Adaptive 6-DoF Haptic Contact Stiffness Using the Gauss Map
IEEE Transactions on Haptics 2016
People
- Hongyi Xu
University of Southern California - Jernej Barbič
University of Southern California
Project material
Citation
-
Hongyi Xu, Jernej Barbič:
Adaptive 6-DoF Haptic Contact Stiffness Using the Gauss Map, IEEE Transactions on Haptics, 2016, 9(3), p. 323-332 BIBTEX
Abstract
The penalty method is a popular approach to resolving contact in haptic rendering. In simulations involving complex distributed contact, there are, however, many simultaneous individual contacts. These contacts have normals pointing in several directions, many of which may be parallel, causing the stiffness to effectively accumulate in a temporally highly-varying and unpredictable way. Consequently, penalty-based simulation suffers from stability problems. Previous methods tackled this problem using implicit integration, or by scaling the stiffness down globally by the number of contacts. Although this provides some control over the net stiffness, it leads to large penetrations, as small contacts are effectively ignored when compared to larger contacts. We propose an adaptive stiffness method that employs the Gauss map of contact normals to ensure a spatially uniform and controllable stiffness in all contact directions. Combined with virtual coupling saturation, penetration can be kept shallow and simulation remains stable, even for complex geometry in distributed contact. Our method is fast and can be applied to any penalty-based formulation between rigid objects. While used primarily for rigid objects, we also apply our method to reduced deformable objects. We demonstrate our approach on several challenging 6-DoF haptic rendering scenarios, such as car engine and landing gear virtual assembly.
Comments, questions to Jernej Barbič.Related projects
- 6-DoF Haptic Rendering using Continuous Collision Detection Between Points and Signed Distance Fields
- Six-DoF Haptic Rendering of Contact between Geometrically Complex Reduced Deformable Models
- Signed Distance Fields for Polygon Soup Meshes
Funding
- NSF (CAREER-1055035, IIS-1422869)
- Sloan Foundation
- Okawa Foundation
- USC Annerberg Graduate Fellowship to Hongyi Xu
Acknowledgment
- Boeing for providing the Boeing 777 dataset
Disclaimer
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
Copyright notice
The documents contained in these directories are included by the contributing authors as a means to ensure timely dissemination of scholarly and technical work on a non-commercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.
Unique accesses: