Entanglement End Effector for Accurate Overpackage Grasping
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Solution Overview
Problem
Robotic systems face challenges in accurately and efficiently manipulating objects of varying shapes, sizes, and weights due to uncertainty in identification and grasping, which hinders their ability to process high volumes of objects in fulfillment centers.
Innovation Solution
The implementation of an entanglement end effector with detectable identifiers and an entanglement structure that allows for precise localization and grasping of objects within an overpackage, using QR codes, AprilTags, and RFID tags to guide the end effector for accurate positioning and lifting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional robotic grippers are used to manipulate objects, then the system can handle standard-shaped objects, but it fails to accurately grasp objects with varying shapes, sizes, and weights
Solution Approach 1:
The robotic system employs dynamic gripper mechanisms that can adjust their shape, size, and gripping force in real-time based on the detected object characteristics. The gripper components are designed to be movable and reconfigurable, allowing the robot to adapt its end effector geometry to match the specific shape and size of each object being manipulated, thereby maintaining high grasping accuracy across diverse object types
Solution Approach 2:
The system changes physical parameters such as gripper aperture size, gripping force magnitude, and end effector position coordinates based on detected object properties. By dynamically adjusting these parameters according to the object's shape, size, and weight, the robotic system achieves accurate grasping of varied objects without requiring multiple specialized grippers
2Productivity
If manual object identification and manipulation methods are used, then accuracy can be maintained, but processing speed decreases and cannot meet high-volume fulfillment requirements
Solution Approach 1:
The system replaces manual visual inspection and decision-making with automated optical sensing systems, machine vision algorithms, and computer-controlled gripper mechanisms. Sensors and cameras detect object properties, and control systems automatically calculate optimal grasping parameters, enabling high-speed accurate manipulation without human intervention
Solution Approach 2:
The robotic system creates digital representations or models of objects based on sensor data, including 3D geometric models, texture maps, and physical property estimates. These digital copies are used for virtual manipulation testing and planning, allowing the system to determine accurate grasping strategies at high speed before executing physical manipulation
3Measurement precision
If robotic systems attempt to grasp objects directly without preliminary identification, then speed may be maintained, but accuracy of object identification and grasping point selection deteriorates
Solution Approach 1:
The robotic system performs preliminary object identification, localization, and grasping point selection before the actual manipulation task. Sensors scan and detect object properties in advance, the system calculates optimal gripper positioning and orientation, and prepares the grasping trajectory beforehand, enabling accurate and efficient object manipulation without time loss during execution
Data Source
AI summary
Features are disclosed for an end effector for automated identification and handling of an object. The end effector includes an entangling structure that can be positioned over an entanglement point of an overpackage in which a desired object is location using sensors. Using the location information, the end effector can identify a path to the entanglement location and detect whether the overpackage is engaged by detecting environmental changes at the end effector.