基于马尔科夫生存模型与粒子群算法的动态航路规划
|
摘要
针对未知情况下航路规划问题,采用动态规划策略保证飞机可以实时规划未来路径,并引入基于马尔科夫的生存模型来获取飞机的生存状态概率,从而评估生存代价,再综合任务、油耗、飞机机动性等作为粒子群算法的目标函数与约束条件,同时为了缓解生存与任务之间的矛盾,引入目标函数权重自适应策略.仿真实验证明,提出的动态航路规划策略是可行的,自适应权重也在一定程度上缓解了生存与任务之间的矛盾,同时将基于马尔科夫的生存模型应用于动态航路搜索中,能够更加直观地掌握每一时刻飞机的生存代价以及各状态的概率.
Aiming at the problem of route planning for aircraft under unknown condition,the online planning strategy is adopted to ensure that the aircraft can plan the future path in real time,and Markov survival model is introduced to obtain the survival probability of the aircraft,so as to evaluate the survival cost.Furthermore,missions,oil confusion,aircraft maneuverability are set as the objective function and constraints of PSO(Particle Swarm Optimization)algorithm.At the same time,self-adaptive weight strategy is presented to alleviate the contradiction between survival and missions.The simulation results show that the proposed online route planning strategy is feasible,and the self-adaptive weight also alleviates the contradiction between the survival and the mission.The application of Markov survival model in online route planning can indeed have more effective command at the survival cost and state probability of aircraft in each moment.
Aiming at the problem of route planning for aircraft under unknown condition,the online planning strategy is adopted to ensure that the aircraft can plan the future path in real time,and Markov survival model is introduced to obtain the survival probability of the aircraft,so as to evaluate the survival cost.Furthermore,missions,oil confusion,aircraft maneuverability are set as the objective function and constraints of PSO(Particle Swarm Optimization)algorithm.At the same time,self-adaptive weight strategy is presented to alleviate the contradiction between survival and missions.The simulation results show that the proposed online route planning strategy is feasible,and the self-adaptive weight also alleviates the contradiction between the survival and the mission.The application of Markov survival model in online route planning can indeed have more effective command at the survival cost and state probability of aircraft in each moment.
引文
[1]屈耀红.小型无人机航迹规划及组合导航关键技术研究[D].西安:西北工业大学,2006.
[2]Peng Z,Li B,Chen X,et al.Online route planning for UAV based on model predictive control and particle swarm optimization algorithm[C]//Intelligent Control and Automation.[s.l.]:IEEE,2012.
[3]Sun B,Zhu F.Real-time track-planning of UAVs based on PSO algorithm[J].Electron Optics Control,2008,15:35.
[4]Wang X Z,Lin-Lin C I,Jun-Shan L I,et al.Realtime route planning for UAV based on improved PSO algorithm[J].Microelectron Comput,2011,28:87.
[5]Roberge V,Tarbouchi M,Labonte G.Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning[J].IEEE Trans Ind Inf,2013,9:132.
[6]Tulum K,Durak U,Yder S K.Situation aware UAV mission route planning[C]//Proceedings of the IEEE Aerospace Conference.[s.l.]:IEEE,2009.
[7]Foo J L,Knutzon J,Oliver J,et al.Three-dimensional path planning of unmanned aerial vehicles using particle swarm optimization[C]//Proceedings of the Aiaa/issmo Multidisciplinary Analysis&Optimization Conference.[s.l.]:AIAA,2006.
[8]何光,龙宪军.基于粒子群优化算法的期权波动率估计[J].四川大学学报:自然科学版,2017,54:925.
[9]Erlandsson T,Niklasson L.Automatic evaluation of air mission routes with respect to combat survival[J].Inform Fusion,2014,20:88.
[10]Wang X Z,Lin-Lin C I,Jun-Shan L I,et al.Real-time route planning for UAV based on improved PSO algorithm[J].Microelectron Comput,2011,28:87.
[11]Kelmelis E J.A five states survivability model for missions with ground-to-air threats[J].Proc SPIE-Int Soc Opt Eng,2013,8752:875207.
[2]Peng Z,Li B,Chen X,et al.Online route planning for UAV based on model predictive control and particle swarm optimization algorithm[C]//Intelligent Control and Automation.[s.l.]:IEEE,2012.
[3]Sun B,Zhu F.Real-time track-planning of UAVs based on PSO algorithm[J].Electron Optics Control,2008,15:35.
[4]Wang X Z,Lin-Lin C I,Jun-Shan L I,et al.Realtime route planning for UAV based on improved PSO algorithm[J].Microelectron Comput,2011,28:87.
[5]Roberge V,Tarbouchi M,Labonte G.Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning[J].IEEE Trans Ind Inf,2013,9:132.
[6]Tulum K,Durak U,Yder S K.Situation aware UAV mission route planning[C]//Proceedings of the IEEE Aerospace Conference.[s.l.]:IEEE,2009.
[7]Foo J L,Knutzon J,Oliver J,et al.Three-dimensional path planning of unmanned aerial vehicles using particle swarm optimization[C]//Proceedings of the Aiaa/issmo Multidisciplinary Analysis&Optimization Conference.[s.l.]:AIAA,2006.
[8]何光,龙宪军.基于粒子群优化算法的期权波动率估计[J].四川大学学报:自然科学版,2017,54:925.
[9]Erlandsson T,Niklasson L.Automatic evaluation of air mission routes with respect to combat survival[J].Inform Fusion,2014,20:88.
[10]Wang X Z,Lin-Lin C I,Jun-Shan L I,et al.Real-time route planning for UAV based on improved PSO algorithm[J].Microelectron Comput,2011,28:87.
[11]Kelmelis E J.A five states survivability model for missions with ground-to-air threats[J].Proc SPIE-Int Soc Opt Eng,2013,8752:875207.