Abstract-In the long history of robotics research, the most prominent problem has always been, to develop robots that can safely operate in human-centered environments. One way towards the goal of a safe, and human-friendly robot, is to incorporate more and more of the flexibility that can be found in humans, by mimicking the internal mechanisms. In this work we propose a scalable joint-space control scheme based on computed torque control for an anthropomimetic robot. To achieve this, the dynamic system model of the robot is decomposed into hierarchical subsystems, using scalable modeling algorithms where possible. Machine learning techniques were employed to tackle the problem of muscle force to joint torque mapping.The developed control scheme has been evaluated using the highly refined simulation of an anthropomimetic robot arm featuring 11 muscles, a revolute elbow joint and a spherical shoulder joint. We show trajectory tracking based on a lowlevel muscle and a high-level joint control scheme, taking into account the coupling between the joints due to inertial reactions and bi-articular muscles.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.