Collision Isolation and Identification Using Proprioceptive Sensing for Parallel Robots to Enable Human-Robot Collaboration

TL;DR

This paper presents a novel proprioceptive sensing algorithm for collision detection, isolation, and identification in parallel robots, enabling safer human-robot collaboration through accurate collision classification and localization.

Contribution

It introduces a physically modeled feature extraction method combined with neural networks and particle filters for effective collision detection and localization in parallel robots.

Findings

Collision classification accuracy of 84% across 300k load cases.

Platform collisions are explicitly isolated and identified.

Contact location and force are estimated with errors less than 3cm and 4N.

Abstract

Parallel robots (PRs) allow for higher speeds in human-robot collaboration due to their lower moving masses but are more prone to unintended contact. For a safe reaction, knowledge of the location and force of a collision is useful. A novel algorithm for collision isolation and identification with proprioceptive information for a real PR is the scope of this work. To classify the collided body, the effects of contact forces at the links and platform of the PR are analyzed using a kinetostatic projection. This insight enables the derivation of features from the line of action of the estimated external force. The significance of these features is confirmed in experiments for various load cases. A feedforward neural network (FNN) classifies the collided body based on these physically modeled features. Generalization with the FNN to 300k load cases on the whole robot structure in other joint angle configurations is successfully performed with a collision-body classification accuracy of 84% in the experiments. Platform collisions are isolated and identified with an explicit solution, while a particle filter estimates the location and force of a contact on a kinematic chain. Updating the particle filter with estimated external joint torques leads to an isolation error of less than 3cm and an identification error of 4N in a real-world experiment.