MPC-based trajectory tracking control of unmanned underwater tracked bulldozer considering track slipping and motion smoothing

Li, Yong; He, Dingchang; Ma, Fang; Liu, Pengfei; Liu, Yonggang

doi:10.1016/j.oceaneng.2023.114449

Cited by 8 publications

(1 citation statement)

References 31 publications

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“…Compared to other control algorithms, this method is simple and versatile. The commonly used path-tracking methods include path tracking based on kinematics [9,10], pure path tracking based on proportion integration differentiation (PID) [11,12], improved pure path tracking [9,[13][14][15][16], and Model Predictive Control (MPC) control [17][18][19], among others.…”

Section: Introductionmentioning

confidence: 99%

Path Planning and Control System Design of an Unmanned Weeding Robot

Yang,

Jin,

et al. 2023

Agriculture

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Aiming at the demand by unmanned farms for unmanned operation in the entire process of field management, an unmanned plant protection robot for field management was developed based on a platform comprising a traditional high-clearance spray rod sprayer, integrated unmanned driving technology, image recognition technology, intelligent control technology, and precision operation technology. According to the agricultural machinery operation mode, agricultural machinery path planning, linear path tracking, and header path tracking algorithms were developed. Based on the overall structure and working principle of the chassis, the robot control system, steering control system, and operation control system were set. Based on the YOLOv5 image recognition algorithm, the crop–weed recognition model was developed. After 6000 rounds of training, the accuracy, recall, and mean average precision of the model were 87.7%, 84.5%, and 79.3%, respectively. Finally, a field experiment was carried out with the unmanned plant protection robot equipped with a complete system. Results show that the average lateral error of the robot is 0.036 m, the maximum lateral error is 0.2 m, the average root mean square error is 0.053 m, the average velocity error is 0.034 m/s, and the average root mean square error of velocity is 0.045 m/s when the robot works in a straight line. In weeding operations, the area ratio of weedy zones to field is 25%, which saves 75% of the herbicide compared to that dispensed in full spraying mode. The unmanned plant protection robot designed in this study effectively achieves machinery’s autonomous operation, providing valuable insights for research in unmanned farming and autonomous agricultural machinery.

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