Suction Gripper Pressure Sequencing for Pick Quality Detection
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Solution Overview
Problem
Existing sorting systems lack an efficient mechanism to evaluate the success or failure of pick actions by diverting mechanisms, particularly in complex systems with multiple mechanisms and objects with non-uniform surfaces.
Innovation Solution
The implementation of a pressure sequence analysis system that correlates the pressure of airflow through a gripper mechanism over time with representative pressure sequences to determine pick quality, combined with a machine learning approach to optimize pick locations and improve capture success rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple diverting mechanisms and vision systems are used to handle complex sorting tasks, then the sorting capability and versatility are improved, but tracking the success or failure of pick actions becomes more difficult and the system complexity increases
Solution Approach 1:
The patent implements a feedback mechanism that monitors pick actions by multiple diverting mechanisms and provides performance data back to the control system. This allows the system to track and evaluate the success or failure of each pick action, enabling learning and optimization of sorting behaviors while managing the complexity of multiple mechanisms through centralized monitoring and control.
2Measurement precision
If AI systems with machine learning are used to recognize objects, then the sorting accuracy and adaptability are improved, but the need for feedback mechanisms to evaluate pick performance increases system complexity
Solution Approach 1:
The patent integrates a feedback mechanism that works in conjunction with AI object recognition systems. The feedback system evaluates the actual pick performance and provides performance data that can be used to refine and optimize the machine learning models, creating a closed-loop system that improves accuracy while managing complexity through automated evaluation and learning processes.
Solution Approach 2:
The system enables the AI/ML components to learn and improve autonomously by processing feedback data about pick successes and failures. The machine learning models automatically adjust their parameters and behaviors based on observed performance, reducing the need for manual system configuration and simplifying the overall feedback mechanism architecture.
3Measurement precision
If pressure sequence analysis is used to evaluate pick quality, then the measurement precision of pick success is improved, but the device complexity increases due to additional sensors and processing
Solution Approach 1:
The patent replaces complex mechanical evaluation systems with pressure-based sensing and computational analysis. Instead of using multiple mechanical sensors and complex processing systems, the invention uses pressure sequence data from the diverting mechanism's operation to infer pick quality, substituting mechanical complexity with simpler pressure measurement and algorithmic analysis.
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 solution enables accurate evaluation of pick operations, improves the success rate of object captures, and facilitates the identification of optimal pick locations, thereby enhancing the overall performance and efficiency of sorting systems.
Implementation Method 1
A vacuum-based suction gripper mechanism is actuated to perform a pick operation on a target object
Implementation Method 2
A pressure sequence that describes the pressure of vacuum airflow through the suction gripper mechanism over time during the pick operation is recorded
Data Source
AI summary
Pick quality determination is disclosed, including: using a pressure meter to sense a pressure associated with an airflow through a gripper mechanism of a diverting mechanism over time during a pick operation on a target object; storing the sensed pressure associated with the airflow through the gripper mechanism over time as a pressure sequence associated with the pick operation on the target object; and correlating the pressure sequence with representative pressure sequences associated with corresponding pick quality types to determine whether the pick operation on the target object was successful or not.