Variable-Height Robotic Palletization for Stable Mixed-Item Stacking
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Solution Overview
Problem
Current palletization systems struggle with efficiently stacking dissimilar items on pallets due to variations in size, weight, and stability, often requiring manual intervention and leading to unstable pallets.
Innovation Solution
A robotic system equipped with 3D cameras, force sensors, and a library of item types and attributes, which uses cost functions and machine learning to optimize the selection and placement of items on pallets, ensuring stability and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If robotic systems are used to automate palletization, then productivity and consistency improve, but the complexity of handling dissimilar items with varying attributes increases system complexity
Solution Approach 1:
The system dynamically adjusts robotic arm movement parameters (speed, position, orientation) based on real-time sensor data about item attributes. The control system modifies operational parameters to accommodate variations in item size, weight, and shape, enabling automated handling of dissimilar items without requiring complex mechanical reconfiguration
Solution Approach 2:
The robotic system employs dynamic control algorithms that adapt to varying item characteristics during operation. The system continuously receives feedback from sensors and adjusts its palletization strategy in real-time, transforming a static automated system into a dynamic one that can handle diversity in items while maintaining productivity
2Productivity
If items are stacked to maximize pallet capacity, then productivity improves, but pallet stability may deteriorate
Solution Approach 1:
The system incorporates sensors that continuously monitor pallet stability during the stacking process and provide feedback to the control algorithm. Based on this feedback, the system adjusts item placement decisions to maintain stability while maximizing capacity, resolving the contradiction between filling the pallet and keeping it stable
Solution Approach 2:
The control algorithm performs preliminary calculations to determine optimal item placement before actual stacking occurs. By pre-planning the stacking sequence and positions based on item attributes and stability requirements, the system ensures both maximum capacity and stability are achieved from the outset rather than requiring corrective actions later
3Stability of the object's composition
If manual selection and stacking by human workers is used, then pallet stability improves through human judgment, but productivity and consistency deteriorate
Solution Approach 1:
The system replaces human workers with robotic systems equipped with sensors and control algorithms. The robotic system uses vision systems and sensors to detect item attributes and applies computational algorithms to make stacking decisions, substituting human judgment with automated intelligence that achieves both high productivity and consistent stability
Solution Approach 2:
The robotic system autonomously performs the entire palletization process without human intervention. The system independently identifies items, determines optimal placement, executes stacking, and monitors stability, enabling the system to serve itself and achieve both high productivity and stability simultaneously
Data Source
AI summary
A robotic palletization/depalletization system is disclosed. In various embodiments, data associated with a plurality of items to be stacked on or in a destination location is received, and a plan to stack the items on or in the destination location is generated based at least in part on the received data. The generating the plan includes determining a source location from which to pick the item based at least in part on (i) an attribute of the source location, and (ii) a state of a platform or receptacle on which one or more items are to be stacked.