TY - JOUR
T1 - An Anti-rollover Trajectory Planning Method for Unmanned Ground Vehicles Under Rough Terrain
AU - Yang, Chao
AU - Wang, Yansong
AU - Qie, Tianqi
AU - Wang, Weida
AU - Yang, Fan
AU - Zuo, Yinchu
N1 - Publisher Copyright:
© China Society of Automotive Engineers (China SAE) 2026.
PY - 2026
Y1 - 2026
N2 - Trajectory planning for unmanned ground vehicles (UGVs) involves creating a feasible path that includes a speed profile, essential for ensuring driving safety. Compared to structured roads, off-road environments frequently feature rough terrain, which possibly lead to significant rolling of UGVs, thereby compromising driving safety. To address this problem, an unequal probability sampling rapidly exploring random tree (UP-RRT*) method is proposed for UGVs to navigate rough terrains effectively. Initially, a novel flatness extraction method is introduced to refine the sampling process. This method adjusts the sampling range and probability by correlating the capability of UGVs with terrain characteristics, significantly enhancing the safety of autonomous driving in off-road environments and accelerating the convergence of the RRT* algorithm. Subsequently, a cost function designed for off-road environments is developed to optimize the structure of the random tree. Once the random tree extends to the designated goal point, it is smoothed using the cubic spline method to generate a preliminary trajectory. Lastly, the trajectory’s speed profile is dynamically adjusted by constraining the attitude of the UGVs in the prospective planned trajectory. The proposed method is verified by experiments in rough terrain with an actual vehicle. Results show that the absolute average value of the roll angle and pitch angle of the UP-RRT* method is decreased by 69.3% and 66.2% compared with the RRT* method. The average speed is increased by 43.7% and the computation time is reduced by 30.3% compared with the RRT* method. The proposed method significantly improves the comfort, driving efficiency of UGVs, and enhances the real-time performance of the trajectory planning.
AB - Trajectory planning for unmanned ground vehicles (UGVs) involves creating a feasible path that includes a speed profile, essential for ensuring driving safety. Compared to structured roads, off-road environments frequently feature rough terrain, which possibly lead to significant rolling of UGVs, thereby compromising driving safety. To address this problem, an unequal probability sampling rapidly exploring random tree (UP-RRT*) method is proposed for UGVs to navigate rough terrains effectively. Initially, a novel flatness extraction method is introduced to refine the sampling process. This method adjusts the sampling range and probability by correlating the capability of UGVs with terrain characteristics, significantly enhancing the safety of autonomous driving in off-road environments and accelerating the convergence of the RRT* algorithm. Subsequently, a cost function designed for off-road environments is developed to optimize the structure of the random tree. Once the random tree extends to the designated goal point, it is smoothed using the cubic spline method to generate a preliminary trajectory. Lastly, the trajectory’s speed profile is dynamically adjusted by constraining the attitude of the UGVs in the prospective planned trajectory. The proposed method is verified by experiments in rough terrain with an actual vehicle. Results show that the absolute average value of the roll angle and pitch angle of the UP-RRT* method is decreased by 69.3% and 66.2% compared with the RRT* method. The average speed is increased by 43.7% and the computation time is reduced by 30.3% compared with the RRT* method. The proposed method significantly improves the comfort, driving efficiency of UGVs, and enhances the real-time performance of the trajectory planning.
KW - Rapidly exploring random tree (RRT)
KW - Rough terrain
KW - Trajectory planning
KW - Unmanned ground vehicles (UGVs)
UR - https://www.scopus.com/pages/publications/105035812253
U2 - 10.1007/s42154-025-00414-3
DO - 10.1007/s42154-025-00414-3
M3 - Article
AN - SCOPUS:105035812253
SN - 2096-4250
JO - Automotive Innovation
JF - Automotive Innovation
ER -