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...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/12/3595 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849426012698312704 |
|---|---|
| author | Xiao Ji Peng Liu Meng Zhang Chengchun Zhang Shuang Yu Bing Qi Man Zhao |
| author_facet | Xiao Ji Peng Liu Meng Zhang Chengchun Zhang Shuang Yu Bing Qi Man Zhao |
| author_sort | Xiao Ji |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-334a0409cd9644d2ada24adc4444fcdb |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-334a0409cd9644d2ada24adc4444fcdb2025-08-20T03:29:35ZengMDPI AGSensors1424-82202025-06-012512359510.3390/s25123595Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning TechnologyXiao Ji0Peng Liu1Meng Zhang2Chengchun Zhang3Shuang Yu4Bing Qi5Man Zhao6China Satellite Network Digital Technology Co., Ltd., Xiong’an 070001, ChinaChina Satellite Network Digital Technology Co., Ltd., Xiong’an 070001, ChinaChina Satellite Network Digital Technology Co., Ltd., Xiong’an 070001, ChinaChina Satellite Network Digital Technology Co., Ltd., Xiong’an 070001, ChinaChina Satellite Network Digital Technology Co., Ltd., Xiong’an 070001, ChinaChina Satellite Network Digital Technology Co., Ltd., Xiong’an 070001, ChinaSchool of Computer, China University of Geosciences (Wuhan), Wuhan 430074, ChinaThis 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.https://www.mdpi.com/1424-8220/25/12/3595multi-source datapath planning algorithmsA* algorithm optimizationdeep reinforcement learningdynamic decision making |
| spellingShingle | Xiao Ji Peng Liu Meng Zhang Chengchun Zhang Shuang Yu Bing Qi Man Zhao Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology Sensors multi-source data path planning algorithms A* algorithm optimization deep reinforcement learning dynamic decision making |
| title | Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology |
| title_full | Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology |
| title_fullStr | Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology |
| title_full_unstemmed | Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology |
| title_short | Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology |
| title_sort | multi source data driven terrestrial multi algorithm fusion path planning technology |
| topic | multi-source data path planning algorithms A* algorithm optimization deep reinforcement learning dynamic decision making |
| url | https://www.mdpi.com/1424-8220/25/12/3595 |
| work_keys_str_mv | AT xiaoji multisourcedatadriventerrestrialmultialgorithmfusionpathplanningtechnology AT pengliu multisourcedatadriventerrestrialmultialgorithmfusionpathplanningtechnology AT mengzhang multisourcedatadriventerrestrialmultialgorithmfusionpathplanningtechnology AT chengchunzhang multisourcedatadriventerrestrialmultialgorithmfusionpathplanningtechnology AT shuangyu multisourcedatadriventerrestrialmultialgorithmfusionpathplanningtechnology AT bingqi multisourcedatadriventerrestrialmultialgorithmfusionpathplanningtechnology AT manzhao multisourcedatadriventerrestrialmultialgorithmfusionpathplanningtechnology |