An autonomous driving controller with heading adaptive calibration for agricultural robots

Abstract

Purpose This paper aims to propose a new autonomous driving controller to calibrate the absolute heading adaptively. Besides, the second purpose of this paper is to propose a new angle-track loop with a mass regulator to improve the adaptability of the autonomous driving system under different loads and road conditions. Design/methodology/approach In this paper, the error model of heading is built and a new autonomous driving controller with heading adaptive calibration is designed. The new controller calculates the average lateral error by the self-adjusting interval window and calibrates the absolute heading through the incremental proportional–integral–derivative (PID) controller. A window-size adjustment strategy, based on the current lateral error and the derivative of lateral error, is proposed to improve both the transient and the steady-state responses. An angle-tracking loop with mass regulator is proposed to improve the adaptability of autonomous steering system under different loads and road conditions. Findings The experiment results demonstrate that this method can compensate the heading installation error and restrain the off-track error from 13.8 to 1.30 cm. The standard error of new controller is smaller than fuzzy-PID calibration controller and the accuracy of autonomous driving system is improved. Originality/value The accuracy of heading calibrated by the new controller is not affected by external factors and the efficiency of calibration is improved. As the model parameters of steering system can be obtained manually, the new autonomous steering controller has more simple structure and is easy to implement. Mass regulator is adjusted according to the road conditions and the mass of harvester, which can improve the system adaptability.

Adaptive neural network-based path tracking control for autonomous combine harvester with input saturation

“…In the wheat field, the max absolute error, mean absolute error, and the standard deviation of the proposed controller are 0.0390 m, 0.0143 m and 0.0160 m, which are reduced by 33.90%, 29.43% and 33.47% with contrast to the conventional backstepping controller, respectively. In addition, Qiao et al (2020b) has evaluated the control strategy based on the pure pursuit algorithm for harvesting wheat, the standard deviation of the traditional pure pursuit algorithm is 0.0236 m, which is slightly less than our conventional backstepping control. Furthermore, Qiao has improved the control strategy, achieving better accuracy.…”