Self-tuning Look-ahead Distance of Pure-pursuit Path-following Control for Autonomous Vehicles Using an Automated Curve Information Extraction Method

Ibrahim, Azza Elsayed Bayoumi

doi:10.1007/s13177-022-00319-z

Cited by 7 publications

(2 citation statements)

References 22 publications

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“…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]. If the forwardlooking distance is kept large, the response becomes slower and may even see larger corner cuts in some cases, such as taking shortcuts around path corners, thus reducing tracking quality and safety [36], as shown in Fig.…”

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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“…PID control is simple and effective, but due to the delay between the sensor and the actuator, the PID controller can only get the position deviation of the previous moment, so there is an unavoidable delay in the system, and this error is not negligible when the vehicle speed is fast, so the PID algorithm is poorly adaptable and vulnerable to the change of the road environment, and each parameter is difficult to adjust and cannot meet the requirements of path tracking under high-speed conditions, so In practical applications, it is generally used in conjunction with other control algorithms. PP algorithm is a more reliable path tracking control algorithm, Figure 3 shows the geometric relationship schematic diagram of PP algorithm, its principle is to control the vehicle turning radius R, so that the vehicle rear axle center point along the arc to reach the reference path target point (gx, gy) with forward-looking distance l, and then based on Ackermann steering model to calculate the required front wheel turning angle δ for control [29]. This control method has simple control and better robustness, even if large lateral errors and curvature changes occur in the tracking process still achieve better tracking results [30].…”

Section: Pid Algorithmmentioning

confidence: 99%

A Review of Autonomous Vehicle Path Tracking Algorithm Research

CHEN

Zheng

2022

Preprint

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Driverless technology aims to improve driving safety, accuracy and comfort. Path tracking is a basic component of the motion control module of autonomous vehicles, and its control algorithm directly affects the path tracking effect. Based on the preliminary results of the application of path tracking control algorithm, this paper analyzes the principles, advantages and disadvantages, applications and current research progress of the path tracking algorithm under different working conditions from the perspective of different working conditions at low speed and high speed, and provides an outlook on the future development, aiming to provide reference for future in-depth research.

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