A Voronoi–A* Fusion Algorithm with Adaptive Layering for Efficient UAV Path Planning in Complex Terrain
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| הוצא לאור ב: | Drones vol. 9, no. 8 (2025), p. 542-568 |
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| מחברים אחרים: | , , , |
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MDPI AG
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| גישה מקוונת: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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|---|---|---|---|
| 001 | 3244009940 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2504-446X | ||
| 024 | 7 | |a 10.3390/drones9080542 |2 doi | |
| 035 | |a 3244009940 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Dong Boyu |u School of Electronics and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China | |
| 245 | 1 | |a A Voronoi–A* Fusion Algorithm with Adaptive Layering for Efficient UAV Path Planning in Complex Terrain | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a <sec sec-type="highlights"> This paper proposes a Voronoi–A* fusion algorithm for UAV path planning in complex terrain. A DEM layering method based on obstacle density is introduced to decompose complex 3D terrain into multiple horizontal flight planes, significantly reducing computational complexity while maintaining 3D flexibility. Within each plane, a greedy Voronoi algorithm constructs a sparse vertex network as path nodes. A weighting function incorporates start/goal proximity, heading consistency, and altitude safety. The algorithm employs greedy heuristics to prioritize high-weight vertices, enabling the rapid generation of safe paths. What are the main findings? <list list-type="bullet"> <list-item> </list-item>A DEM layering method based on obstacle density. <list-item> A greedy Voronoi algorithm applied within each flight plane. </list-item> What is the implication of the main finding? <list list-type="bullet"> <list-item> </list-item>Computational complexity is minimized, and efficiency is enhanced. <list-item> Generated paths closely approximate the optimal path. </list-item> Unmanned Aerial Vehicles (UAVs) face significant challenges in global path planning within complex terrains, as traditional algorithms (e.g., A*, PSO, APF) struggle to balance computational efficiency, path optimality, and safety. This study proposes a Voronoi–A* fusion algorithm, combining Voronoi-vertex-based rapid trajectory generation with A* supplementary expansion for enhanced performance. First, an adaptive DEM layering strategy divides the terrain into horizontal planes based on obstacle density, reducing computational complexity while preserving 3D flexibility. The Voronoi vertices within each layer serve as a sparse waypoint network, with greedy heuristic prioritizing vertices that ensure safety margins, directional coherence, and goal proximity. For unresolved segments, A* performs localized searches to ensure complete connectivity. Finally, a line-segment interpolation search further optimizes the path to minimize both length and turning maneuvers. Simulations in mountainous environments demonstrate superior performance over traditional methods in terms of path planning success rates, path optimality, and computation. Our framework excels in real-time scenarios, such as disaster rescue and logistics, although it assumes static environments and trades slight path elongation for robustness. Future research should integrate dynamic obstacle avoidance and weather impact analysis to enhance adaptability in real-world conditions. | |
| 653 | |a Apexes | ||
| 653 | |a Adaptability | ||
| 653 | |a Optimization | ||
| 653 | |a Greedy algorithms | ||
| 653 | |a Safety margins | ||
| 653 | |a Horizontal flight | ||
| 653 | |a Layering | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Systems stability | ||
| 653 | |a Heuristic | ||
| 653 | |a Density | ||
| 653 | |a Energy consumption | ||
| 653 | |a Efficiency | ||
| 653 | |a Adaptive algorithms | ||
| 653 | |a Performance enhancement | ||
| 653 | |a Digital Elevation Models | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Planes | ||
| 653 | |a Impact analysis | ||
| 653 | |a Flexibility | ||
| 653 | |a Methods | ||
| 653 | |a Weighting functions | ||
| 653 | |a Complexity | ||
| 653 | |a Real time | ||
| 653 | |a Segments | ||
| 653 | |a Path planning | ||
| 653 | |a Obstacle avoidance | ||
| 653 | |a Terrain | ||
| 700 | 1 | |a Zhang, Gong |u School of Electronics and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China | |
| 700 | 1 | |a Yang, Yan |u AVIC Aviation Electronics Co., Ltd., Beijing 100081, China | |
| 700 | 1 | |a Yuan Peiyuan |u AVIC Aviation Electronics Co., Ltd., Beijing 100081, China | |
| 700 | 1 | |a Lu Shuntong |u AVIC Aviation Electronics Co., Ltd., Beijing 100081, China | |
| 773 | 0 | |t Drones |g vol. 9, no. 8 (2025), p. 542-568 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3244009940/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3244009940/fulltextwithgraphics/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3244009940/fulltextPDF/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |