of two particle swarm algorithms which have reverse direction of search,and this algorithm can extend the range of search through cooperation of two particle swarm algorithms,over-come the problem of falling into local optimal solution and improve the precision of flight route.With unexpected threat detected in flight, UAV will search near point of flight route and then replan route from this point to target point.Simulation results show the effectiveness of the algorithm.
分 类 号:V279[航空宇航科学与技术—飞行器设计]
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