Grinding metal parts remains a repetitive and ergonomically unfriendly task that often generates inconsistent results. This project empowers American workers to transition from manual grinders into safer robot-operated roles through the development of an advanced robotic grinding system. The system uses model-based control and learning algorithms to account for uncertainties in its environment while using automated trajectory generation and 3D part model and identification of surface regions, creating consistent, high-quality results.
Increase the efficiency of grinding operations with decreased cost and better working conditions for human workers.
This project focused on the creation of an optimal work cell designed to facilitate efficiency in robotic grinding. The system uses:
- Automated trajectory generation, 3D part models, and the identification of surface regions that need grinding using sensors and efficient algorithms
- Model-based control and learning algorithms to account for uncertainties
The components and algorithms generated will undergo comprehensive testing from stakeholders as part of the projects.
Texas A&M University, ITAMCO, University of Southern California, York Exponential