基于Q-Learning的障碍环境下无人机集群路径规划系统
Q-Learning based system for path planning with unmanned aerial vehicles swarms in obstacle environments
AI总结：
针对障碍环境下无人机集群区域覆盖路径规划问题，提出基于Q-Learning的强化学习系统，采用人工神经网络自调整，无需先验信息，实验验证其随无人机数量增多路径动作更少、能耗更低，效果优于现有前沿方法。
摘要：
无人机（Unmanned Aerial Vehicle, UAV）集群自主控制的路径规划方法凭借其带来的诸多优势正蓬勃发展。需要多无人机自主控制的场景越来越多，这类场景中大多存在大量障碍物，例如电线或树木。若所有无人机都能自主运行，便可降低人力成本。此外，若无人机的飞行路径达到最优，还能减少能耗，从而为其他作业留出更多电池续航时间。本文提出了一种基于强化学习的系统，利用Q-Learning解决存在障碍物的环境中的该类问题。该方法允许模型——在本研究中具体为人工神经网络（Artificial Neural Network）——通过从自身的错误和成果中学习进行自我调整。无论地图大小或集群中无人机的数量如何，这些路径的目标都是确保完全覆盖存在固定障碍物的区域，以执行田野勘探等任务。该系统无需设定目标，也不需要除提供的地图之外的任何先验信息。实验阶段使用了五张大小不同、障碍物各异的地图，且采用不同数量的无人机开展实验。计算结果时，将每次实验中所有无人机完成任务所采取的动作数量纳入考量：动作数量越少，路径越短，能耗也越低。实验结果令人满意，表明无人机数量越多，该系统生成解决方案所需的动作次数越少。为了更直观地展示效果，研究还将这些结果与另一种前沿（state-of-the-art）方法进行了对比。
英文摘要：
Path Planning methods for autonomous control of Unmanned Aerial Vehicle (UAV) swarms are on the rise because of all the advantages they bring. There are more and more scenarios where autonomous control of multiple UAVs is required. Most of these scenarios present a large number of obstacles, such as power lines or trees. If all UAVs can be operated autonomously, personnel expenses can be decreased. In addition, if their flight paths are optimal, energy consumption is reduced. This ensures that more battery time is left for other operations. In this paper, a Reinforcement Learning based system is proposed for solving this problem in environments with obstacles by making use of Q-Learning. This method allows a model, in this particular case an Artificial Neural Network, to self-adjust by learning from its mistakes and achievements. Regardless of the size of the map or the number of UAVs in the swarm, the goal of these paths is to ensure complete coverage of an area with fixed obstacles for tasks, like field prospecting. Setting goals or having any prior information aside from the provided map is not required. For experimentation, five maps of different sizes with different obstacles were used. The experiments were performed with different number of UAVs. For the calculation of the results, the number of actions taken by all UAVs to complete the task in each experiment is taken into account. The lower the number of actions, the shorter the path and the lower the energy consumption. The results are satisfactory, showing that the system obtains solutions in fewer movements the more UAVs there are. For a better presentation, these results have been compared to another state-of-the-art approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
