Reasoning for Improved Capacity in Robotic Pick-and-Place Tasks

Abstract

As e-commerce continues to grow, warehouses face the challenge of keeping up with product flows. Vanderlande provides a pick-and-place robot (SIR) that helps in improving capacity. However, SIR's capacity is limited by its vacuum gripper and its inability to solve failures. The aim of this thesis is to improve SIR by adding a second gripper and reasoning capabilities. To this end, a grasp synthesis approach is developed that complements the vacuum gripper, based on existing approaches in literature and the devices available to SIR. Furthermore, an ontology is developed according to Methontology that allows the robot to reason about item properties and failures, while providing operators with insight into its thought process. The improved system is referred to as RSIR.
The grasp synthesis approach, applicable to parallel-jaw grippers, synthesizes grasps by detecting parallel edges in depth data. Tests on real depth data showed that the approach synthesized reasonable grasps in reasonable time for a variety of items, some of which are not graspable by SIR's vacuum gripper. It did not need a segmentation of the items to do so. Furthermore, RSIR was tested on a virtual robotic set-up. The results showed that RSIR reasoned how best to complete a task when provided with information about a robot's components and possible item properties. RSIR also drew conclusions based on the execution of the task, and reasoned what actions should be performed to prevent failures from re-occurring. The above results show RSIR's potential for autonomously estimating item properties and selecting the best approach for grasping an item. In practice, this decreases downtime and thus improves capacity. Because RSIR is designed to be expandable to related tasks, its benefits could be useful in more of Vanderlande's applications than just the SIR system.