Zhang Guoqing, Liu Jiandong, Luo Wei, Zhao Yongxiang, Tang Ruiyin, Mei Keyu, Wang Penggang
North China Institute of Aerospace Engineering, School of Remote Sensing and Information Engineering, Langfang 065000, China.
Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang 065000, China.
Sensors (Basel). 2024 Nov 25;24(23):7514. doi: 10.3390/s24237514.
Unmanned aerial vehicles (UAVs) have made significant advances in autonomous sensing, particularly in the field of precision agriculture. Effective path planning is critical for autonomous navigation in large orchards to ensure that UAVs are able to recognize the optimal route between the start and end points. When UAVs perform tasks such as crop protection, monitoring, and data collection in orchard environments, they must be able to adapt to dynamic conditions. To address these challenges, this study proposes an enhanced Q-learning algorithm designed to optimize UAV path planning by combining static and dynamic obstacle avoidance features. A shortest distance priority (SDP) strategy is integrated into the learning process to minimize the distance the UAV must travel to reach the target. In addition, the root mean square propagation (RMSP) method is used to dynamically adjust the learning rate according to gradient changes, which accelerates the learning process and improves path planning efficiency. In this study, firstly, the proposed method was compared with state-of-the-art path planning techniques (including A-star, Dijkstra, and traditional Q-learning) in terms of learning time and path length through a grid-based 2D simulation environment. The results showed that the proposed method significantly improved performance compared to existing methods. In addition, 3D simulation experiments were conducted in the AirSim virtual environment. Due to the complexity of the 3D state, a deep neural network was used to calculate the Q-value based on the proposed algorithm. The results indicate that the proposed method can achieve the shortest path planning and obstacle avoidance operations in an orchard 3D simulation environment. Therefore, drones equipped with this algorithm are expected to make outstanding contributions to the development of precision agriculture through intelligent navigation and obstacle avoidance.
无人机（UAVs）在自主传感方面取得了重大进展，尤其是在精准农业领域。有效的路径规划对于大型果园中的自主导航至关重要，以确保无人机能够识别起点和终点之间的最佳路线。当无人机在果园环境中执行诸如作物保护、监测和数据收集等任务时，它们必须能够适应动态条件。为应对这些挑战，本研究提出了一种增强的Q学习算法，旨在通过结合静态和动态避障功能来优化无人机路径规划。一种最短距离优先（SDP）策略被集成到学习过程中，以最小化无人机到达目标所需飞行的距离。此外，均方根传播（RMSP）方法用于根据梯度变化动态调整学习率，这加速了学习过程并提高了路径规划效率。在本研究中，首先，通过基于网格的二维模拟环境，将所提出的方法与现有最先进的路径规划技术（包括A*算法、迪杰斯特拉算法和传统Q学习）在学习时间和路径长度方面进行了比较。结果表明，与现有方法相比，所提出的方法显著提高了性能。此外，在AirSim虚拟环境中进行了三维模拟实验。由于三维状态的复杂性，基于所提出的算法使用了深度神经网络来计算Q值。结果表明，所提出的方法能够在果园三维模拟环境中实现最短路径规划和避障操作。因此，配备该算法的无人机有望通过智能导航和避障为精准农业的发展做出杰出贡献。
