Robotic Bagging via Dynamic Image Analysis
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Solution Overview
Problem
The inefficiency of manually placing items into bags for grocery order pickup due to the irregular and inconsistent shape of bags, which affects the settling location and orientation of items, making the process time-consuming and labor-intensive.
Innovation Solution
A system utilizing image capture devices to generate dynamic placement data for non-rigid containers, where a robotic device places items based on assigned locations and orientations within the container, ensuring efficient bagging without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual bagging is used to accommodate irregular bag shapes, then flexibility in handling non-rigid containers is improved, but labor intensity and time consumption increase significantly
Solution Approach 1:
The patent replaces manual mechanical bagging operations with an automated robotic system that uses computer vision and machine learning algorithms. The robotic device captures images of the bag interior, analyzes available space using neural networks, and automatically places items without human intervention, thereby eliminating labor intensity while maintaining adaptability to irregular bag shapes.
Solution Approach 2:
The system enables the bagging process to be self-regulating through real-time image analysis and dynamic placement algorithms. The robotic device autonomously adjusts item placement based on detected bag geometry and existing item arrangements, allowing the system to adapt to each unique bag configuration without requiring manual intervention or pre-programmed instructions for every scenario.
2Productivity
If items are placed randomly into bags, then the bagging process is simple and quick, but item settling becomes unpredictable and bag capacity is not optimized
Solution Approach 1:
The system performs preliminary analysis of the bag interior geometry and available space before placing any items. The image analysis component captures images of the empty or partially-filled bag and uses neural networks to identify optimal placement zones, ensuring that subsequent item placement is both efficient and precise, maximizing bag capacity while maintaining speed.
Solution Approach 2:
The patent implements a feedback loop where the robotic device continuously captures images of the bag interior during the bagging process, analyzes the current item arrangement and available space, and adjusts placement strategies in real-time. This closed-loop control ensures optimal item settling and bag capacity utilization while maintaining high bagging speed through automated adaptation.
3Extent of automation
If automated robotic bagging is implemented, then labor intensity is reduced, but system complexity and initial setup requirements increase
Solution Approach 1:
The system uses computer vision to create digital copies (images) of the physical bag interior and item arrangements. These visual representations are processed by neural networks to determine optimal placement strategies, replacing complex mechanical sensing and control systems with optical copying and computational analysis, thereby reducing overall system complexity while maintaining high automation levels.
Solution Approach 2:
The patent transforms the physical bagging problem into a computational problem by changing parameters from mechanical measurements to visual data analysis. The system uses image processing and neural network parameters to model bag geometry and item placement, allowing complex automation to be achieved through software-based parameter optimization rather than complex hardware control mechanisms.
Data Source
AI summary
Examples provide a system and method for autonomously placing items into non-rigid containers. An image analysis component analyzes image data generated by one or more cameras associated with picked items ready for bagging and/or a non-rigid container, such as, but not limited to, a bag. The image analysis component generates dynamic placement data identifying how much space is available inside the bag, bag tension, and/or contents of the bag. A dynamic placement component generates a per-item assigned placement for a selected item ready for bagging based on a per-bag placement sequence and the dynamic placement data. Instructions, including the per-item assigned placement designating a location within the interior of the non-rigid container to the selected item and an orientation for the selected item after bagging, is sent to at least one robotic device. The robotic device places the selected item into the non-rigid container in accordance with the instructions.