| 제목 |
Research on Path Tracking Control Based on Optimal Look-Ahead Points |
| 분야 |
Engine and Emissions |
| 언어 |
English |
| 저자 |
Yong Guan(School of Electrical and Automation Engineering , East China Jiaotong University), Ning Li(School of Intelligent Manufacturing , Taizhou University), Pengzhan Chen(School of Intelligent Manufacturing , Taizhou University), Yongchao Zhang(School of Intelligent Manufacturing , Taizhou University) |
| Key Words |
Path Tracking, Pure Pursuit Algorithm, Longitudinal Look-Ahead Distance, Optimal Look-Ahead Point, DDPG, Automotive Engineering |
| 초록 |
Pure pursuit tracking algorithms are a popular control method in the field of autonomous navigation, where the selection of a look-ahead point plays a crucial role in tracking performance. However, the computation of the look-ahead point involves issues that are challenging to describe precisely using mathematics. To enhance the tracking precision of vehicles on curved trajectories, we propose an improved optimal look-ahead point path tracking algorithm. This algorithm primarily seeks the optimal look-ahead point by considering both longitudinal look-ahead distance and lateral position offset. To begin, we employ the Deep Deterministic Policy Gradient (DDPG) algorithm to train vehicles to determine the optimal longitudinal look-ahead distance under various constant curvature and velocity conditions. Subsequently, by utilizing the optimal longitudinal look-ahead distance and the front-wheel steering angle, we construct a lateral deviation search region. Finally, we use an evaluation function to search for the optimal look-ahead point within this region. Simulation tests demonstrate that the proposed algorithm significantly improves tracking accuracy under varying curvature trajectory conditions. |
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