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Researchers Propose Novel Model to Better Understand Control Mechanism of Bio-syncretic Robotics

Jan 16, 2018

Bio-syncretic robotic is an emerging field in the past decade and is expected to resolve the problems such as low control degree of freedom and insufficient flexibility. The study on bio-syncretic robots mainly focuses on the movement realization of cells-actuated micro structures such as the simple change of movement speed and direction. Lacking theoretical model of biological actuators, bio-syncretic robots are faced with challenges on the movement control and dynamic analysis.

Recently, the micro and nano robotics research team from Shenyang Institute of Automation of Chinese Academy of Sciences developed a dynamic model to characterize contractile behaviors and mechanical properties of a cardiomyocyte. The results were published in Biophysical Journal.

The researchers developed a linear dynamic model of a single cardiomyocyte cell at the subcellular scale to characterize the contractile behaviors of heart cells. The mechanical dynamics model of a single cardiomyocyte is completed by modeling the subcellular structures by the corresponding mechanical and electronic components, such as springs, dampers and motors.

To verify the proposed model, they used Scanning Ion Conductance Microscopy (SICM) to measure the beating pattern of a living cell. According to the beating data, the system parameters can be identified based on the theoretical model, to describe the physical electromechanical characteristics of the subcellular structures of a single cardiomyocyte, such as the mass, viscosity, elasticity and action potential.

Taking advantage of the non-contact measurement mode of SICM and the living cells based dynamic model, the multiple lossless electromechanical characteristics (mass, viscosity, elasticity and action potential) of subcellular structures can be obtained in situ.

This study may lay the foundation for the research on dynamic optimization and control technology of bio-syncretic robots based on the biology. 

This work was supported by the National Natural Science Foundation of China, Chinese Academy of Sciences, and State Key Laboratory of Robotics.

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