摘要
针对复杂地形和威胁环境下的无人机航路规划问题,对粒子群算法进行了改进,提出了融入威胁启发机制的改进粒子群算法。充分利用无人机在任务区域中已知的威胁信息,将其作为威胁启发项,构成粒子群速度更新公式的一部分,有效丰富粒子群算法的搜索行为,增强粒子在搜索过程中的针对性和指导性。使用最小威胁曲面方法,降低粒子编码的维数,并采用航路在线再规划的方法解决无人机飞行过程可能遇到的突发威胁。仿真试验表明,所提方法能够有效地规划出无人机的最优航路,提高规划过程的时效性,并且满足航路再规划的实时性要求。
In order to solve the problem of UAV＇s route planning under the environment with complex terrain and threats, an improved Particle Swarm Optimization （PSO） was proposed, in which the threat heuristic mechanism was integrated. The new algorithm made full use of the known threat information in mission area and took it as the threat heuristic item for forming the particles＇ velocity updating formula. The threat heuristic information could enhance the guiding movement of particles in mission area, enrich search behavior of PSO and improve the planning efficiency. Surface of minimum risk was used for reducing the dimension of particles, and online route planning was adopted to deal with the unexpected threats. The simulation results showed that the proposed method can effectively obtain the optimum route for the UAV with less planning time than standard PSO, and satisfy the real-time requirement of online route replanning.
出处
《电光与控制》
北大核心
2011年第12期1-4,43,共5页
Electronics Optics & Control
基金
航空科学基金(20101352015)
关键词
无人机
航路规划
粒子群算法
启发信息
Unmanned Aerial Vehicle （UAV）
route planning
particle swarm optimization
heuristicinformation
