RWPSO与马尔科夫链的无人机航路规划

UAV route planning based on RWPSO and Markov chain

摘要:

粒子群算法（PSO）是基于种群的全局搜索算法,具有原理简单,搜索稳定高效等特性,在航路规划领域被普遍运用,但是其在陷入局部最优以及收敛速度方面都存在一定的缺陷.本文针对无人机的任务权重值与生存权重值引入随机游走策略,按照一定规律改变粒子的惯性权重值,可以有效的避免上述情况发生,提升无人机在航路规划中找到最优路径的效率.另一方面,为了能够给规划的路径提供优劣性的判断标准或参考依据,需要构建适用于评估无人机飞行路径点上的生存状态概率模型,本文将随机游走粒子群算法（RWPSO）的航路规划模型与马尔科夫链生存状态随机性模型相结合,得到一个可以用来评估路径点生存概率的航路规划问题模型.仿真结果表明,基于任务权重、生存权重、任务生存权重随机游走的RWPSO算法在寻优时比PSO、量子粒子群算法（QPSO）效率更高,并成功结合马尔科夫链得到一个可以描述出无人机生存概率变化的模型.此模型框架还能够扩展应用于有辐射源、武器、电磁干扰等复杂场景中的航路与任务规划.

Abstract:

Particle swarm optimization (PSO) is a global search algorithm based on population, which is characterized by simple principle and stable and efficient search. It is widely used in the field of route planning, but it is defective when falling into local optimum and in convergence speed. In this paper, random walk strategy is introduced to the mission weight and survival weight of UAV. By changing the inertia weight of particles according to certain rules, PSO’s defects can be effectively avoided, and UAV’s efficiency in finding the optimal path can be improved. On the other hand, in order to provide a criterion or reference to evaluate planned path, it is necessary to construct a survival state probability model to evaluate UAV flight path points. The route planning model of the random walk particle swarm optimization algorithm (RWPSO) was combined with the Markov chain survival state randomness model, thereby building up a route planning model for estimating the survival probability of path points. Simulation results show that RWPSO based on random walk of task weight, survival weight, and task survival weight was more efficient than PSO and quantum particle swarm optimization (QPSO) in optimization. A model describing the change of survival probability of UAV was thus obtained successfully by combining Markov chain with RWPSO. The framework can be extended to route and mission planning in complex scenes with radiation sources, weapons, or electromagnetic interference.