Data-driven Actuator Selection for Artificial Muscle-Powered Robots

TL;DR

This paper introduces a novel data-driven method using Support Vector Machines to assist in selecting suitable artificial muscle actuators for robots, enabling easier comparison and benchmarking of different technologies.

Contribution

It presents the first SVM-based approach for actuator selection in artificial muscle robots, facilitating informed decision-making and benchmarking of new and existing actuators.

Findings

Method successfully predicts suitable actuators for specific needs.

Provides a web-based platform for actuator data and model access.

Enables continuous improvement through community data contributions.

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 gives users insight into which actuators fit their specific needs and actuation performance criteria, making it possible for researchers and engineer with little to no prior knowledge of artificial muscles to focus on application design. It also provides a platform to benchmark existing, new, or yet-to-be-discovered artificial muscle technologies. We test our method on unseen existing robot muscle designs to prove its usability on real-world applications. We provide an open-access, web-searchable interface for easy access to our models that will additionally allow for continuous contribution of new actuator data from groups around the world to enhance and expand these models.