AME Risk Monitoring With Edge Alerts for Cluttered Workspaces
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Solution Overview
Problem
Unstructured and cluttered factory and warehouse environments pose challenges for autonomous mobile entities (AMEs) due to difficulty in perception and comprehension, exacerbated by excessive noise and generic safety alarms that do not target individuals at risk.
Innovation Solution
A centralized risk monitoring and mitigation system using IoT technology and edge computing provides individually tailored warnings and safe maneuvering services by collecting sensor data from infrastructure and AMEs to assess risks and issue targeted alerts to users, adjusting volume, frequency, and direction based on proximity and severity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized control systems are implemented to improve safety monitoring, then safety and reliability improve, but device complexity increases
Solution Approach 1:
The patent introduces an edge computing system as an intermediary between AMEs and centralized control. This edge system processes sensor data locally, performs risk assessments, and generates warnings, thereby distributing computational complexity and reducing the burden on both individual AMEs and central systems while maintaining improved safety monitoring.
Solution Approach 2:
The control system is segmented into multiple components: individual AMEs with their own sensors, an edge computing system for local processing, and centralized monitoring. This segmentation allows each component to handle specific tasks independently, improving overall system reliability while managing complexity through modular architecture.
2Ease of operation
If generic safety alarms are used to warn all users, then implementation simplicity is maintained, but effectiveness decreases because warnings are not targeted to individuals at risk
Solution Approach 1:
The warning system provides local quality by tailoring warnings to specific users based on their individual risk levels. The edge computing system assesses risk for each user-AME interaction and generates customized warnings with appropriate volume, frequency, and direction, ensuring that each user receives relevant safety information rather than generic alerts.
3Loss of time
If edge computing is deployed to process data locally and reduce latency, then response time improves, but device complexity and infrastructure requirements increase
Solution Approach 1:
The edge computing system performs preliminary actions by pre-processing sensor data and conducting risk assessments locally before results are needed by AMEs or users. This advance processing reduces latency in critical safety responses while distributing computational load across the infrastructure, making the complexity manageable through proactive rather than reactive processing.
Data Source
AI summary
Various systems and methods for detecting risk conditions in a physical workspace. An apparatus can include an interface to receive smart sensor signals from at least one autonomous mobile entity (AME) in the physical workspace. The apparatus can also include processing circuitry coupled to the interface to detect a risk condition associated with the at least one AME, based on the smart sensor signals, relative to a user device associated with a human present in the physical workspace. The processing circuitry can also detect a direction of the risk condition relative to the user device and cause a notification to the first user device. The notification can indicate the direction of the risk condition relative to the user device. Other systems, methods and apparatuses are described.