Mobile Robot Image Change Detection Using Reference Model Alignment
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Solution Overview
Problem
Existing robotic systems are inefficient and cost-prohibitive in detecting changes within dynamic environments due to their dependence on static locations, lighting conditions, and the need for scene-specific training, and manual detection is time-consuming and error-prone.
Innovation Solution
A system that utilizes a mobile robot to obtain sensor data, aligns the robot's position and orientation with a reference model, and performs change detection using neural networks to identify anomalies in real-time, enabling efficient and automated change detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If robotic systems use static locations and scene-specific training for change detection, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent applies universality by using a single mobile robot equipped with sensors that can detect changes across multiple locations and scenes without requiring separate static sensors or scene-specific training for each location. The robot performs change detection by comparing sensor data from current locations with reference images from a database, enabling one system to handle multiple functions and locations universally.
Solution Approach 2:
The patent applies dynamics by transitioning from static sensor locations to a mobile robot that moves dynamically through the environment. The robot can navigate to different locations, capture images, and perform change detection adaptively. The system also dynamically retrieves appropriate reference images from a database based on the current location, enabling flexible and adaptive change detection without fixed installations.
2Reliability
If multiple static sensors are deployed for change detection, then reliability is improved, but loss of energy and cost increase
Solution Approach 1:
The patent applies merging by consolidating multiple static sensor functions into a single mobile robot. Instead of deploying numerous static sensors across different locations, the robot combines navigation, sensing, and change detection capabilities in one mobile platform. This reduces the total number of sensors needed while maintaining reliable change detection through the robot's ability to visit multiple locations and compare images against a reference database.
3Measurement precision
If manual change detection is performed, then measurement precision is maintained, but productivity decreases
Solution Approach 1:
The patent applies mechanics substitution by replacing manual change detection with an automated computer vision system. The mobile robot captures images and the system automatically compares them with reference images from a database using image processing algorithms. This eliminates the need for manual inspection while maintaining detection accuracy, significantly improving productivity by automating the change detection process.
4Measurement precision
If scene-specific training is used for change detection, then measurement precision is improved, but adaptability decreases
Solution Approach 1:
The patent applies preliminary action by pre-collecting reference images for multiple locations and storing them in a database before the mobile robot performs change detection. Instead of training the system for each specific scene when needed, the reference images are captured and stored in advance. When the robot visits a location, it retrieves the appropriate reference image from the database, enabling rapid adaptation to different scenes without requiring on-the-spot training.
Data Source
AI summary
Systems and methods are described for detecting changes at a location based on image data by a mobile robot. A system can instruct navigation of the mobile robot to a location. For example, the system can instruct navigation to the location as part of an inspection mission. The system can obtain input identifying a change detection. Based on the change detection and obtained image data associated with the location, the system can perform the change detection and detect a change associated with the location. For example, the system can perform the change detection based on one or more regions of interest of the obtained image data. Based on the detected change and a reference model, the system can determine presence of an anomaly condition in the obtained image data.