An improved pure pursuit path tracking control method based on heading error rate

Abstract

Purpose In path tracking, pure pursuit (PP) has great superiority due to its simple control. However, when in agricultural applications, the performance and accuracy of PP are not so well; it cannot be tracked in time has slow convergence, and low tracking accuracy. Furthermore, in some severe driving scenarios, PP is insufficient to convey the effects of the tracking error. This paper aims to propose an autonomous driving controller to improve the PP model based on heading error rate (Improved PP-improved search strategy ant colony optimization [ISSACO]). Design/methodology/approach First, the heading error rate is added as the control method in the PP model. Second, the predicted heading error was selected as the objective function; the ISSACO is used to obtain the minimum value of the predicted heading error. A PP controller is integrated with the heading error rate by ISSACO to better deal with tracking error by trading off between PP and heading error rate. Third, the ISSACO was used to obtain the optimal values of PP and heading error rate weight. Finally, the error feedback adaptive dynamic adjustment of the improved algorithm is realized to reduce the convergence time and tracking error. Findings The proposed method was tested on a four-wheeled vehicle robot, and the effectiveness of its convergence was proved. Experiments show that the proposed method can effectively reduce the tracking error, increase convergence, then improve the robot’s working quality. Originality/value An adaptive improved PP path tracking control is proposed, which considers both heading error rate and parameter uncertainties. The new autonomous controller has a simple structure and is easy to implement. It can be adjusted according to the path tracking status to improve the adaptability of the system.

Advancements in Learning-Based Navigation Systems for Robotic Applications in MRO Hangar: Review

“…The combination of adaptive control systems, sensor technologies [ 76 ], and advanced deep learning techniques have been shown to enhance robust real-time path tracking capability for robot navigation in such scenarios. From the study in [ 77 ], the most applied path tracking algorithms include pure pursuit (PP) [ 78 ], model predictive control (MPC) [ 79 , 80 ], as well as learning-based models to generate control laws leveraging training data and experience from a variety of scenarios [ 81 ].…”

A Review of Autonomous Vehicle Path Tracking Algorithm Research

“…The PP algorithm or the improved PP algorithm also has more applications in the path tracking control of parking and farm scenarios. For example, Yu et al implemented path tracking in a parking scenario based on the PP algorithm, and the results showed that the algorithm met the requirements of smoothness, ride comfort, and safety through the continuous transformation of the steering wheel angle [33].Zhang et al designed a path tracking algorithm for autonomous navigation of agricultural machines using PP and fuzzy control algorithms [34], and Wang et al designed a PP model for agricultural applications by adding the heading error rate PP model for agricultural applications, and both showed good tracking accuracy and convergence of the improved algorithm through testing [35].…”

Adaptive Skid-Steering Control Approach for Robots on Uncertain Inclined Planes with Redundant Load-Bearing Mobility

“…Geometric-based controllers guide the robot along a desired path by considering geometric relationships between its position, orientation, and reference trajectory. Examples include PID [14], pure pursuit [15], Stanley controller [16], and follow-the-carrot [17]. Modelbased controllers rely on accurate mathematical models of robots to predict the response corresponding to control inputs and disturbances and generate appropriate control actions.…”