An Efficient Autonomous Exploration Framework for Autonomous Vehicles in Uneven Off-Road Environments
Autonomous exploration of autonomous vehicles in off-road environments remains challenging due to the adverse impact on exploration efficiency and safety caused by uneven terrain. In this paper, we propose a path planning framework for autonomous exploration to obtain feasible and smooth paths for a...
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MDPI AG
2025-07-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/9/7/490 |
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| author | Le Wang Yao Qi Binbing He Youchun Xu |
| author_facet | Le Wang Yao Qi Binbing He Youchun Xu |
| author_sort | Le Wang |
| collection | DOAJ |
| description | Autonomous exploration of autonomous vehicles in off-road environments remains challenging due to the adverse impact on exploration efficiency and safety caused by uneven terrain. In this paper, we propose a path planning framework for autonomous exploration to obtain feasible and smooth paths for autonomous vehicles in 3D off-road environments. In our framework, we design a target selection strategy based on 3D terrain traversability analysis, and the traversability is evaluated by integrating vehicle dynamics with geometric indicators of the terrain. This strategy detects the frontiers within 3D environments and utilizes the traversability cost of frontiers as the pivotal weight within the clustering process, ensuring the accessibility of candidate points. Additionally, we introduced a more precise approach to evaluate navigation costs in off-road terrain. To obtain a smooth local path, we generate a cluster of local paths based on the global path and evaluate the optimal local path through the traversability and smoothness of the path. The method is validated in simulations and real-world environments based on representative off-road scenarios. The results demonstrate that our method reduces the exploration time by up to 36.52% and ensures the safety of the vehicle while exploring unknown 3D off-road terrain compared with state-of-the-art methods. |
| format | Article |
| id | doaj-art-681a631ff6d5437994546b98d07890cc |
| institution | DOAJ |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-681a631ff6d5437994546b98d07890cc2025-08-20T02:45:55ZengMDPI AGDrones2504-446X2025-07-019749010.3390/drones9070490An Efficient Autonomous Exploration Framework for Autonomous Vehicles in Uneven Off-Road EnvironmentsLe Wang0Yao Qi1Binbing He2Youchun Xu3Department of Vehicle Engineering, Army Military Transportation University, Tianjin 300161, ChinaInstitute of Military Transportation, Army Military Transportation University, Tianjin 300161, ChinaInstitute of Military Transportation, Army Military Transportation University, Tianjin 300161, ChinaInstitute of Military Transportation, Army Military Transportation University, Tianjin 300161, ChinaAutonomous exploration of autonomous vehicles in off-road environments remains challenging due to the adverse impact on exploration efficiency and safety caused by uneven terrain. In this paper, we propose a path planning framework for autonomous exploration to obtain feasible and smooth paths for autonomous vehicles in 3D off-road environments. In our framework, we design a target selection strategy based on 3D terrain traversability analysis, and the traversability is evaluated by integrating vehicle dynamics with geometric indicators of the terrain. This strategy detects the frontiers within 3D environments and utilizes the traversability cost of frontiers as the pivotal weight within the clustering process, ensuring the accessibility of candidate points. Additionally, we introduced a more precise approach to evaluate navigation costs in off-road terrain. To obtain a smooth local path, we generate a cluster of local paths based on the global path and evaluate the optimal local path through the traversability and smoothness of the path. The method is validated in simulations and real-world environments based on representative off-road scenarios. The results demonstrate that our method reduces the exploration time by up to 36.52% and ensures the safety of the vehicle while exploring unknown 3D off-road terrain compared with state-of-the-art methods.https://www.mdpi.com/2504-446X/9/7/490autonomous vehiclesautonomous explorationoff-road environmentspath planning |
| spellingShingle | Le Wang Yao Qi Binbing He Youchun Xu An Efficient Autonomous Exploration Framework for Autonomous Vehicles in Uneven Off-Road Environments Drones autonomous vehicles autonomous exploration off-road environments path planning |
| title | An Efficient Autonomous Exploration Framework for Autonomous Vehicles in Uneven Off-Road Environments |
| title_full | An Efficient Autonomous Exploration Framework for Autonomous Vehicles in Uneven Off-Road Environments |
| title_fullStr | An Efficient Autonomous Exploration Framework for Autonomous Vehicles in Uneven Off-Road Environments |
| title_full_unstemmed | An Efficient Autonomous Exploration Framework for Autonomous Vehicles in Uneven Off-Road Environments |
| title_short | An Efficient Autonomous Exploration Framework for Autonomous Vehicles in Uneven Off-Road Environments |
| title_sort | efficient autonomous exploration framework for autonomous vehicles in uneven off road environments |
| topic | autonomous vehicles autonomous exploration off-road environments path planning |
| url | https://www.mdpi.com/2504-446X/9/7/490 |
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