Grinding metal parts remains a repetitive, time-consuming, and ergonomically unfriendly process. This project seeks to address these issues resulting in higher quality and better working conditions for human workers.

Project

Grinding metal parts remains a repetitive and ergonomically unfriendly task that often generates inconsistent results. This project will empower American workers to transition from manual grinders into safer robot-operated roles through the development of an advanced robotic grinding system. The system will use 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.

Objective

Increase the efficiency of grinding operations with decreased cost and better working conditions for human workers.

Technical Approach

This project focuses on the creation of an optimal work cell designed to facilitate efficiency in robotic grinding. The system will use:

  • 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.

Participants

ITAMCO, University of Southern California, York Exponential