Physics-Engine Pallet Stability Evaluation for Mixed-Item Stacking
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Solution Overview
Problem
Current methods for palletizing dissimilar items are inefficient and prone to instability due to the variety of items, sizes, weights, and shapes, making it challenging for robotics to ensure stable stacks that can be handled by heavy lifting equipment without damaging the items.
Innovation Solution
A system utilizing a physics engine to simulate the interaction of items on a pallet, determining the stability through geometric modeling and machine learning, and adjusting the placement of items to achieve a stable stack by using spacers or reordering items based on real-time data from sensors and vision systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If items are stacked manually by human workers using judgment and intuition, then the palletization process is flexible and adaptable to various item types, but the process is inefficient and time-consuming
Solution Approach 1:
The system creates a digital twin or virtual model of the palletization process that replicates the decision-making logic of human workers. This virtual model simulates various stacking scenarios and evaluates stability using physics engines, allowing automated systems to learn and apply human-like judgment without manual intervention, thereby improving efficiency while maintaining adaptability
Solution Approach 2:
The patent replaces manual mechanical stacking with an automated robotic system guided by a physics-based simulation engine. The system uses computational models to predict stack stability and optimize item placement, substituting human intuition with algorithmic decision-making that processes item attributes and environmental factors to determine optimal stacking configurations
2Stability of the object's composition
If items are stacked automatically without stability evaluation, then the palletization process is fast and efficient, but the resulting pallets are unstable and may collapse or lean
Solution Approach 1:
The system performs preliminary stability evaluation through physics engine simulations before actual palletization occurs. The virtual model tests various stacking configurations and predicts potential instability issues in advance, allowing the system to optimize item placement and prevent collapse before the pallet is physically assembled, ensuring both stability and efficiency
Solution Approach 2:
The patent implements a feedback mechanism where the physics engine continuously evaluates the stability of the virtual pallet model during the planning phase. The system uses sensor data from the actual environment to update and refine the virtual model, comparing predicted stability with real-world conditions and adjusting the stacking plan accordingly to ensure both stability and operational efficiency
3Measurement precision
If a physics engine simulation is used to evaluate pallet stability, then accurate stability prediction is achieved, but computational complexity and processing time increase
Solution Approach 1:
The system applies partial simulation by focusing physics engine calculations only on critical stability factors and key item interactions rather than modeling every detail of the entire pallet. This selective approach maintains high accuracy in predicting potential collapse or leaning while reducing unnecessary computational overhead and system complexity
Solution Approach 2:
The patent dynamically adjusts simulation parameters based on the specific characteristics of items being palletized. The system modifies physics engine settings, such as friction coefficients, gravity effects, and contact forces, to match the actual properties of the items, achieving high measurement precision while avoiding the need for overly complex universal models
Data Source
AI summary
A robotic system is disclosed. The system includes a memory configured to store for each of a plurality of items a set of attribute values representing one or more physical attributes of the item. The system includes one or more processors coupled to the communication interface and configured to use the attribute values as inputs to a physic engine configured to compute the stability of a simulated stack of items comprising at least a subset of the plurality of items.