Mobile Robot Charging Dock Identification Using Infrared and Depth
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Solution Overview
Problem
Existing methods for identifying charging devices by mobile robots suffer from low accuracy due to light intensity and environmental interference, making it difficult for robots to reliably detect and dock with charging stations.
Innovation Solution
The method involves using a depth camera to capture infrared and depth images, identifying suspected charging device areas based on high reflectivity, and verifying these areas through geometric information from depth images, combined with RGB image analysis to confirm the presence and location of charging devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If vision-based navigation and infrared sensors are used to identify charging devices, then autonomous charging capability is achieved, but identification accuracy deteriorates due to light intensity and environmental interference
Solution Approach 1:
The identification process is segmented into multiple independent stages: initial detection using infrared images to find high-reflectivity markers, verification using depth images to check geometric constraints, and final confirmation using RGB images. This segmentation allows each stage to focus on specific aspects of identification, improving overall accuracy while maintaining automation.
Solution Approach 2:
A depth camera is introduced as an intermediary device between the mobile robot and the charging device. It provides geometric information that acts as a mediator to verify the detected charging device areas, filtering out false positives caused by environmental interference before final identification.
2Measurement precision
If multiple verification methods are used to improve identification accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
Multiple types of cameras (infrared, depth, and RGB) are merged into a single integrated identification system. The infrared camera detects high-reflectivity markers, the depth camera verifies geometric constraints, and the RGB camera provides color information. By merging these functions, the system achieves high accuracy without requiring separate independent systems.
Solution Approach 2:
The system transitions from two-dimensional image analysis to three-dimensional verification by incorporating depth information. The depth camera provides height relative to the depth camera as an additional dimension, enabling geometric verification that filters false positives while adding minimal complexity compared to pure 2D image processing.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly improves the accuracy of charging device identification by filtering out false positives and ensuring geometric alignment, enabling more reliable autonomous charging of mobile robots.
Implementation Method 1
a marker is provided on the charging device, and a reflectivity of the marker is greater than a first specified value
Data Source
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AI summary
The present application provides a method of identifying a charging device, a mobile robot and a system for identifying a charging device. The method of identifying the charging device may include: capturing an infrared image and a depth image of a current field of view with a depth camera; determining, according to the infrared image, whether there are one or more suspected charging device areas that satisfy first specified conditions; in response to determining there are the one or more suspected charging device areas, determining, according to the depth image, whether there is a target charging device area whose height relative to a depth camera is within a specified range in the one or more suspected charging device areas; in response to determining there is the target charging device area in the one or more suspected charging device areas, identifying the charging device according to the target charging device area. The first specified conditions indicate that a gray-scale value of each of pixels in an area is greater than a second specified value, and a number of the pixels in the area is greater than a third specified value.