Off-Axis Compliant RCM Joint with Near-Isotropic Stiffness and Minimal Parasitic Error

This paper presents an off-axis, monolithic compliant Remote Center of Motion (RCM) joint for neuroendoscopic manipulation, combining near-isotropic stiffness with minimal parasitic motion. Based on the Tetra II concept, the end-effector is placed outside the tetrahedral flexure to improve line of sight, facilitate sterilization, and allow rapid tool release. Design proceeds in two stages: mobility panels are sized with a compliance-based isotropy objective, then constraining panels are synthesized through finite-element feasibility exploration to trade stiffness isotropy against RCM drift. The joint is modeled with beam elements and validated via detailed finite-element analyses, including fatigue-bounded stress constraints. A PA12 prototype is fabricated by selective laser sintering and characterized on a benchtop: a 2 N radial load is applied at the end-effector while a 6-DOF electromagnetic sensor records pose. The selected configuration produces a stiffness-ellipse principal axis ratio (PAR) of 1.37 and a parasitic-to-useful rotation ratio (PRR) of 0.63%. Under a 4.5° commanded rotation, the predicted RCM drift remains sub-millimetric (0.015-0.172 mm). Fatigue analysis predicts a usable rotational workspace of 12.1°-34.4° depending on direction. Experiments reproduce the simulated directional stiffness trend with typical deviations of 6-30%, demonstrating a compact, fabrication-ready RCM module for constrained surgical access.

How can AI reduce wrist injuries in the workplace?

This paper explores the development of a control and sensor strategy for an industrial wearable wrist exoskeleton by classifying and predicting workers' actions. The study evaluates the correlation between exerted force and effort intensity, along with sensor strategy optimization, for designing purposes. Using data from six healthy subjects in a manufacturing plant, this paper presents EMG-based models for wrist motion classification and force prediction. Wrist motion recognition is achieved through a pattern recognition algorithm developed with surface EMG data from an 8-channel EMG sensor (Myo Armband); while a force regression model uses wrist and hand force measurements from a commercial handheld dynamometer (Vernier GoDirect Hand Dynamometer). This control strategy forms the foundation for a streamlined exoskeleton architecture designed for industrial applications, focusing on simplicity, reduced costs, and minimal sensor use while ensuring reliable and effective assistance.

Ergonomic Assessment of Work Activities for an Industrial-oriented Wrist Exoskeleton

Musculoskeletal disorders (MSD) are the most common cause of work-related injuries and lost production involving approximately 1.7 billion people worldwide and mainly affect low back (more than 50%) and upper limbs (more than 40%). It has a profound effect on both the workers affected and the company. This paper provides an ergonomic assessment of different work activities in a horse saddle-making company, involving 5 workers. This aim guides the design of a wrist exoskeleton to reduce the risk of musculoskeletal diseases wherever it is impossible to automate the production process. This evaluation is done either through subjective and objective measurement, respectively using questionnaires and by measurement of muscle activation with sEMG sensors.

Human-inspired Grasping Strategies of Fresh Fruits and Vegetables Applied to Robotic Manipulation

Robotic manipulation of fresh fruits and vegetables, including the grasping of multiple loose items, has a strong industrial need but it still is a challenging task for robotic manipulation. This paper outlines the distinctive manipulation strategies used by humans to pick loose fruits and vegetables with the aim to better adopt them for robotic manipulation of diverse items. In this work we present a first version of a robotic setup designed to pick different single or multiple fresh items, featuring multi-fingered compliant robotic gripper. We analyse human grasping strategies from the perspective of industrial Key Performance Indicators (KPIs) used in the logistic sector. The robotic system was validated using the same KPIs, as well as taking into account human performance and strategies. This paper lays the foundation for future development of the robotic demonstrator for fresh fruit and vegetable intelligent manipulation, and outlines the need for generic approaches to handle the complexity of the task.