A Bio-Inspired Tensegrity Manipulator with Multi-DOF, Structurally Compliant Joints

Most traditional robotic mechanisms feature inelastic joints that are unable to robustly handle large deformations and off-axis moments. As a result, the applied loads are transferred rigidly throughout the entire structure. The disadvantage of this approach is that the exerted leverage is magnified at each subsequent joint possibly damaging the mechanism. In this paper, we present two lightweight, elastic, bio-inspired tensegrity robotics arms which mitigate this danger while improving their mechanism's functionality. Our solutions feature modular tensegrity structures that function similarly to the human elbow and the human shoulder when connected. Like their biological counterparts, the proposed robotic joints are flexible and comply with unanticipated forces. Both proposed structures have multiple passive degrees of freedom and four active degrees of freedom (two from the shoulder and two from the elbow). The structural advantages demonstrated by the joints in these manipulators illustrate a solution to the fundamental issue of elegantly handling off-axis compliance.Comment: IROS 201

CRUX: a Compliant Robotic Upper-Extremity eXosuit for Lightweight, Portable, Multi-DoF Muscular Augmentation

Wearable robots can potentially offer their users enhanced stability and strength. These augmentations are ideally designed to actuate harmoniously with the users movements and provide extra force as needed. The creation of such robots, however, is particularly challenging due to the complexity of the underlying human body. In this paper, we present a compliant, robotic exosuit for upper-extremities called CRUX. This exosuit, inspired by tensegrity models of the human arm, features a lightweight (1.3 kg), flexible design for portability. We also show how CRUX maintains full flexibility of the upper-extremities for its users while providing multi- DoF augmentative strength to the major muscles of the arm, as evident by tracking the heart rate of an individual exercising said arm. Exosuits such as CRUX may be useful in physical therapy and in extreme environments where users are expected to exert their bodies to the fullest extent

Exploring sonic parameter mapping for network data structures

Presented at the 25th International Conference on Auditory Display (ICAD 2019) 23-27 June 2019, Northumbria University, Newcastle upon Tyne, UK.In this paper, we explore how sonic features can be used to represent network data structures that define relationships between elements. Representations of networks are pervasive in contemporary life (social networks, route planning, etc), and network analysis is an increasingly important aspect of data science (data mining, biological modeling, deep learning, etc). We present our initial findings on the ability of users to understand, decipher, and recreate sound representations to support primary network tasks, such as counting the number of elements in a network, identifying connections between nodes, determining the relative weight of connections between nodes, and recognizing which category an element belongs to. The results of an initial exploratory study (n=6) indicate that users are able to conceptualize mappings between sounds and visual network features, but that when asked to produce a visual representation of sounds users tend to generate outputs that closely resemble familiar musical notation. A more in-depth pilot study (n=26) more specifically examined which sonic parameters (melody, harmony, timbre, rhythm, dynamics) map most effectively to network features (node count, node classification, connectivity, edge weight). Our results indicate that users can conceptualize relationships between sound features and network features, and can create or use mappings between the aural and visual domains