Grid Transport Detection Using Wide-Angle Vision
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Solution Overview
Problem
Existing storage and fulfilment systems struggle to accurately detect and locate transport devices within a grid framework structure, which can lead to inefficiencies and potential collisions.
Innovation Solution
A method and system that utilize an ultra wide-angle camera and an object detection model trained to detect transport devices on a grid, allowing for the capture and processing of image data to determine the presence and location of transport devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional detection methods are used for transport devices on the grid, then the system structure remains simple, but the detection reliability and accuracy deteriorate
Solution Approach 1:
The patent replaces traditional mechanical or rule-based detection methods with an ultra wide-angle camera system and deep learning-based object detection model. The camera captures images of the grid framework structure, and the trained model automatically identifies and locates transport devices, achieving reliable detection without complex mechanical sensors or multiple coordinated detectors.
Solution Approach 2:
The patent creates a digital copy of the physical grid environment by capturing images with the ultra wide-angle camera and processing them through the object detection model. This digital representation allows the system to monitor transport device positions and detect faults independently, improving reliability without adding physical detection hardware to the transport devices themselves.
2Reliability
If an ultra wide-angle camera and object detection model are implemented, then detection reliability improves, but device complexity increases
Solution Approach 1:
The ultra wide-angle camera serves multiple functions: capturing images of the entire grid framework, enabling the object detection model to identify transport devices, and providing independent verification of device positions. This single camera system replaces what would otherwise require multiple specialized sensors or detection mechanisms, managing complexity while improving reliability.
3Reliability
If transport device positions are monitored independently, then collision prevention capability improves, but information processing requirements increase
Solution Approach 1:
The object detection model continuously processes images from the ultra wide-angle camera to monitor transport device positions in real-time. This independent detection system provides feedback on device locations and states, enabling collision prevention by alerting the control system when devices are in unsafe positions or moving towards potential conflict zones, without requiring excessive information processing from the primary control system.
Data Source
AI summary
A method and system for detecting a transport device in a workspace comprising a grid, the grid comprising a plurality of grid spaces. One or more transport devices are arranged to selectively move in at least one of the X-direction or Y-direction on the tracks, and to handle a container stacked beneath the tracks within a footprint of a single grid space. Image data, representative of an image of at least part of the workspace, is obtained and processed with an object detection model trained to detect instances of transport devices on the grid. It is determined, based on the processing, whether the image includes a transport device of the one or more transport devices. In response to determining that the image includes the transport device, annotation data indicating the transport device in the image is outputted.