manufacturing involves coordinated motion between multiple robots performing additive, subtractive, and transformative manufacturing tasks, with integrated sensing and control. The overall objective of the project is to develop a convergent manufacturing control architecture that can acquire data from the suite of sensors and command motion of multiple robots, and to use tool and part measurements to improve the accuracy and efficiency of the manufacturing process. The project will first develop an integrated software architecture to support the coordinated motion of robots from multiple vendors and data acquisition of in-process sensor measurements. Using this software architecture, the project will demonstrate motion adaptation algorithms for multiple robot motion coordination using in-process tool sensing and metrology. The goal is to achieve accurate relative tool motion at high speed without extensive calibration as required today. We plan to demonstrate the performance in an existing wire-arc additive manufacturing (WAAM) testbed at RPI that has a Yaskawa Motoman WAAM system consisting of a 6-dof welding robot and a 2-dof positioner equipped with a suite of in-process sensors (thermal imaging camera, spectroscopy sensor, acoustic detector, thermocouples). A second 6-dof Motoman robot will have a wrist- 3D metrology scanner to assess part geometry between layers. A motion capture system will provide robot end effector measurements to assess relative tool pose during motion. Algorithm development and demonstration will be conducted on the RPI WAAM testbed. A GE dual-robot cold spray testbed will serve as a potential technology transition site.
Robot Motion Program for Tracking Complex Geometric Paths
Advanced Robotics for Manufacturing (ARM) Institute
John T. Wen
Agung Julius, Santiago Paternain, Glenn Saunders
Convergent Manufacturing using Multiple Industrial Robots
Advanced Robotics in Manufacturing (ARM) Institute
John T. Wen
Johnson Samuel, Agung Julius, Santiago Paternain, Glenn Saunders
Industrial robots are increasingly deployed in applications requiring an end effector tool to closely track a specified path, such as in spraying and welding. Performance and productivity present possibly conflicting objectives: tracking accuracy, path speed, and speed uniformity. Industrial robots are programmed through motion primitives consisting of waypoints connected by pre-defined motion segments, with specified parameters such as path speed and blending zone. The actual executed robot motion depends on the robot joint servo controller and joint motion constraints (velocity, acceleration, etc.) which are largely unknown to the users. Programming a robot to achieve the desired performance today is time-consuming and mostly manual, requiring tuning a large number of coupled parameters in the motion primitives. The performance also depends on the choice of several static parameters: possible redundant degrees of freedom, location of the target curve, and the robot configuration. This project develops a systematic approach to optimize the robot configuration and motion for performance. The approach first selects the static parameters, then the motion primitives, and finally iteratively update the waypoints to minimize the tracking error. The ultimate performance objective is to maximize the path speed subject to the tracking accuracy and speed uniformity constraints over the entire path. We have demonstrated the effectiveness of this approach in simulation for ABB and FANUC robots for two challenging example curves, and experimentally for an ABB robot. Comparing with the baseline using the current industry practice, the optimized performance shows over 400% performance improvement.