Safe Collision and Clamping Reaction for Parallel Robots During Human-Robot Collaboration
Aran Mohammad, Moritz Schappler, Tim-Lukas Habich, Tobias Ortmaier

TL;DR
This paper presents a comprehensive safety strategy for parallel robots during human-robot collaboration, combining contact detection, reactive retraction, impedance adjustment, and neural network-based classification to ensure rapid and effective collision and clamping responses.
Contribution
It introduces a novel integrated safety approach using proprioceptive sensing, neural networks, and kinematic projections for collision and clamping mitigation in parallel robots.
Findings
Reaction time under 130ms in all contact scenarios
Neural network classification accuracy of 80% for clamping detection
Effective contact force limitation to 70N during experiments
Abstract
Parallel robots (PRs) offer the potential for safe human-robot collaboration because of their low moving masses. Due to the in-parallel kinematic chains, the risk of contact in the form of collisions and clamping at a chain increases. Ensuring safety is investigated in this work through various contact reactions on a real planar PR. External forces are estimated based on proprioceptive information and a dynamics model, which allows contact detection. Retraction along the direction of the estimated line of action provides an instantaneous response to limit the occurring contact forces within the experiment to 70N at a maximum velocity 0.4m/s. A reduction in the stiffness of a Cartesian impedance control is investigated as a further strategy. For clamping, a feedforward neural network (FNN) is trained and tested in different joint angle configurations to classify whether a collision or…
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Taxonomy
TopicsRobot Manipulation and Learning · Muscle activation and electromyography studies · Prosthetics and Rehabilitation Robotics
