Cooperative Motion Planning for Persistent 3D Visual Coverage With Multiple Quadrotor UAVs

强化学习 运动规划 计算机科学 弹道 地形 任务(项目管理) 人工智能 实时计算 模拟 数学优化 机器人 工程类 数学 生物 物理 生态学 系统工程 天文
作者
Hongpeng Wang,Shangyuan Song,Qiang-Hui Guo,Dian Xu,Xiaoyang Zhang,P. Wang
出处
期刊:IEEE Transactions on Automation Science and Engineering [Institute of Electrical and Electronics Engineers]
卷期号:21 (3): 3374-3383 被引量:2
标识
DOI:10.1109/tase.2023.3279092
摘要

In this paper, we address the multiple quadrotor UAVs trajectory planning optimization problem for large-scale, persistent, high-depth visual coverage tasks in three-dimensional (3-D) terrain environment. To minimize the overall energy expenditure of the UAVs for accomplishing a task, we set up an air-to-ground collaborative system which introduces base stations to hold and recharge UAVs. The system is formulated as an integer programming, and solved by a novel hierarchical reinforcement learning trajectory planning algorithm (RL-TP), in which the paths are obtained by reinforcement learning method, and then the trajectories are obtained by Bézier curve method. Both simulation and physical experiments show that RL-TP can effectively improve the efficiency and persistence of aerial visual coverage task. Note to Practitioners —While the multi-rotor UAV has been an important means for field monitoring, it suffers the problem of short battery life a lot. To make it more efficient and persistent, we use multiple UAVs and introduce ground base stations to charge the UAVs. The scenario is formulated as an air-to-ground collaborative system, and the motion planning strategy is to minimize the energy consumption. We propose a hierarchical collaborative coverage reinforcement learning trajectory planning algorithm (RL-TP) to solve it. We carry out both simulation and physical field experiments, and compare RL-TP with other popular methods. The experimental results show that the system is feasible and RL-TP performs well in both time efficiency and energy consumption. In future research, we will introduce unmanned ground vehicles to replace the stationary ground base stations to make the air-to-ground collaborative system more powerful and flexible.
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