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Volume 7 Issue 3
Apr.  2020

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2020  >  7(3): 745-751
Dong Zhang, Hao Yuan and Zhengcai Cao, "Environmental Adaptive Control of a Snake-like Robot With Variable Stiffness Actuators," IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 745-751, May 2020. doi: 10.1109/JAS.2020.1003144
Citation: Dong Zhang, Hao Yuan and Zhengcai Cao, "Environmental Adaptive Control of a Snake-like Robot With Variable Stiffness Actuators," IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 745-751, May 2020. doi: 10.1109/JAS.2020.1003144
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Environmental Adaptive Control of a Snake-like Robot With Variable Stiffness Actuators

doi: 10.1109/JAS.2020.1003144
Funds:  This work was supported by the National Natural Science Foundation of China (51575034), Beijing Leading Talents Program (Z191100006119031), Beijing Municipal Natural Science Foundation (3202022), National Key Research and Development Program of China (2018YFB1304600), and the State Key Laboratory of Robotics of China (2018-O15)
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    Abstract
  • This work investigates adaptive stiffness control and motion optimization of a snake-like robot with variable stiffness actuators. The robot can vary its stiffness by controlling magneto-rheological fluid (MRF) around actuators. In order to improve the robot’s physical stability in complex environments, this work proposes an adaptive stiffness control strategy. This strategy is also useful for the robot to avoid disturbing caused by emergency situations such as collisions. In addition, to obtain optimal stiffness and reduce energy consumption, both torques of actuators and stiffness of the MRF braker are considered and optimized by using an evolutionary optimization algorithm. Simulations and experiments are conducted to verify the proposed adaptive stiffness control and optimization methods for a variable stiffness snake-like robots.

     

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    Highlights

    • A snake-like robot with variable stiffness actuators is designed. The robot can vary its stiffness by controlling magnetorheological fluid around its actuators.
    • An adaptive stiffness control strategy is proposed to improve the robot’s physical stability in complex environments. This strategy is also useful for the robot to avoid disturbing caused by emergency situations such as collisions.
    • Torques of actuators and stiffness of the magnetorheological fluid braker are considered and optimized by using an evolutionary optimization algorithm to obtain optimal stiffness and reduce energy consumption.

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