Quality-Oriented Hybrid Path Planning Based on A* and Q-Learning for Unmanned Aerial Vehicle
Quality-Oriented Hybrid Path Planning Based on A* and Q-Learning for Unmanned Aerial Vehicle
复制标题
基于A*和q学习的无人机质量混合路径规划
DOI：
10.1109/access.2021.3139534
复制
发表时间：
2022-01-01
期刊：
影响因子：
3.9
通讯作者：
Chau, Matthew
中科院分区：
文献类型：
--
作者：
Li, Dongcheng;Yin, Wangping;Chau, Matthew
无人驾驶飞机（UAV）由于其低成本，地面支持的低需求，高可操作性，高环境适应性和高安全性，在人们的日常生活中起着越来越重要的作用。然而，由于路径环境的复杂和动态性质，在各种安全风险（例如崩溃和碰撞）下的无人机路径计划并不是一件容易的事。因此，为无人机计划开发一种有效且灵活的算法已成为不可避免的。本文针对面向质量的无人机路径计划，旨在分析两个方面的无人机路径计划：全球静态计划和本地动态分层计划。通过一种理论和数学方法，建立了三维无人机路径计划模型。根据A*算法，搜索策略，步骤大小和成本函数得到了改善，并简化了开放式集合，从而缩短了计划时间并大大提高了算法的执行效率。此外，将动态探索因子添加到Q学习的勘探机制中，以解决Q学习的勘探 - 诠释困境，以适应无人机的局部动态路径调整。通过组合两者，形成了全球 - 本地杂种无人机路径计划算法。仿真结果表明，拟议的计划模型和算法可以有效地解决无人机路径计划的问题，提高路径质量，并且可以是解决与路径计划有关的其他问题的重要参考，例如可靠性，安全性和安全性当嵌入所提出算法的启发式功能中时，无人机的属性。
Unmanned aerial vehicles (UAVs) are playing an increasingly important role in people's daily lives due to their low cost of operation, low requirements for ground support, high maneuverability, high environmental adaptability, and high safety. Yet UAV path planning under various safety risks, such as crash and collision, is not an easy task, due to the complicated and dynamic nature of path environments. Therefore, developing an efficient and flexible algorithm for UAV path planning has become inevitable. Aimed at quality-oriented UAV path planning, this paper is designed to analyze UAV path planning from two aspects: global static planning and local dynamic hierarchical planning. Through a theoretical and mathematical approach, a three-dimensional UAV path planning model was established. Based on the A* algorithm, the search strategy, the step size, and the cost function were improved, and the OPEN set was simplified, thereby shortening the planning time and greatly improving the execution efficiency of the algorithm. Moreover, a dynamic exploration factor was added to the exploration mechanism of Q-learning to solve the exploration-exploitation dilemma of Q-learning to adapt to the local dynamic path adjustment for UAVs. The global-local hybrid UAV path planning algorithm was formed by combining the two. The simulation results indicate that the proposed planning model and algorithm can efficiently solve the problem of UAV path planning, improve the path quality, and can be a significant reference for solving other problems related to path planning, such as the reliability, security, and safety of UAV, when embedded into the heuristic function of the proposed algorithm.