Robot Item Selection Using 3D Clearance-Based Grasp Point Screening
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Solution Overview
Problem
In warehouse storage systems, automated retrieval of items using robots with lateral-motion grippers is challenging due to the need to assess clearance in 3D Point Cloud data from Depth Sensors, as existing methods fail to accurately select items with sufficient space for grasping without obstructed paths.
Innovation Solution
A retrieval controller system that processes depth maps from Depth Sensors to establish a global coordinate system, segment 3D Point Clouds, compute enclosing prisms, and determine grasp points with sufficient clearance, allowing robots to select and retrieve items based on UV coordinates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a lateral-motion gripper is used to retrieve items, then the robot can approach items from horizontal directions, but the gripper cannot grasp an item if the path to the grasp point is occupied by other items
Solution Approach 1:
The patent segments the 3D Point Cloud data into individual item representations, allowing the system to analyze clearance for each item independently. This segmentation enables the robot to identify valid grasp points by checking if paths from different approach directions are occupied by other items, thus resolving the contradiction between versatile approach directions and reliable grasp execution.
2Device complexity
If strict constraints are imposed on item locations and destination states, then blind material handling equipment can be used without sensors, but the system cannot cope with variations in locations and destinations
Solution Approach 1:
The patent replaces complex sensor-based detection systems with a computational geometry approach. By using 3D Point Cloud data and applying clearance assessment algorithms with enclosing prisms and grasp point validation, the system achieves adaptive item selection without requiring sophisticated sensors, thus resolving the contradiction between simple device complexity and high adaptability.
3Adaptability or versatility
If a robot uses sensor readings to locate items and destinations in uncontrolled environments, then the system can cope with location variations, but the complexity of the retrieval system increases
Solution Approach 1:
The patent transforms the retrieval problem by changing parameters from continuous 3D spatial reasoning to discrete geometric validation. By representing items as segmented point clouds and using enclosing prisms with defined grasp points, the system simplifies the complexity of environmental adaptability while maintaining the ability to handle location variations through computational geometry operations.
Data Source
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AI summary
The present invention provides a retrieval controller for identifying an item to be retrieved from a flat storage surface by a robot so as to solve the problem of identifying one item, amongst a collection of items stored on a common surface that can be retrieved by a robot equipped with a lateral-motion gripper. To achieve this, the present invention provides a retrieval controller comprising a depth map computing unit arranged to establish a global coordinate system, establish an orthonormal set of basis vectors u, v and w defined in the global coordinate system, where w is approximately orthogonal to the surface that the items are stored on, receiving a depth map from a depth sensor, converting the received depth map into a 3D Point Cloud defined in the global coordinate system, computing a representation of a partitioning into segments of the 3D Points of the 3D Point Cloud such that a segment contains a pair of 3D Points only if the 3D points should be considered to be part of the surface of the same item and a prism calculating unit arranged to compute a right, enclosing prism for each segment. The retrieval controller further comprises a vector determination unit arranged to compute each of: a) outwards-pointing normal of each w-aligned face of each computed right, enclosing prism, b) outwards-pointing normal of each w-aligned edge of each computed right, enclosing prism and c) which w-aligned edges of each computed right, enclosing prism correspond to grasp points that should be precluded from an item selection process. Moreover, the retrieval controller comprises an item selection unit arranged to iterate over the w-aligned edges of each right, enclosing prism, that do not correspond to grasp points that should be precluded from the item selection process, computing a pair of quadrilateral-based, right prisms for each such a w-aligned edge, checking whether or not the interior of either of the two quadrilateral-based, right prisms associated with a w-aligned edge intersects any of the right, enclosing prisms and a robot instructing unit arranged to instruct the robot to retrieve the item based on uv coordinates of one or more w-aligned edges whose associated quadrilateral-based, right prisms do not have interiors that intersect any of the right, enclosing prisms as selected by the item selection unit.