MPN-RRT*: A New Method in 3D Urban Path Planning for UAV Integrating Deep Learning and Sampling Optimization
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...
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MDPI AG
2025-07-01
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| author | Yue Zheng Ang Li Zihan Chen Yapeng Wang Xu Yang Sio-Kei Im |
| author_facet | Yue Zheng Ang Li Zihan Chen Yapeng Wang Xu Yang Sio-Kei Im |
| author_sort | Yue Zheng |
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| description | 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. |
| format | Article |
| id | doaj-art-0f864ff7f41d4d0498a0bd9daceecb75 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-0f864ff7f41d4d0498a0bd9daceecb752025-08-20T03:16:56ZengMDPI AGSensors1424-82202025-07-012513414210.3390/s25134142MPN-RRT*: A New Method in 3D Urban Path Planning for UAV Integrating Deep Learning and Sampling OptimizationYue Zheng0Ang Li1Zihan Chen2Yapeng Wang3Xu Yang4Sio-Kei Im5Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, ChinaFaculty of Applied Sciences, Macao Polytechnic University, Macao 999078, ChinaFaculty of Applied Sciences, Macao Polytechnic University, Macao 999078, ChinaFaculty of Applied Sciences, Macao Polytechnic University, Macao 999078, ChinaFaculty of Applied Sciences, Macao Polytechnic University, Macao 999078, ChinaFaculty of Applied Sciences, Macao Polytechnic University, Macao 999078, ChinaThe 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.https://www.mdpi.com/1424-8220/25/13/4142deep learningpath planningUAVMPNetRRT*MPN-RRT* |
| spellingShingle | Yue Zheng Ang Li Zihan Chen Yapeng Wang Xu Yang Sio-Kei Im MPN-RRT*: A New Method in 3D Urban Path Planning for UAV Integrating Deep Learning and Sampling Optimization Sensors deep learning path planning UAV MPNet RRT* MPN-RRT* |
| title | MPN-RRT*: A New Method in 3D Urban Path Planning for UAV Integrating Deep Learning and Sampling Optimization |
| title_full | MPN-RRT*: A New Method in 3D Urban Path Planning for UAV Integrating Deep Learning and Sampling Optimization |
| title_fullStr | MPN-RRT*: A New Method in 3D Urban Path Planning for UAV Integrating Deep Learning and Sampling Optimization |
| title_full_unstemmed | MPN-RRT*: A New Method in 3D Urban Path Planning for UAV Integrating Deep Learning and Sampling Optimization |
| title_short | MPN-RRT*: A New Method in 3D Urban Path Planning for UAV Integrating Deep Learning and Sampling Optimization |
| title_sort | mpn rrt a new method in 3d urban path planning for uav integrating deep learning and sampling optimization |
| topic | deep learning path planning UAV MPNet RRT* MPN-RRT* |
| url | https://www.mdpi.com/1424-8220/25/13/4142 |
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