Dynamic Path Planning for Unmanned Autonomous Vehicles Based on CAS-UNet and Graph Neural Networks
This paper proposes a deeply integrated model called CAS-GNN, aiming to solve the collaborative path-planning problem for multi-agent vehicles operating in dynamic environments. Our proposed model integrates CAS-UNet and Graph Neural Network (GNN), and, by introducing a dynamic edge enhancement modu...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/14/4283 |
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| author | Yuchu Ji Rentong Sun Yang Wang Zijian Zhu Zhenghao Liu |
| author_facet | Yuchu Ji Rentong Sun Yang Wang Zijian Zhu Zhenghao Liu |
| author_sort | Yuchu Ji |
| collection | DOAJ |
| description | This paper proposes a deeply integrated model called CAS-GNN, aiming to solve the collaborative path-planning problem for multi-agent vehicles operating in dynamic environments. Our proposed model integrates CAS-UNet and Graph Neural Network (GNN), and, by introducing a dynamic edge enhancement module and a dynamic edge weight update module, it improves the accuracy of obstacle boundary recognition in complex scenarios and adaptively changes the influence of different edges during the information transmission process. We generate data through online trajectory optimization to enhance the model’s adaptability to dynamic environments. Simulation results show that our proposed CAS-GNN model has good performance in path planning. In a dynamic scenario involving six vehicles, our model achieved a success rate of 92.8%, a collision rate of 0.0836%, and a trajectory efficiency of 64%. Compared with the traditional A-GNN model, our proposed CAS-GNN model improves the planning success rate by 2.7% and the trajectory efficiency by 8%, while reducing the collision rate by 23%. |
| format | Article |
| id | doaj-art-a58f5b868dab4af4a8ead4122a6797c8 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-a58f5b868dab4af4a8ead4122a6797c82025-08-20T02:47:22ZengMDPI AGSensors1424-82202025-07-012514428310.3390/s25144283Dynamic Path Planning for Unmanned Autonomous Vehicles Based on CAS-UNet and Graph Neural NetworksYuchu Ji0Rentong Sun1Yang Wang2Zijian Zhu3Zhenghao Liu4College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, ChinaCollege of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, ChinaCollege of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, ChinaCollege of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, ChinaCollege of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, ChinaThis paper proposes a deeply integrated model called CAS-GNN, aiming to solve the collaborative path-planning problem for multi-agent vehicles operating in dynamic environments. Our proposed model integrates CAS-UNet and Graph Neural Network (GNN), and, by introducing a dynamic edge enhancement module and a dynamic edge weight update module, it improves the accuracy of obstacle boundary recognition in complex scenarios and adaptively changes the influence of different edges during the information transmission process. We generate data through online trajectory optimization to enhance the model’s adaptability to dynamic environments. Simulation results show that our proposed CAS-GNN model has good performance in path planning. In a dynamic scenario involving six vehicles, our model achieved a success rate of 92.8%, a collision rate of 0.0836%, and a trajectory efficiency of 64%. Compared with the traditional A-GNN model, our proposed CAS-GNN model improves the planning success rate by 2.7% and the trajectory efficiency by 8%, while reducing the collision rate by 23%.https://www.mdpi.com/1424-8220/25/14/4283path planninggraph neural networkattention mechanismsmulti-unmanned autonomous vehicles |
| spellingShingle | Yuchu Ji Rentong Sun Yang Wang Zijian Zhu Zhenghao Liu Dynamic Path Planning for Unmanned Autonomous Vehicles Based on CAS-UNet and Graph Neural Networks Sensors path planning graph neural network attention mechanisms multi-unmanned autonomous vehicles |
| title | Dynamic Path Planning for Unmanned Autonomous Vehicles Based on CAS-UNet and Graph Neural Networks |
| title_full | Dynamic Path Planning for Unmanned Autonomous Vehicles Based on CAS-UNet and Graph Neural Networks |
| title_fullStr | Dynamic Path Planning for Unmanned Autonomous Vehicles Based on CAS-UNet and Graph Neural Networks |
| title_full_unstemmed | Dynamic Path Planning for Unmanned Autonomous Vehicles Based on CAS-UNet and Graph Neural Networks |
| title_short | Dynamic Path Planning for Unmanned Autonomous Vehicles Based on CAS-UNet and Graph Neural Networks |
| title_sort | dynamic path planning for unmanned autonomous vehicles based on cas unet and graph neural networks |
| topic | path planning graph neural network attention mechanisms multi-unmanned autonomous vehicles |
| url | https://www.mdpi.com/1424-8220/25/14/4283 |
| work_keys_str_mv | AT yuchuji dynamicpathplanningforunmannedautonomousvehiclesbasedoncasunetandgraphneuralnetworks AT rentongsun dynamicpathplanningforunmannedautonomousvehiclesbasedoncasunetandgraphneuralnetworks AT yangwang dynamicpathplanningforunmannedautonomousvehiclesbasedoncasunetandgraphneuralnetworks AT zijianzhu dynamicpathplanningforunmannedautonomousvehiclesbasedoncasunetandgraphneuralnetworks AT zhenghaoliu dynamicpathplanningforunmannedautonomousvehiclesbasedoncasunetandgraphneuralnetworks |