Robot Fleet Task Ledger for Verified Job Completion Tracking
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Solution Overview
Problem
Current additive manufacturing and supply chain management systems face inefficiencies, product inconsistencies, and unreliability in 3D printing, leading to quality issues and increased costs, while conventional machine vision systems struggle with capturing rich data and recognizing objects in dynamic environments.
Innovation Solution
A robot fleet management platform with a governance-enabling intelligence layer that includes artificial intelligence services, machine learning, and digital twins, integrated with a cloud-based management platform for optimizing additive manufacturing and supply chain operations, enabling smarter product design, monitoring, and automation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional additive manufacturing systems are used, then manufacturing capability is provided, but manufacturing precision and product consistency deteriorate due to quality issues and unreliability
Solution Approach 1:
The patent implements a feedback mechanism where machine vision systems capture images of printed parts, AI services analyze defects and characteristics, and the results are fed back to adjust printing parameters. This closed-loop control ensures consistent quality and reliability by continuously monitoring and correcting manufacturing variations in real-time.
Solution Approach 2:
The patent replaces conventional mechanical quality inspection methods with AI-based machine vision systems. Instead of relying on manual measurement or simple optical sensors, the system uses deep learning models to automatically detect defects, measure dimensions, and assess part quality, thereby improving manufacturing precision and reliability.
2Loss of information
If more data is collected from IoT sensors and smart devices, then insights opportunities increase, but data complexity and volume overwhelm users making decision-making difficult
Solution Approach 1:
The patent introduces AI services as an intermediary layer between raw IoT data and users. The AI services automatically process, analyze, and synthesize data from multiple IoT sensors, converting complex raw data into actionable insights and recommendations. This mediator handles data complexity internally while presenting simplified information to users for decision-making.
Solution Approach 2:
The system implements self-service through automated AI analysis that independently processes sensor data without requiring user intervention. The AI services automatically detect patterns, identify anomalies, and generate insights from collected data, enabling the system to serve itself in transforming raw data into meaningful information while reducing the burden on users.
3Measurement precision
If machine vision systems are used to capture manufacturing data, then object detection capability is provided, but recognition accuracy deteriorates in dynamic environments
Solution Approach 1:
The patent implements dynamic adaptation by training AI models to recognize objects and defects under varying conditions such as different lighting, angles, and positions. The system continuously adjusts its recognition algorithms to accommodate dynamic manufacturing environments, maintaining high measurement precision even when objects move or environmental conditions change.
Solution Approach 2:
The patent creates a universal machine vision system that can handle multiple types of objects, defects, and manufacturing scenarios through a single AI platform. The system is designed to be versatile across different additive manufacturing processes and part types, maintaining accurate recognition regardless of the specific dynamic conditions or object characteristics.
Data Source
AI summary
A system includes a job definition data structure, based on a request for a robotic fleet to perform a job, that defines tasks for the job. A robotic fleet configuration data structure corresponding to the job is based on the tasks and a fleet resource inventory. The robotic fleet configuration data structure assigns fleet resources from the fleet resource inventory to the tasks defined in the job definition data structure. The system provisions the respective fleet resource based on the configuration data structure, deploys the robotic fleet, monitors task completion status, and configures a distributed ledger for tracking task or job completion. The system, in response to completion of a task, updates a set of job completion data in the distributed ledger that reflects at least one of robotic task completion data, allocation of robotic resources to parties associated with the job, and actions triggered in response to the completion.