Data-driven actuator selection for artificial muscle-powered robots

Proc. IEEE International Conference on Robotics and Automation (ICRA), 2021

Taylor West Henderson, Yuheng Zhi, Angela Liu, Michael C Yip

Publisher Link: http://ieeexplore.ieee.org/abstract/document/9561050/

Abstract: Even though artificial muscles have gained popularity due to their compliant, flexible and compact properties, there currently does not exist an easy way of making informed decisions on the appropriate actuation strategy when designing a muscle-powered robot; thus limiting the transition of such technologies into broader applications. What’s more, when a new muscle actuation technology is developed, it is difficult to compare it against existing robot muscles. To accelerate the development of artificial muscle applications, we propose a data-driven approach for robot muscle actuator selection using Support Vector Machines (SVM). This first-of-its-kind method gives users insight into which actuators fit their specific needs and actuation performance criteria, making it possible for researchers and engineers with little to no prior knowledge of artificial muscles to focus on application design. It also provides a platform to …

Henderson et al. (2021) Data-driven actuator selection for artificial muscle-powered robots, Proc. IEEE International Conference on Robotics and Automation (ICRA), pp. 1783-1789.