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Mobile manipulators are being deployed for transporting parts between machines and work stations in warehouses and shop floors. To increase the efficiency of operations, these mobile manipulators are required to complete the tasks as fast as possible. Picking up parts with the manipulator while the mobile base is moving decreases the time required to complete the transportation task and increases the efficiency of operations. However, motions of the manipulator on a moving platform can be risky, and hence, it is desired that the manipulator starts and ends its motions as close as possible to the part being picked up. In this article, we present a bidirectional sampling-based scheme for generating such manipulator trajectories for a given mobile base trajectory for pickup and transportation. Our approach implicitly determines the location of the mobile base where the manipulator motion starts and ends as well as where grasping happens. It also determines which grasping pose to use for picking up the part. Furthermore, we have presented the techniques to reduce the manipulator motion time (span time) and the computation time. Our approach enables us to reduce span time on average by 35% with a 16x reduction in the computation time compared to the RRT-based baseline methods.

This paper presents planning algorithms for robotic cleaning of stains on nonplanar surfaces. Access to different portions of the stain may require frequent repositioning and reorienting of the object. Some portions with prominent stain may require multiple passes to remove the stain completely. Two robotic arms have been used in the experiments. The object is immobilized with one arm and the cleaning tool is manipulated with the other. The algorithm generates a sequence of reorientation and repositioning moves required to clean the part after analyzing the stain. The plan is generated by accounting for the kinematic constraints of the robot. Our algorithm uses a depth-first branch-and-bound search to generate setup plans. Cleaning trajectories are generated and optimal cleaning parameters are selected by the algorithm. We have validated our approach through numerical simulations and robotic cleaning experiments with two KUKA robots. Note to Practitioners —We encounter nonrepetitive cleaning tasks everyday in both industrial and household environments. Variations in stain pattern, geometry, and material of the object make it difficult to manually program robots for such tasks. In this paper, we present planning algorithms to automate the cleaning task using robots. The practical impact of our approach is evidenced by the actual robot results involving realistic examples like cleaning of hard paint stains on curved surfaces and rust on metal surfaces. Practitioners from industry can use the methods presented in this paper to develop automated robotic systems for nonrepetitive tasks like cleaning and polishing. Our approach caters to the primary requirements of these applications like multiple setups, multiple passes within each setup, and determination of optimal motion parameters like velocity, force, and oscillation frequency of the cleaning tool.