Robots that autonomously manipulate objects within warehouses have the potential to shorten the package delivery time and improve the efficiency of the e-commerce industry. In this paper, we present a robotic system that is capable of both picking and placing general objects in warehouse scenarios. Given a target object, the robot autonomously detects it from a shelf or a table and estimates its full 6D pose. With this pose information, the robot picks the object using its gripper, and then places it into a container or at a specified location. We describe our pick-and-place system in detail while highlighting our design principles for the warehouse settings, including the perception method that leverages knowledge about its workspace, three grippers designed to handle a large variety of different objects in terms of shape, weight and material, and grasp planning in cluttered scenarios. We also present extensive experiments to evaluate the performance of our picking system and demonstrate that the robot is competent to accomplish various tasks in warehouse settings, such as picking a target item from a tight space, grasping different objects from the shelf, and performing pick-and-place tasks on the table.
In this paper we describe some of the key technologies of a mobile manipulator that are used for package transportation in a pharmaceutical warehouse. The paper presents, beyond the functional aspects of the system, the main modules of the software that controls the mobile manipulator. The robot is a demonstrator of technologies for transporting safely and efficiently goods in partially structured, dynamic and public environments. Several issues are discussed such as the design and integration of mechanical elements, the development of non invasive localization and guidance procedures, the design and control of grasping devices for specific box storage combinations, and the development of testing, verification and validation procedures satisfying strict pharmaceutical regulations. Results of the laboratory tests demonstrate the capability of the prototype. The paper describes an application of computer vision in the autonomous guidance of a traditional forklift truck, ROBOLIFT, which is a product of Elsag Bailey Telerobot and FIAT OM. Computer vision represents the main sensory system for both navigation and load recognition. The system is now shifting from the prototype stage to production and commercialization. Field tests have been carried out and results are reported. We present a novel trajectory computation algorithm to smooth piecewise linear collision-free trajectories computed by sample-based motion planners. Our approach uses cubic B-splines to generate trajectories that are C2 almost everywhere, except on a few isolated points. The algorithm performs local spline refinement to compute smooth, collision-free trajectories and it works well even in environments with narrow passages. We also present a fast and reliable algorithm for collision checking between a robot and the environment along the B-spline trajectories. We highlight the performance of our algorithm on complex benchmarks, including path computation for rigid and articulated models in cluttered environments. 