Robot Task Assignment for Automated Quality Assessment
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Solution Overview
Problem
Human quality assessment in task management systems is manual, prone to errors, time-intensive, and costly, with insufficient time to determine if tasks are completed successfully and to the required quality.
Innovation Solution
A system utilizing robots for automated quality assessment, where a robot management system generates robot tasks based on human task completion data, filters tasks by robot capabilities, and assigns tasks efficiently to available robots, allowing robots to perform quality checks during or after completing primary tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual quality assessment is used, then human operators can perform quality checks, but it is time-intensive and costly
Solution Approach 1:
The patent replaces manual quality assessment with an automated image recognition system using machine learning models. The system captures images of completed tasks and automatically analyzes them to determine quality, eliminating the need for manual inspection by human operators. This substitution of mechanical/manual processes with automated computational systems directly resolves the contradiction by maintaining assessment accuracy while dramatically reducing time consumption.
2Reliability
If more quality checks are performed, then quality control improves, but productivity decreases due to insufficient time
Solution Approach 1:
The system enables continuous quality assessment by automatically analyzing task images as they are completed, without interrupting the workflow. The image recognition system operates continuously in the background, processing images and providing quality feedback in real-time or near-real-time. This allows quality control to be maintained at every stage of task completion without pausing production, thereby maintaining both high quality standards and productivity levels.
3Reliability
If manual quality assessment is used, then quality checks can be performed, but it is prone to errors
Solution Approach 1:
The system implements a feedback mechanism where the machine learning model continuously learns from annotated images and quality assessment results. The system receives feedback in the form of labeled images with quality ratings, uses this feedback to retrain and improve its models, and applies the improved models to subsequent assessments. This closed-loop feedback system ensures consistent and progressively improving quality assessment accuracy, eliminating human error while maintaining reliability.
4Reliability
If quality assessment is performed thoroughly, then quality control improves, but cost increases
Solution Approach 1:
The system performs self-service quality assessment by automatically capturing, analyzing, and evaluating task images without requiring human intervention. The machine learning models independently process images and determine quality metrics, eliminating the need to pay human operators for quality inspection work. This self-service approach maintains high quality control standards while significantly reducing the labor costs associated with manual quality assessment.
Data Source
AI summary
Disclosed is a robot management system comprising a control circuit configured to receive data indicative of a worker task being completed by a human from a human task management system coupled to the robot management system, generate a new robot task based on the data, add the new robot task to a robot task queue, detect an available robot, determine capabilities of the available robot, and filter the robot task queue based on the capabilities of the available robot to generate a group of robot tasks, the group being a subset of the robot task queue and each task in the group can be performed by the available robot. The control circuit is further configured to determine a priority group comprising a priority for the available robot for each task in the group and assign a robot task from the group to the available robot based on the priority group.