Authors

Abstract

To mitigate tracking degradation caused by unstable speeds in weeding robots, this study integrates Linear Active Disturbance Rejection Control (LADRC) with the Pure Pursuit (PP) algorithm. An Improved Northern Goshawk Optimization (INGO) algorithm is employed to optimize the LADRC parameters, enabling more precise speed regulation. Field experiments conducted at speeds of 0.5, 0.8, and 1.0 m/s compared the proposed approach with a conventional PID-PP controller. The results demonstrate that the proposed method reduced the maximum lateral tracking error by 9.67%, 19.0%, and 20.5%, respectively, while consistently improving both MAE and RMSE. These findings confirm that the proposed control strategy effectively enhances path tracking stability and precision, thereby improving the autonomous navigation performance of weeding robots.

Abstract in Chinese

针对除草机器人因作业速度不稳定影响纯追踪（PP）算法计算精度，进而导致路径跟踪性能下降的问题，本文提出了一种融合线性自抗扰控制（LADRC）与PP算法的路径跟踪算法。该方法旨在增强速度稳定性，从而减小横向和航向误差。在路径跟踪方面，采用了基于机器人结构分析并具有动态前视距离调整功能的PP控制器。同时，利用改进北方苍鹰优化（INGO）算法对LADRC参数进行寻优，以实现速度控制，显著提高了速度精度和抗干扰能力。仿真结果表明，INGO优化的LADRC-PP控制器在跟踪稳定性方面优于传统PID-PP控制器。在0.5、0.8和1.0 m/s速度下的田间试验进一步验证了该系统的有效性。与PID-PP算法相比，所提方法将最大横向误差分别降低了9.67%、19.0%和20.5%。平均绝对误差（MAE）和均方根误差（RMSE）也表现出了一致的降低。结果证实，LADRC-PP算法有效提高了自主除草作业的路径跟踪稳定性和精度。