Details
| Original language | English |
|---|---|
| Title of host publication | 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
| Pages | 5910-5917 |
| Number of pages | 8 |
| ISBN (electronic) | 978-1-6654-9190-7 |
| Publication status | Published - 2023 |
Publication series
| Name | Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems |
|---|---|
| ISSN (Print) | 2153-0858 |
| ISSN (electronic) | 2153-0866 |
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 3 cm and an identification error of 4 N in a real-world experiment.
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Computer Science(all)
- Computer Science Applications
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2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2023. p. 5910-5917 ( Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
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 -