Experience-based optimization of universal manipulation strategies for industrial assembly tasks
The trend towards smaller lot sizes and shorter product life cycles requires automation solutions with higher flexibility. Todays robotic systems often are uneconomical for frequently changing boundary conditions and varying tasks due to high engineering costs needed for a well-defined supply of parts and pallets. At the same time, even small inaccuracies due to shape deviations in parts or pallets often cause high downtime. This work contributes to the robustness of industrial assembly processes with high inaccuracy concurrent to narrow tolerances. Therefore, contact-based manipulation strategies are defined, which are model-free and object-independent and solve common industrial tasks as palletizing, packaging and machine feeding. While the strategies are robust to inaccuracy up to 5mm/5 due to localization uncertainty or object displacement, they handle usual industrial assembly tolerance of far below 1mm. The necessary flexibility and reusability for new tasks is guaranteed by hierarchical decomposition into atomic sub-strategies. In order to accelerate the execution, the manipulation strategies are customized to each specific task by unsupervised experience-based learning. The flexibility of the manipulation strategies and the progress in cycle time during the execution are shown on common industrial tasks with varying objects, tolerances and inaccuracies.
experience-based learning industrial manipulation robotic assembly hierarchical task decomposition cycle-time optimization case-based reasoning