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Volume 25
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10.3390/s25123595
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology

by
Xiao Ji
1,
Peng Liu
1,
Meng Zhang
1,*,
Chengchun Zhang
1,
Shuang Yu
1,
Bing Qi
1 and
Man Zhao
2,*
1
China Satellite Network Digital Technology Co., Ltd., Xiong’an 070001, China
2
School of Computer, China University of Geosciences (Wuhan), Wuhan 430074, China
*
Authors to whom correspondence should be addressed.
Sensors 2025, 25(12), 3595; https://doi.org/10.3390/s25123595 (registering DOI)
Submission received: 23 April 2025 / Revised: 5 June 2025 / Accepted: 5 June 2025 / Published: 7 June 2025
(This article belongs to the Special Issue Industrial Soft-Sensing Technology Based on Data-Driven and Artificial Intelligence Technologies)
Versions Notes

Abstract

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.
Keywords: multi-source data; path planning algorithms; A* algorithm optimization; deep reinforcement learning; dynamic decision making multi-source data; path planning algorithms; A* algorithm optimization; deep reinforcement learning; dynamic decision making

Share and Cite

MDPI and ACS Style

Ji, X.; Liu, P.; Zhang, M.; Zhang, C.; Yu, S.; Qi, B.; Zhao, M. Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology. Sensors 2025, 25, 3595. https://doi.org/10.3390/s25123595

AMA Style

Ji X, Liu P, Zhang M, Zhang C, Yu S, Qi B, Zhao M. Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology. Sensors. 2025; 25(12):3595. https://doi.org/10.3390/s25123595

Chicago/Turabian Style

Ji, Xiao, Peng Liu, Meng Zhang, Chengchun Zhang, Shuang Yu, Bing Qi, and Man Zhao. 2025. "Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology" Sensors 25, no. 12: 3595. https://doi.org/10.3390/s25123595

APA Style

Ji, X., Liu, P., Zhang, M., Zhang, C., Yu, S., Qi, B., & Zhao, M. (2025). Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology. Sensors, 25(12), 3595. https://doi.org/10.3390/s25123595

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