This workshop will describe computational foundations for creating robotic assistants for small volume manufacturing tasks. The presentation will begin with an overview of an integrated decision making approach that brings together concepts from perception, planning, control, and learning to realize robotic assistants that can aid human workers in manufacturing.
Traditional off-line robot programming approaches cannot be used in small production volume applications. We will describe a decision making approach based on the integration of real-time planning and perception for performing non-repetitive tasks using robots. There are many tasks for which a simulation-based planning approach cannot be used to select the optimal process parameters. For such tasks, we will describe a new approach for robots to learn task parameters from self-exploration. Both humans and robots can make errors in a hybrid cell, hence creating contingency situations. We will describe a decision making approach for detecting and managing contingencies. Bin picking, assembly, and finishing tasks will be used as illustrative examples to show how robots can be used in small volume manufacturing tasks.
Who Should Attend
Company Management, Engineering, Researchers, Design/Product Development
- Understand recent trends in industrial robots
- Use automated trajectory planning for industrial robots
- Design robotic cells for bin picking, assembly, and finishing applications
Traditionally, industrial robots have been used on mass production lines, where the same manufacturing operation is repeated many times. Many sectors of manufacturing such as aerospace, defense, ship building, mold and die making involve small production volumes. Currently, industrial robots are not used in such applications. The use of robotic assistants can significantly improve human operator productivity in small production volume manufacturing and eliminate the need for human involvement in tasks that pose risks to human health.