Perpendicular Parking Path Generation and Optimal Path Tracking Algorithm for Auto-parking of Trailers

Shin, Heeseok; Kim, Myeong-Jun; Baek, Sun-Woo; Crane, Carl D.; Kim, Jungha

doi:10.1007/s12555-021-0268-9

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…As can be seen from Eq. 5, the Pure Pursuit algorithm has only one parameter which is the forward-looking distance (L d ) need to be adjusted [34]. The forward-looking distance is like a proportional gain factor.If the forward-looking distance is kept small, the robot tends to track the trajectory more accurately and the steering angle changes rapidly, but may cause the heading angle to oscillate [35].…”

Section: A Ackermann Geometry Modelmentioning

confidence: 99%

PP-ST: An Indoor Mobile Robot Path Tracking Algorithm

Cao,

Zhao,

Zhu

et al. 2023

IEEE Access

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Aiming at the problems that Pure Pursuit algorithm is too slow to approach the target path and take shortcuts at the corners under long forward-looking distance, and large heading jitter at the corners of the target path under short forward-looking distance, this paper proposes a path tracking algorithm based on Pure Pursuit and Stanley. First, a new nearest point selection strategy is proposed according to the need of algorithm fusion. Then, new algorithm coefficients are designed in order to adapt the algorithm to indoor environment. Finally, a new fusion method is proposed to solve the above problems. In order to verify the effectiveness of the proposed algorithm, it was tested in three different paths and compared with the improved Pure Pursuit algorithm proposed by two other related researchers in 2023. The experimental results show that the proposed fusion algorithm is 62.8% faster than the improved Pure Pursuit algorithm proposed by Steve Macenski et al. in approaching the target path in indoor environment, and the proposed fusion algorithm's distance error is 40% less than that of the improved Pure Pursuit algorithm proposed by Steve Macenski et al. in indoor environment; the proposed fusion algorithm's average heading angle error at the corners of the target path is 20.3% less than that of the improved Pure Pursuit algorithm proposed by Wu, Jiangdong et al. in indoor environment, and the proposed fusion algorithm's maximum heading angle error is 30.9% less than that of the improved Pure Pursuit algorithm proposed by Wu, Jiangdong et al. in indoor environment. Overall, the fusion algorithm is more suitable than the other two algorithms for mobile robot path tracking in indoor environments.

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Section: A Ackermann Geometry Modelmentioning

confidence: 99%

PP-ST: An Indoor Mobile Robot Path Tracking Algorithm

Cao,

Zhao,

Zhu

et al. 2023

IEEE Access

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Investigating Reversing Motion of Truck-Semitrailer Along Clothoid Curve

Mihályi,

Takács

2024

Lecture Notes in Mechanical Engineering

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The focus of the research is on the most common transport vehicle, the truck-semitrailer combination. A single-track kinematic model is used, supplemented by the dynamics of the steering system, to design a linear state feedback controller for the low-speed path-following problem in reverse. Despite the significant time delay considered in our study, the linear stability of the system is ensured, even for complicated maneuvers. The advantages of the adaptive gain tuning method are presented based on simulations of the commonly used 90-degree alley dock.

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