Robot Fleet Task Orchestration for Demand-Responsive Value Chains
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Solution Overview
Problem
Existing additive manufacturing processes are inefficient, prone to product inconsistency, and unreliable, leading to increased costs and supply chain inefficiencies, while conventional vision technologies struggle with capturing rich object information and dynamic environments, and robotics implementations fail to leverage emerging technologies for optimal robot fleet management.
Innovation Solution
A robot fleet management platform utilizing a governance library, intelligence layer, and digital twins to optimize robot fleet configuration and task ordering, combined with a cloud-based management platform for value chain networks, enabling real-time data processing and adaptive intelligence for demand management and supply chain optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional additive manufacturing processes are used, then manufacturing capability is provided, but efficiency is low and product consistency is poor
Solution Approach 1:
The system implements real-time feedback loops where sensors monitor the additive manufacturing process parameters (temperature, layer quality, material flow) and continuously adjust control settings to maintain optimal conditions, ensuring both high efficiency and consistent product quality across production batches
Solution Approach 2:
The patent replaces manual mechanical adjustment systems with automated digital control systems that use software algorithms to precisely manage manufacturing parameters, enabling better consistency and efficiency through programmable precision rather than mechanical variability
2Loss of information
If more data is collected from IoT sensors and systems, then more insights can be obtained, but complexity and volume overwhelm users
Solution Approach 1:
The system extracts only the most critical and actionable data points from the vast array of IoT sensor inputs, filtering out redundant information and presenting distilled insights through dashboards that highlight key performance indicators and anomalies without overwhelming users with raw data volume
Solution Approach 2:
The patent introduces an intermediate layer of data processing and analytics platforms that act as mediators between raw IoT data sources and end users, transforming complex multi-source data into simplified visualizations and actionable recommendations that reduce perceived complexity while maintaining information richness
3Adaptability or versatility
If robot fleet management uses traditional methods, then basic operations are performed, but adaptability to dynamic environments is limited
Solution Approach 1:
The system implements dynamic task allocation and routing algorithms that continuously adapt robot fleet assignments based on real-time environmental conditions, task priorities, and robot status, enabling the fleet to flexibly respond to changing requirements rather than following static pre-programmed sequences
Solution Approach 2:
The patent employs predictive analytics and digital twin simulations to pre-plan and optimize robot fleet configurations for anticipated tasks and environmental conditions, allowing the system to proactively adjust automation strategies before actual operations begin, enhancing both adaptability and intelligent automation
Data Source
AI summary
A robot fleet platform for preparing a job request includes one or more processors configured to execute instructions. The instructions include a job request ingestion system configured to receive job content relating to at least one of picking, packing, moving, storing, warehousing, transporting or delivering of items in a supply chain. The job content includes an electronic job request and related data. The instructions include a job content parsing system configured to apply filters to the received job content to identify candidate portions thereof for robot automation. The instructions include a fleet intelligence layer that activates a set of intelligence services to process terms in the candidate portions of the job content and receive therefrom at least one recommended robot task and associated contextual information. The instructions include a demand intelligence layer that provides real time information relating to a parameter of demand for the items in the supply chain.