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Tool-Path planning is the foundation for automating many manufacturing processes. Robotic manipulators are increasingly being considered to automate tasks that require complex tool motions. Robotic manipulators provide extra degrees of freedom and are more flexible than traditional automation technologies. However, a tool-path needs to be planned and given to the manipulator trajectory generator as input. The traditional tool-path planning considers the tool to have one contact point, known as the Tool Center Point (TCP). This underutilizes the available flexibility of the manipulator. To make use of the manipulator’s flexibility, multiple contact points or multiple TCPs can be considered. These tool contact considerations make the tool path planning problem complex and computationally challenging. In this paper, we present a novel tool path planning algorithm. Our algorithm incorporates the multiple tool contact points consideration during tool-path planning in an efficient manner to generate a high-quality tool-path in a reasonable amount of run time. We evaluated our algorithm on four different test cases. The test cases include the parts with complex geometry and tools which can have surface area for establishing contacts with the part. In these test cases, a traditional single contact tool-path will not work due to geometric constraints, and multiple tool contacts make the problem challenging. Our algorithm was able to generate tool-paths for these four test cases successfully.

High-performance composites are widely used in industry because of specific mechanical properties and lightweighting opportunities. Current automation solutions to manufacturing components from prepreg (pre-impregnated precursor material) sheets are limited. Our previous work has demonstrated the technical feasibility of a robotic cell to automate the sheet layup process. Many decisions are required for the cell to function correctly, and the time necessary to make these decisions must be reduced to utilize the cell effectively. Robot placement with respect to the mold is a significant and complex decision problem. Ensuring that robots can collaborate effectively requires addressing multiple constraints related to the robot workspace, singularity, and velocities. Solving this problem requires developing computationally efficient algorithms to find feasible robot placements in the cell. We describe an approach based on successive solution refinement strategy to identify a cell design that satisfies all constraints related to robot placement.

Using fixtures for assembly operations is a common practice in manufacturing processes with high production volume. For automated assembly cells using robotic arms, trajectories are programmed manually and robots follow the same path repeatedly. It is not economically feasible to build fixed fixtures for small volume productions as they require high accuracy and are part specific. Moreover, hand coding robot trajectories is a time consuming task. The uncertainties in part localization and inaccuracy in robot motions make it challenging to automate the task of assembling two parts with tight tolerances. Researchers in past have developed methods for automating the assembly task using contact-based search schemes and impedance control-based trajectory execution. Both of these approaches may lead to undesired collision with critical features on the parts. Our method guarantees safety for parts with delicate features during the assembly process. Our approach enables us to select optimum impedance control parameters and utilizes a learning-based search strategy to complete assembly tasks under uncertainties in bounded time. Our approach was tested on an assembly of two rectangular workpieces using KUKA IIWA 7 manipulator. The method we propose was able to successfully select the optimal control parameters. The learning-based search strategy successfully estimated the uncertainty in pose of parts and converged in few iterations.

Surface finishing is an important manufacturing process. Many parts with complex geometries require finishing of internal regions before they can be used. In small and medium volume productions most of the finishing tasks are non-repetitive in nature, and have to be performed manually. These finishing operations for parts with complex geometries can be quite labor intensive, and may pose risk to humans. We have developed a collaborative finishing system where human operators work on high level decision making, and the robot assistants carry out the labor intensive low level finishing tasks. The human operator guides the robotic system by transferring operator knowledge through a user interface. Our system generates instructions for the robots based on the user inputs and task requirements. We have also developed a planning algorithm that automatically computes the paths for the robots by using the CAD model of the part. This significantly reduces the robot programming time and improves the efficiency of the finishing system. If needed, the system seeks help from the human operator by generating notifications.

We present an approach to automatically learn a bimanual robotic cleaning task on compliant objects. One robot grasps the object, while the other robot cleans it. Given a part with unknown deformation characteristics, the system visually detects the regions to be cleaned, and generates plans for both the grasping and cleaning arms. As the system performs cleaning attempts and gains experience with multiple new parts, it learns models of the part deformation depending on the cleaning force and grasping parameters. A planner iteratively generates tool paths for both robots using the available knowledge to optimize the cleaning time, including (1) delays from regrasping a part to minimize deflection and (2) time taken for repeated cleaning attempts over regions that remained dirty. A nonparametric deflection model is learned separately for each part, with minimal assumptions of the material behavior. We demonstrate the approach on a system of two KUKA LWR iiwa robots and a set of thin planar parts. Results indicate that the system is effective at rapidly learning part deformation models to enable effective iterative cleaning performance.