Zheng Yue, Li Ang, Chen Zihan, Wang Yapeng, Yang Xu, Im Sio-Kei
Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China.
Sensors (Basel). 2025 Jul 2;25(13):4142. doi: 10.3390/s25134142.
The increasing deployment of unmanned aerial vehicles (UAVs) in complex urban environments necessitates efficient and reliable path planning algorithms. While traditional sampling-based methods such as Rapidly exploring Random Tree Star (RRT*) are widely adopted, their computational inefficiency and suboptimal path quality in intricate 3D spaces remain significant challenges. This study proposes a novel framework (MPN-RRT*) that integrates Motion Planning Networks (MPNet) with RRT* to enhance UAV navigation in 3D urban maps. A key innovation lies in reducing computational complexity through dimensionality reduction, where 3D urban terrains are sliced into 2D maze representations while preserving critical obstacle information. Transfer learning is applied to adapt a pre-trained MPNet model to the simplified maps, enabling intelligent sampling that guides RRT* toward promising regions and reduces redundant exploration. Extensive MATLAB simulations validate the framework's efficacy across two distinct 3D environments: a sparse 200 × 200 × 200 map and a dense 800 × 800 × 200 map with no-fly zones. Compared to conventional RRT*, the MPN-RRT* achieves a 47.8% reduction in planning time (from 89.58 s to 46.77 s) and a 19.8% shorter path length (from 476.23 m to 381.76 m) in simpler environments, alongside smoother trajectories quantified by a 91.2% reduction in average acceleration (from 14.67 m/s² to 1.29 m/s²). In complex scenarios, the hybrid method maintains superior performance, reducing flight time by 14.2% and path length by 13.9% compared to RRT*. These results demonstrate that the integration of deep learning with sampling-based planning significantly enhances computational efficiency, path optimality, and smoothness, addressing critical limitations in UAV navigation for urban applications. The study underscores the potential of data-driven approaches to augment classical algorithms, providing a scalable solution for real-time autonomous systems operating in high-dimensional dynamic environments.
无人机（UAV）在复杂城市环境中的部署日益增加，这就需要高效且可靠的路径规划算法。虽然诸如快速扩展随机树星（RRT*）等传统基于采样的方法被广泛采用，但它们在复杂三维空间中的计算效率低下和路径质量欠佳仍然是重大挑战。本研究提出了一种新颖的框架（MPN-RRT*），该框架将运动规划网络（MPNet）与RRT相结合，以增强无人机在三维城市地图中的导航能力。一项关键创新在于通过降维来降低计算复杂度，即将三维城市地形切片成二维迷宫表示形式，同时保留关键的障碍物信息。迁移学习被应用于使预训练的MPNet模型适应简化地图，从而实现智能采样，引导RRT朝着有希望的区域前进，并减少冗余探索。大量的MATLAB模拟在两个不同的三维环境中验证了该框架的有效性：一个稀疏的200×200×200地图和一个带有禁飞区的密集的800×800×200地图。与传统的RRT相比，在较简单的环境中，MPN-RRT的规划时间减少了47.8%（从89.58秒降至46.77秒），路径长度缩短了19.8%（从476.23米降至381.76米），同时轨迹更平滑，平均加速度降低了91.2%（从14.67米/秒²降至1.29米/秒²）。在复杂场景中，与RRT*相比，这种混合方法保持了卓越的性能，飞行时间减少了14.2%，路径长度减少了13.9%。这些结果表明，深度学习与基于采样的规划相结合显著提高了计算效率、路径最优性和平滑度，解决了城市应用中无人机导航的关键限制。该研究强调了数据驱动方法增强经典算法的潜力，为在高维动态环境中运行的实时自主系统提供了一种可扩展的解决方案。
