Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology
This paper presents a multi-source data-driven hybrid path planning framework that integrates global A* search with local Deep Q-Network (DQN) optimization to address complex terrestrial routing challenges. By fusing ASTER GDEM terrain data with OpenStreetMap (OSM) road networks, we construct a stan...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/12/3595 |
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| Summary: | This paper presents a multi-source data-driven hybrid path planning framework that integrates global A* search with local Deep Q-Network (DQN) optimization to address complex terrestrial routing challenges. By fusing ASTER GDEM terrain data with OpenStreetMap (OSM) road networks, we construct a standardized geospatial database encompassing elevation, traffic, and road attributes. A dynamic-heuristic A* algorithm is proposed, incorporating traffic signals and congestion penalties, and is enhanced by a DQN-based local decision module to improve adaptability to dynamic environments. Experimental results on a realistic urban dataset demonstrate that the proposed method achieves superior performance in risk avoidance, travel time reduction, and dynamic obstacle handling compared to traditional models. This study contributes a unified architecture that enhances planning robustness and lays the foundation for real-time applications in emergency response and smart logistics. |
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| ISSN: | 1424-8220 |