Grip Region Detection for Flexible Object Robot Picking
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Solution Overview
Problem
Robots face challenges in reliably gripping flexible objects, such as packages of clothing, due to their deformable nature, which can result in non-smooth surfaces that interfere with the adhesion of end effector components like suction cups, leading to inconsistent gripping and potential collisions during motion.
Innovation Solution
A computing system that identifies a grip region on a flexible object's surface by searching for a smooth, contiguous area using image information from a camera, and defines a safety region to reduce collision risk, enabling precise robot motion planning for gripping and movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If robots use end effector components like suction cups to grip flexible objects, then gripping capability is improved, but the deformable nature of flexible objects creates non-smooth surfaces that reduce adhesion reliability
Solution Approach 1:
The system performs preliminary detection of smooth surface regions on the flexible object before gripping. By using a camera to capture images and identify regions that satisfy smoothness conditions and size thresholds, the system prepares grip region information in advance, allowing the robot to select optimal gripping locations that avoid non-smooth areas caused by the object's deformable nature.
2Measurement precision
If robots perform motion planning for gripping flexible objects, then gripping precision is improved, but the deformable nature of the objects increases collision risk during motion
Solution Approach 1:
The system performs preliminary identification of safety regions that extend from the grip region along the vertical dimension before motion execution. This advance preparation of safety region information allows the motion planning system to account for potential collisions with other objects in the environment, reducing collision risk while maintaining gripping precision.
3Stability of the object's composition
If the system identifies grip regions based on smoothness conditions and size thresholds, then gripping stability is improved, but the complexity of image processing and region identification increases
Solution Approach 1:
The system segments the image processing task into distinct stages: capturing images of the flexible object, identifying surface regions that satisfy smoothness conditions, filtering regions by size thresholds, and selecting optimal grip regions. This segmentation of the image processing pipeline reduces overall complexity by breaking down the complex task into manageable, sequential steps with clear criteria for each stage.
Data Source
AI summary
A method performed by a computing system is presented. The method may include the computing system receiving image information that represents an object surface associated with a flexible object, and identifying, as a grip region, a surface region of the object surface that satisfies a defined smoothness condition and has a region size that is larger than or equal to a defined region size threshold, wherein the grip region is identified based on the image information. The method may further include identifying, as a safety region, a three-dimensional (3D) region which surrounds the grip region in one or more horizontal dimensions, and which extends from the grip region along a vertical dimension that is perpendicular to the one or more horizontal dimensions. The method may further include performing robot motion planning based on the grip region and the safety region.