Suction Cup Deformation Modeling for Reliable Robotic Picking
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Solution Overview
Problem
In industrial robotic systems, particularly in product distribution centers, there is a challenge in training machine learning models for robotic picking arms with suction devices to handle diverse product shapes and sizes efficiently, as existing methods require extensive and diverse data sets, which are difficult to obtain, leading to suboptimal performance and potential damage to items.
Innovation Solution
The use of a suction cup model with a three-dimensional spring-lattice to estimate seal quality metrics, allowing for the simulation of picking operations and refinement of machine learning models based on these metrics, enabling the robotic arm to determine optimal contact points and behaviors for various items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If extensive and diverse real-world data sets are collected for training machine learning models, then model performance improves, but data collection time and complexity increase significantly
Solution Approach 1:
The patent creates virtual copies of physical objects (products, suction devices, environments) through 3D modeling and spring-lattice representations. These digital twins enable simulation of picking operations without requiring physical data collection, thereby maintaining model training effectiveness while eliminating time-consuming real-world data gathering.
Solution Approach 2:
The system performs preliminary simulation and evaluation of picking behaviors in virtual environments before executing them in the real world. By pre-training models using simulated data and evaluating candidate contact points through spring-lattice deformation analysis, the system prepares optimal picking strategies in advance, reducing the need for extensive real-world trial and error data collection.
2Reliability
If extensive and diverse real-world data sets are collected for training machine learning models, then model performance improves, but system complexity increases
Solution Approach 1:
The patent replaces complex real-world data collection systems with simplified virtual modeling systems. By creating digital representations of products, suction devices, and environments, the system maintains the ability to train effective machine learning models while dramatically reducing the complexity of data acquisition infrastructure.
Solution Approach 2:
The patent substitutes physical mechanical data collection processes with computational simulation methods. Instead of physically manipulating diverse products to gather training data, the system uses spring-lattice models and virtual simulations to evaluate picking behaviors, thereby reducing system complexity while maintaining model performance.
3Device complexity
If traditional robotic picking methods are used without deformation modeling, then system simplicity is maintained, but seal quality estimation and picking reliability deteriorate
Solution Approach 1:
The patent introduces deformation modeling parameters (spring constants, lattice configurations, contact force parameters) that enable accurate prediction of suction device behavior. By incorporating these physical parameters into the simulation, the system achieves reliable seal quality estimation and picking success prediction while maintaining computational efficiency through optimized spring-lattice implementations.
4Productivity
If machine learning models are trained without seal quality metric evaluation, then training speed is maintained, but picking operation reliability deteriorates
Solution Approach 1:
The patent uses virtual copies and simulations to evaluate seal quality metrics during model training without requiring slow real-world experimentation. By assessing predicted contact points through spring-lattice deformation analysis in silico, the system maintains rapid training speeds while ensuring picking operation reliability through accurate seal quality prediction.
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
This approach enhances the robotic arm's ability to reliably pick and transport items of different shapes and sizes, reducing the likelihood of failure and damage, while minimizing the need for extensive real-world data collection and improving operational efficiency in high-diversity product environments.
Implementation Method 1
modeling deformation and contact between the suction device and the contact point on the item, using at least one three-dimensional spring-lattice to model deformation and contact
Implementation Method 2
model deformation and contact between the suction device and the contact point on the item, using at least one three-dimensional spring-lattice to model deformation
Implementation Method 3
determine a first behavior for retrieving an item by holding the item at a contact point using a suction device
Implementation Method 4
calculate an expected seal quality metric... using the three-dimensional spring-lattice and a fluids model
Data Source
AI summary
Method and apparatus for training a machine learning model for controlling a robotic picking device having a suction device end effector. A robotic control operation and a candidate contact point for holding a first item using a suction device of a robotic picking arm are determined, by processing information describing the first item as an input to a machine learning model. A seal quality metric is estimated for the candidate contact point, based on a predicted deformed n-dimensional shape of the suction device and a n-dimensional shape associated with the first item. One or more weights within the machine learning model are refined based on the estimated seal quality metric.