Planning and optimization of robotic pick-and-place operations in highly constrained industrial environments

Purpose The purpose of this paper is to give a comprehensive solution method for the manipulation of parts with complex geometries arriving in bulk into a robotic assembly cell. As bin-picking applications are still not reliable in intricate workcells, first, the problem is transformed to a semi-structured pick-and-place application, then by collecting and organizing the required process planning steps, a methodology is formed to achieve reliable factory applications even in crowded assembly cell environments. Design/methodology/approach The process planning steps are separated into offline precomputation and online planning. The offline phase focuses on preparing the operation and reducing the online computational burdens. During the online phase, the parts laying in a semi-structured arrangement are first recognized and localized based on their stable equilibrium using two-dimensional vision. Then, the picking sequence and corresponding collision-free robot trajectories are planned and optimized. Findings The proposed method was evaluated in a geometrically complex experimental workcell, where it ensured precise, collision-free operation. Moreover, the applied planning processes could significantly reduce the execution time compared to heuristic approaches. Research limitations/implications The methodology can be further generalized by considering multiple part types and grasping modes. Additionally, the automation of grasp planning and the enhancement of part localization, sequence planning and path smoothing with more advanced solutions are further research directions. Originality/value The paper proposes a novel methodology that combines geometrical computations, image processing and combinatorial optimization, adapted to the requirements of flexible pick-and-place applications. The methodology covers each required planning step to reach reliable and more efficient operation.

Data-Driven Object Pose Estimation in a Practical Bin-Picking Application

This paper addresses the problem of pose estimation from 2D images for textureless industrial metallic parts for a semistructured bin-picking task. The appearance of metallic reflective parts is highly dependent on the camera viewing direction, as well as the distribution of light on the object, making conventional vision-based methods unsuitable for the task. We propose a solution using direct light at a fixed position to the camera, mounted directly on the robot’s gripper, that allows us to take advantage of the reflective properties of the manipulated object. We propose a data-driven approach based on convolutional neural networks (CNN), without the need for a hard-coded geometry of the manipulated object. The solution was modified for an industrial application and extensively tested in a real factory. Our solution uses a cheap 2D camera and allows for a semi-automatic data-gathering process on-site.