A hybrid dynamic model for the AMBIDEX tendon-driven manipulator

Abstract

Tendon-driven actuation allows for light and compact manipulator designs with enhanced safety features. One of the key challenges in model-based control of tendon-driven robots is the increased complexity of the dynamic model, due in large part to difficult-to-model behavior like nonlinear dynamic deformation and friction at the tendons. While purely data-driven modeling approaches, e.g., neural networks, free one from dealing with complex and often error-prone mechanics models, they usually do not generalize well to diverse tasks, and also do not offer the needed intuitive understanding or predictive power of traditional mechanics-based models. In this paper, we present a hybrid modeling approach for complex tendon-driven robots, which effectively complement the limitations of pure physics-based and data-driven learning-based approaches. Rigid multibody equations of motion are augmented with (i) a configuration-dependent viscous-Coulomb friction model and (ii) a recurrent neural network that captures the tendon dynamics, and estimates link joint angles from the motor positions, velocities, and torques. Experiments involving a two-dof tendon-driven parallel wrist mechanism and the 7-dof AMBIDEX tendon-driven manipulator validate the performance advantages of our hybrid model-based control framework.