Beyond End-Effector: Utilizing High-Resolution Tactile Signals for Physical Human–Robot Interaction
This article proposes a control framework for robots to apply their entire body as potential effectors in physical human–robot interaction (pHRI) tasks. This framework is implemented based on high-resolution electronic skin that covers the entire body of the robot. During pHRI, robots must respond appropriately to human intentions, necessitating advanced sensing capabilities to interpret tactile information effectively. However, both discerning human intention from such large-scale tactile data and accommodating interaction across the entire body's surface present challenges. In this article, we propose a method to convert the large-area contact on a link into a contact center and estimate the corresponding wrench. Furthermore, we assign soft priorities and desired trajectories to each contact point and solve for the optimal joint velocities through quadratic programming (QP). By enabling a dual-arm mobile manipulator to dance the waltz with a human, our control framework has been validated for its effectiveness in handling multiple large-area contacts and time-varying pHRI tasks.
Authors
- Wang Wei Lee (ORCID: https://orcid.org/0000-0001-9195-3226)
- Rong Xiong (ORCID: https://orcid.org/0000-0001-9318-9014)
- Haojian Lu (ORCID: https://orcid.org/0000-0002-1393-3040)
- Zhengyou Zhang (ORCID: https://orcid.org/0000-0002-6606-2525)
- L.Y Chen (ORCID: https://orcid.org/0000-0002-4899-9920)
- Cong Xiao
- Yu Sun (ORCID: https://orcid.org/0009-0002-4298-6552)
- Yue Wang (ORCID: https://orcid.org/0000-0002-0981-935X)
- Lipeng Chen (ORCID: https://orcid.org/0000-0003-2169-2766)
- Y. Zheng (ORCID: https://orcid.org/0000-0002-4617-3252)
Institutions
- Tencent (China) (CN)
Publication Details
- Journal
- IEEE Transactions on Industrial Electronics
- Published
- 2024-10-07
- DOI
- https://doi.org/10.1109/tie.2024.3458180
- Citations
- 2
- Primary Topic
- Tactile and Sensory Interactions
- Type
- article
- Field-Weighted Citation Impact
- 0.69