A Tensegrity-Inspired Compliant 3-DOF Compliant Joint @ICRA-cg8kk

Uploaded May 2018 | Updated September 2026, 2 weeks ago

ICRA 2018 Spotlight Video
Interactive Session Wed AM Pod K.8
Authors: Friesen, Jeffrey Michael; Dean, John; Bewley, Thomas; SunSpiral, Vytas
Title: A Tensegrity-Inspired Compliant 3-DOF Compliant Joint

Abstract:
Our Tensegrity-Inspired Compliant Three degree-of-freedom (DOF) robotic joint adds omnidirectional compliance to robotic limbs while reducing sprung mass through base mounted actuation. This enables a robotic limb which is safer to operate alongside humans and fragile equipment while still capable of generating quick movements and large forces if required. Unlike many other soft robotic systems which leverage continuously soft materials, our joint is straightforward to model with low order dynamic models and has a host of embedded sensing which provide ample information of its position and velocity. We first discuss geometry selection and optimization to maximize the theoretical configuration space of the joint. We then show several of our mechatronic design solutions, which are easily generalized to a multitude of cable-driven mechanisms, and demonstrate the performance of these mechanisms within the context of our hardware prototype. We then present results on the controllable stiffness of our physical prototype. Finally, we demonstrate the strength of our prototype which is capable of lifting a 7 kg mass at a distance of 0.95 meters from the joint.

A Tensegrity-Inspired Compliant 3-DOF Compliant Joint

$Enhancing Underwater Imagery Using Generative Adversarial Networks ICRA 2018 Spotlight Video Interactive Session Thu PM Pod J.7 Authors: Fabbri, Cameron; Islam, Md Jahidul; Sattar, Junaed Title: Enhancing Underwater Imagery Using Generative Adversarial Networks Abstract: Autonomous underwater vehicles (AUVs) rely on a variety of sensors acoustic, inertial and visual for intelligent decision making. Due to its non-intrusive, passive nature, and high information content, vision is an attractive sensing modality, particularly at shallower depths. However, factors such as light refraction and absorption, suspended particles in the water, and color distortion affect the quality of visual data, resulting in noisy and distorted images. AUVs that rely on visual sensing thus face difficult challenges, and consequently exhibit poor performance on vision-driven tasks. This paper proposes a method to improve the quality of visual underwater scenes using Generative Adversarial Networks (GANs), with the goal of improving input to vision-driven behaviors further down the autonomy pipeline. Furthermore, we show how recently proposed methods are able to generate a dataset for the purpose of such underwater image restoration. For any visually-guided underwater robots, this improvement can result in increased safety and reliability through robust visual perception. To that effect, we present quantitative and qualitative data which demonstrates that images corrected through the proposed approach generate more visually appealing images, and also provide increased accuracy for a diver tracking algorithm.$

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