Hierarchical Markov decision processes based path planning for UAV in three-dimensional environment
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摘要: 路径规划是UAV(Unmanned Aerial Vehicle)自主飞行的重要保障.初步建立了基于MDP(Markov Decision Processes)的全局路径规划模型,把UAV的路径规划看作是给定环境模型和奖惩原则的情况下,寻求最优策略的问题;为解决算法时空开销大、UAV航向改变频繁的缺点,提出一种基于状态聚类方法的HMDP(Hierarchical Markov Decision Processes)模型,并将其拓展到三维规划中.仿真实验证明:这种简单的规划模型可以有效解决UAV的三维全局路径规划问题,为其在实际飞行中的局部规划奠定了基础.
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关键词:
- 无人机(UAV) /
- 路径规划 /
- 马尔可夫决策过程(MDP) /
- 分层马尔可夫决策过程(HMDP) /
- 仿真
Abstract: The ability of path planning is an important ensure for unmanned aerial vehicle (UAV) in autonomous flight. A path planning model was based on Markov decision processes (MDP), in which the problem of path planning was regarded as looking for the best tactic through the model of environment and the principle of rewards and punishment. To solve the problem such as huge space-time spending and changing course at high frequency, the hierarchical Markov decision processes (HMDP) were introduced based on the method of clustering states. The arithmetic was also used for path planning in three-dimensional environment. The results of simulation show the HMDP model can be used to path planning for UAV in three-dimensional environment. It lays the foundation for local path planning in real flight. -
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