Abstract
In this paper, an admittance controller based on a gray prediction model is designed for end-effector gripping force. The gray prediction model is used to predict environmental parameters in real-time and dynamically adjusts the reference position to reduce the steady-state force error. In this way, the dynamic response capability of impedance control can be improved, and its steady-state force error is also reduced. To this end, the designed method can grasp soft objects with unknown characteristics. The algorithm is validated by simulation experiments, which provide a theoretical basis for flexible fruit grasping.
This work is supported by the projects of National Natural Science Foundation of China (No.61873192), the Innovative Projects (No. 2021-JCJQ-LB-010-11), the Shanghai 2021 “Science and Technology Innovation Action Plan” with Special Project of Biomedical Science and Technology Support (No.21S31902800), and the Key Pre-Research Project of the 14th-Five-Year-Plan on Common Technology. Meanwhile, this work is also partially supported by the Fundamental Research Funds for the Central Universities and the “National High Level Overseas Talent Plan” project, the “National Major Talent Plan” project (No. 2022-JCJQ-XXX-079), as well as one key project (No. XM2023CX4013). It is also partially sponsored by the fundamental research project (No. JCKY2022XXXC133), the Shanghai Industrial Collaborative Innovation Project (Industrial Development Category, No. HCXBCY-2022-051), Laboratory fund of Wuhan Digital Engineering Institute of CSSC, the project of Shanghai Key Laboratory of Spacecraft Mechanism (No. 18DZ2272200), as well as the project of Space Structure and Mechanism Technology Laboratory of China Aerospace Science and Technology Group Co. Ltd (No. YY-F805202210015). All these supports are highly appreciated.
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Tang, Q. et al. (2023). Grasp Compliant Control Using Adaptive Admittance Control Methods for Flexible Objects. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14267. Springer, Singapore. https://doi.org/10.1007/978-981-99-6483-3_44
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