Vision-Guided Depalletizer for Skewed Mixed Pallet Layers
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Solution Overview
Problem
Conventional depalletizing systems struggle with efficiently and accurately removing products from pallets, especially when dealing with mixed cases or heterogeneous pallets, due to variations in layer pose and pallet quality.
Innovation Solution
A vision-assisted robotized depalletizing system that uses a 3D time of flight camera to generate real-time 3D imaging of pallet layers, enabling the robot to compensate for variations in layer pose and pallet quality, and a layer depalletizing tool with a grip system that can adapt to different pallet layer configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional depalletizing systems are used, then the basic function of removing products from pallets is achieved, but accuracy and efficiency deteriorate due to variations in layer pose and pallet quality
Solution Approach 1:
The system performs preliminary 3D imaging and analysis of the pallet load structure before depalletizing begins. The vision system captures images of the entire pallet load and identifies the position and orientation of each layer in advance, allowing the robot to pre-calculate optimal pickup positions and orientations for each layer, thereby ensuring high positioning accuracy despite variations in layer pose.
Solution Approach 2:
The system dynamically adjusts the robot's grip position and orientation based on real-time vision feedback. As each layer is removed, the vision system re-images the updated pallet structure and recalculates the next pickup parameters, allowing the system to adapt to changing layer configurations and maintain positioning accuracy throughout the depalletizing process.
2Productivity
If conventional depalletizing systems are used, then the basic function is achieved, but productivity deteriorates due to inefficiency in handling mixed cases
Solution Approach 1:
The system implements continuous vision feedback during the depalletizing process. The vision system captures images of the pallet load at multiple stages, and the controller uses this feedback information to automatically adjust pickup positions, orientations, and sequences. This closed-loop control enables high productivity by eliminating manual intervention while handling the complexity of mixed cases through automated image analysis and path planning.
Solution Approach 2:
The system replaces manual mechanical operations with automated vision-guided robotic manipulation. Instead of relying on fixed mechanical depalletizing mechanisms that struggle with varied pallet configurations, the patent uses a robot equipped with vision sensing and intelligent control to dynamically determine and execute pickup actions, significantly improving productivity for mixed case handling.
3Measurement precision
If vision-assisted robotized depalletizing is implemented, then positioning accuracy improves, but device complexity increases due to addition of vision system and 3D imaging
Solution Approach 1:
The vision system serves multiple functions: it captures 3D images of the pallet load structure, identifies the position and orientation of each layer, calculates optimal robot pickup positions, and provides feedback for dynamic adjustment. By consolidating these multiple functions into a single integrated vision system, the patent achieves high measurement precision without proportionally increasing system complexity.
Solution Approach 2:
The controller acts as an intermediary that processes vision system data and translates it into robot control commands. The controller receives 3D images from the vision system, analyzes layer configurations, calculates optimal pickup parameters, and generates corresponding robot motion commands. This intermediary processing layer manages the complexity by automating the transformation from visual data to actionable control signals.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system achieves efficient and accurate depalletizing by ensuring precise positioning and orientation of the robot's grip relative to the pallet layers, even with skewed or uneven layers, thereby improving storage, sortation, and transport efficiencies in distribution centers.
Implementation Method 1
A vision-assisted robotized depalletizing system that uses a 3D time of flight camera to generate real-time 3D imaging of pallet layers
Data Source
AI summary
A depalletizer having a pallet station for receiving a pallet load of cases disposed in pallet load layers, a robot with an end effector having a grip to grip and pick at least one of the layers and having a grip engagement interface defining a predetermined layer engagement position and orientation for the layer(s), relative to the depalletizing end effector, a vision system to image the pallet load of cases and generate at least one image of a top portion of the layer(s) independent of robot motion, and a controller that receives the image(s) and effects determination of a layer position and orientation of the layer(s) relative to the predetermined layer engagement position and orientation of the grip engagement interface, and the controller is operably coupled to the robot so as to position the grip and capture and hold the layer(s) with the grip at the grip engagement interface.