Swarm Robot Net Printing for Variable Scrap Handling
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Solution Overview
Problem
Existing systems struggle to provide adequate protection and efficient handling of scrap materials with varying dimensions in a multi-machine environment, as one-size-fits-all packaging fails to account for the diverse sizes and types of objects.
Innovation Solution
A system utilizing swarm robots that receive images of scrap material, divide it into zones, identify characteristics, and print nets around or on the material based on these characteristics to ensure stability and strength during transport, employing machine learning to optimize net designs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional packaging methods are used for scrap material, then the handling process is simple, but the protection and stability during transport are insufficient
Solution Approach 1:
The system performs preliminary actions by dividing the scrap material into zones and identifying characteristics before transport. The net is designed and printed in advance based on predicted characteristics, so when material arrives at a zone, it is already prepared for optimal packaging, improving protection without adding complex handling steps.
Solution Approach 2:
The system changes parameters by adapting net design based on identified scrap material characteristics such as size, shape, and density. Machine learning models predict these characteristics and adjust net parameters (mesh size, tension, dimensions) accordingly, providing optimal protection for each material type while maintaining a standardized packaging process.
2Adaptability or versatility
If a standardized net design is used for all scrap material, then the packaging process is efficient, but it cannot adapt to varying sizes and dimensions of different materials
Solution Approach 1:
The system applies local quality by tailoring net design parameters to specific local characteristics of scrap material in each zone. Different zones with different material types receive customized net designs based on their specific size, shape, and density requirements, while the overall packaging process remains efficient through automated parameter adjustment.
Solution Approach 2:
The system performs preliminary analysis of scrap material characteristics using machine learning models before net application. This allows the net design to be pre-optimized for each material type, enabling rapid deployment without sacrificing adaptability to varying dimensions and properties of different scrap materials.
3Productivity
If manual inspection and packaging of scrap material is performed, then the net can be customized for each item, but the processing time and labor costs increase significantly
Solution Approach 1:
The system replaces manual inspection and measurement with machine learning models that automatically predict scrap material characteristics from images and zone data. This substitution maintains high measurement precision for characteristic identification while dramatically increasing processing speed and eliminating manual labor requirements.
Solution Approach 2:
The system enables self-service by allowing the machine learning models to autonomously identify characteristics, determine optimal net designs, and guide the packaging process without human intervention. The system serves itself by using accumulated data to continuously improve its prediction accuracy, maintaining precision while maximizing productivity.
Data Source
AI summary
An embodiment for handling scrap material with swarm robots in a multi-machine environment is provided. The embodiment may include receiving images of scrap material in a multi-machine environment. The embodiment may also include dividing the scrap material into a plurality of zones. The embodiment may further include identifying one or more characteristics of the divided scrap material in at least one zone of the plurality of zones. The embodiment may also include in response to determining the divided scrap material is in a target location in the at least one zone: printing a net around the divided scrap material in the at least one zone in accordance with the one or more characteristics of the divided scrap material; and transporting the net containing the divided scrap material in the at least one zone to a final destination.