Modeling and Control of General Hydraulic Excavator for Human-in-the-loop Automation

Guangda Chen, Yinghao Gan, Jiayi Chen, Shuanwu Shi, Wei Chen,Yingfeng Chen,Rong Xiong,Changjie Fan

2023 IEEE 35TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, ICTAI(2023)

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摘要
As labor shortages and safety regulations become more prominent, the need for human-in-the-loop automation of excavators is increasing. To meet this demand, we have developed a comprehensive modeling method for the excavator arm using nonlinear optimization approaches, including a simplified model that maps the task space to the joint space, as well as an equivalent model that maps the joint space to the actuator space. These models were then used to build a feedforward-PID joint velocity controller and a joint trajectory controller combined with position feedback, which forms the core of our proposed semi-automatic control system for the excavator arm. Our deployment scheme is simple and efficient, and has been deployed on two excavators of different makes and sizes. Experiments show that our deployment scheme performs well on both excavators, with an average error of 0.05 rad/s for the velocity controller and less than 5 cm for the trajectory controller. Using our semi-automatic system, we have completed demonstration experiments for precise digging and grading operations. A demonstration video can be found at https://youtu.be/N6I0WZGSF68.
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