| A Novel Trajectory Planning Strategy for Autonomous Ground Vehicles Using Tangent-based Artificial Potential Field |
| Xianjian Jin2, Jianning Lu1, Yinchen Tao1, Jianbo Lv1, Nonsly Valerienne1, Opinat Ikiela1, Guodong Yin3 |
1School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200072, China
2Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai, 200072, China
3School of Mechanical Engineering, Southeast University, Nanjing, 211189, China |
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Received: August 1, 2025; Revised: August 1, 2025 Accepted: September 16, 2025. Published online: November 10, 2025. |
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| ABSTRACT |
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Nowadays, autonomous ground vehicles (AGVs) transform urban transport, logistics, and field operations, and they occupy a strategic core role in autonomous driving commercialization. Although the artificial potential field method (APF) has made some achievements in trajectory planning of AGVs, it is obviously inappropriate to directly apply traditional methods to dynamic traffic scenarios. Vehicle dynamics environment, road regulations, and local minimum problems of artificial potential field methods should also be considered. Therefore, this paper introduces an efficient method for trajectory planning of autonomous ground vehicles using tangent-based artificial potential field (TAPF), which aims to address road dynamic obstacles and effectively resolve the local minimum problem. First, a structured potential field model is constructed, including the target gravitational field, obstacle repulsion field, and road boundary repulsion field. Additionally, in the trajectory generation process, the negative gradient method is employed to calculate the global potential field to generate a virtual resultant force to guide the vehicle’s movement. In the algorithm process, when the navigation line is detected to intersect with the obstacle safety circle, the candidate tangent points on the obstacle circle are generated and comprehensively considered, including geometric distance, historical path efficiency, and smoothness, and a tangential force vector is dynamically synthesized based on this. The tangential force integrates into the vehicle force analysis, directly breaks the force balance at local minima, guides the vehicle to bypass obstacles efficiently. Finally, simulation results across diverse traffic scenarios show that average lateral accelerations and max curvatures of planning trajectory with the proposed TAPF method are much smaller than those of classical APF by approximately 33.5% and 33.2%, while the increase of success rates for planning trajectory is about 40% compared with classical APF, which confirms that developed TAPF method can achieve real-time smooth, collision-free trajectory under high sampling frequency with 50 Hz. |
| Key Words:
Autonomous ground vehicles · Vehicle dynamics system · Motion planning · Trajectory planning · Artificial potential field |
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Corresponding Author.
Xianjian Jin , Email. 
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