TY - GEN

T1 - Collision Isolation and Identification Using Proprioceptive Sensing for Parallel Robots to Enable Human-Robot Collaboration

AU - Mohammad, Aran

AU - Schappler, Moritz

AU - Ortmaier, Tobias

N1 - Funding Information: The authors acknowledge the support by the German Research Foundation (DFG) under grant number 444769341.

PY - 2023

Y1 - 2023

N2 - 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 3 cm and an identification error of 4 N in a real-world experiment.

AB - 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 3 cm and an identification error of 4 N in a real-world experiment.

UR - http://www.scopus.com/inward/record.url?scp=85170493661&partnerID=8YFLogxK

U2 - 10.48550/arXiv.2308.09650

DO - 10.48550/arXiv.2308.09650

M3 - Conference contribution

SN - 978-1-6654-9191-4

T3 - Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems

SP - 5910

EP - 5917

BT - 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

ER -