Warehouse Traffic Risk Mapping With AMR Sensor Feedback
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Solution Overview
Problem
Conventional methods for traffic planning in dynamic warehouse environments are laborious, prone to human error, and unsafe, especially in large areas with multiple vehicles, as they rely on manual data collection which can lead to inadequate risk assessment and increased observability costs.
Innovation Solution
An automated system using sensor signals from mobile entities like Autonomous Mobile Robots (AMRs) to generate risk maps, integrating data from various sources and processing it with embedded decision-making processes to create a traffic plan that reduces accident risks and enhances environmental observability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual data collection by roaming humans is used, then observability of environmental risks is improved, but labor cost and time consumption increase significantly
Solution Approach 1:
The system enables self-service by deploying sensor-equipped mobile entities (AMRs) that autonomously collect environmental data during their normal operations. The AMRs automatically detect risks, map hazardous areas, and update traffic plans without human intervention, transforming data collection from a manual task into an automated self-service process
Solution Approach 2:
The patent replaces the mechanical system of manual data collection with sensor-based automated detection. Instead of humans physically roaming and observing, the system uses sensors on mobile entities to detect environmental conditions, replacing human labor with automated sensing and processing systems
2Adaptability or versatility
If manual data collection is used, then flexibility in observing different positions is improved, but human error and fatigue reduce reliability
Solution Approach 1:
The system achieves self-service through autonomous AMRs that consistently perform data collection without fatigue or error. The mobile entities automatically navigate, sense environmental conditions, and report data, eliminating human variability and ensuring reliable, repeatable observations across all warehouse positions
Solution Approach 2:
The system implements continuous feedback loops where sensor data from AMRs is processed to identify risks, update traffic plans, and improve safety protocols. This feedback mechanism ensures that observational data is continuously refined and validated, enhancing both reliability and adaptability of the risk assessment system
3Loss of information
If extensive physical area is monitored manually, then complete risk assessment is improved, but cost and complexity increase
Solution Approach 1:
The system achieves universality by using multi-functional AMRs that perform both material handling tasks and environmental monitoring simultaneously. The same mobile entities used for logistics operations also collect safety data, eliminating the need for separate dedicated monitoring systems and reducing overall system complexity
Solution Approach 2:
The patent merges risk assessment functionality with existing logistics operations by integrating sensors and monitoring capabilities into AMRs. This consolidation combines multiple functions (material handling, navigation, and environmental sensing) into a unified system, reducing complexity while maintaining comprehensive risk coverage
Data Source
AI summary
One example method includes receiving, by an integrating hub from each source in a group of sources, a signal and position information, so that multiple signals and respective position information for the signals is received, generating, by the integrating hub, a full risk map, and the full risk map is generated using the signals and their respective position information, and by the integrating hub, making the full risk map available to the sources, wherein the full risk map is usable by the sources to guide operations of the sources in an operating environment.