Handling System Grip Control with Multi-Algorithm Point Selection
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Solution Overview
Problem
Current handling systems face challenges in accurately determining optimal gripping points for items in cluttered environments, such as warehouses, due to limitations in image processing and gripping point selection criteria, which affects the efficiency and reliability of item picking and placement.
Innovation Solution
A computer-implemented method that utilizes multiple independent gripping point determination algorithms to analyze image data from a detection device, such as cameras or sensors, to identify candidate gripping points, and selects the best point based on specified criteria, allowing for precise and efficient gripping and placement of items using a robot with an end effector.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a single gripping point determination algorithm is used, then the device complexity is low, but the manufacturing precision of gripping point selection is insufficient
Solution Approach 1:
The gripping point determination process is segmented into multiple independent algorithms, each responsible for identifying gripping point candidates from different perspectives or using different criteria. This segmentation allows each algorithm to specialize in specific aspects of gripping point detection, improving overall accuracy while maintaining manageable complexity through modular design.
Solution Approach 2:
Multiple gripping point determination algorithms are merged into a unified system that combines their outputs. The evaluation unit integrates results from different algorithms, selecting the optimal gripping point candidate based on combined assessment. This merging approach leverages the strengths of multiple algorithms to achieve higher precision than any single algorithm could provide alone.
2Manufacturing precision
If multiple gripping point determination algorithms are used, then the manufacturing precision of gripping point selection is improved, but the device complexity increases
Solution Approach 1:
The system dynamically selects and weights different algorithms based on the specific item being handled. The evaluation unit can adjust the contribution of each algorithm depending on the characteristics of the item, making the system adaptable rather than static. This dynamic approach allows the system to optimize performance for different scenarios without requiring manual reconfiguration.
Solution Approach 2:
An evaluation unit acts as an intermediary between multiple gripping point determination algorithms and the final gripping point selection. This intermediary component coordinates the outputs of multiple algorithms, resolves conflicts, and produces a unified decision. The intermediary simplifies the overall system architecture by providing a centralized management layer that handles the complexity of multiple algorithms.
3Reliability
If multiple independent gripping point determination algorithms are used, then the reliability of gripping point determination is improved, but the computing time increases
Solution Approach 1:
The system performs partial evaluation by not requiring all algorithms to complete their full processing for every item. Instead, the evaluation unit can select results from algorithms that provide sufficient confidence, or terminate early when a clearly optimal gripping point is identified. This partial action approach maintains high reliability while reducing unnecessary computing time.
Solution Approach 2:
The system changes parameters such as the number of algorithms activated, the depth of evaluation, or the confidence threshold required for selection, depending on the specific item and operational context. By dynamically adjusting these parameters, the system can balance reliability and computing time, using more algorithms and stricter evaluation only when necessary for difficult-to-grip items.
Data Source
AI summary
A computer-implemented method/product for controlling a handling system, comprising performing one or more control cycles, each control cycle comprising receiving image data that represents an image of at least one portion of an item to be gripped, which image is captured by a detection device, determining a target gripping point on the item for the end effector, comprising analyzing the image data, generating control signals which cause the at least one robot to grip the item at the target gripping point by means of the end effector, wherein determining the target gripping point comprises analyzing the image data by two or more mutually independent gripping point determination algorithms, wherein each of said gripping point determination algorithms determines at least one gripping point candidate, and wherein the gripping point candidates determined by the two or more gripping point determination algorithms form a set Me of gripping point candidates.