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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 25; H. 12; S. 3595
Hauptverfasser: Ji, Xiao, Liu, Peng, Zhang, Meng, Zhang, Chengchun, Yu, Shuang, Qi, Bing, Zhao, Man
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 07.06.2025
MDPI
Schlagworte:
A algorithm optimization
Algorithms
Analysis
Collaboration
Data processing
Decision making
Deep learning
deep reinforcement learning
dynamic decision making
Efficiency
Energy consumption
Heuristic
multi-source data
path planning algorithms
Planning
Questions and answers
Route optimization
Traffic congestion
Traffic control
Traffic flow
Travel
Travelers
multi-source data
dynamic decision making
deep reinforcement learning
A algorithm optimization
path planning algorithms
ISSN:1424-8220, 1424-8220
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1424-8220
1424-8220
DOI:10.3390/s25123595