Simulation Based Initial Feasibility Analysis Pipeline for Small-sized Part Picking

Abstract

Random bin picking is still one of the main tasks for robotics in the current days. When the environment is very cluttered, the calculation of grasping positions can be highly demanding in terms of time and computing power. To ease the computation load, some parts arranging operations can be performed before the segmentation stage. For instance, for small and light parts, a feeder-vibrating table system can be used to separate the components, allowing them to be easily grasped, and increasing the overall performance of the solution. However, as the geometry and requirements for piece type are different, one or more feasibility tests need to be done for each case. These analyses are usually very time and cost intensive and require the use of expensive hardware such as robots, grippers, and prototype cells. The use of virtual reproductions of the environment like digital twins or physical-based simulations could help reduce the time and effort spent on designing the settings, nevertheless, their correct configuration is not trivial. This paper presents a simulation based analysis method for picking small-sized parts. It aims to supply the tools and define a streamlined procedure for efficient feasibility testing. Those concepts are applied in a specific bin picking scenario of multiple small electronic components. For each part type, a set of case-specific initial and boundary conditions are taken into account, then a series of performance metrics for both bin and vibrating table part picking are computed. The obtained information is decisive to make strategic decisions regarding the hardware requirements, the profitability, and the success probability of the project.