Robot Docking Navigation Using HOG and Reference Markers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing cleaning systems face challenges in locating charging stations due to large construction, technical complexity, and issues with visual or signal connection loss between the cleaning robot and the charging station, especially at greater distances.
Innovation Solution
A cleaning system that combines the Histogram of Oriented Gradients (HOG) method and a reference marker method to navigate a self-driving cleaning robot to a charging station, allowing for compact design and reliable location even at distances where direct visual contact is lost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If visible reference markers and optical detecting facilities are used for navigation, then the cleaning robot can locate the charging station, but the system becomes technically complex and expensive
Solution Approach 1:
The patent uses visual copying of the charging station's appearance features and reference marker patterns to create a navigational model. The cleaning robot captures images of the charging station and its reference markers, processes these visual copies through image recognition algorithms, and uses the extracted feature information for navigation without requiring complex direct sensor-to-actuator connections.
Solution Approach 2:
The patent replaces complex mechanical positioning systems and direct signal connection mechanisms with vision-based navigation. Instead of using mechanical encoders, complex signal emitters, and detectors, the system uses camera-based visual recognition and computational image processing to achieve accurate localization and navigation.
2Reliability
If direct visual connection is required between cleaning robot and charging station, then navigation can be achieved, but the system fails when visual connection is lost at greater distances
Solution Approach 1:
The patent transitions from direct line-of-sight visual connection to multi-dimensional navigation capability. By incorporating visual recognition of reference markers at various distances, image processing algorithms that work across different scales, and path planning that considers multiple spatial dimensions, the system can navigate effectively whether the charging station is nearby or far away, maintaining reliability across distance variations.
Solution Approach 2:
The patent implements preliminary visual learning and mapping actions. The cleaning robot预先 captures and processes images of the charging station and reference markers to build a visual model before actual navigation begins. This preliminary visual recognition and feature extraction enables the robot to navigate reliably even when the charging station is at greater distances, as the visual connection has already been established and processed in advance.
3Area of stationary object
If compact charging station design is implemented, then space is saved, but visual and signal connection to the robot becomes more difficult
Solution Approach 1:
The patent uses asymmetric placement and design of reference markers on the compact charging station. Rather than symmetric arrangements that might be space-consuming, the reference markers are positioned asymmetrically in optimized locations that maximize their detectability from various angles and distances. This asymmetric configuration allows the charging station to maintain a compact form factor while ensuring reliable visual detection by the cleaning robot.
4Reliability
If multiple navigation methods are combined, then navigation reliability improves, but the computing requirements and system complexity increase
Solution Approach 1:
The patent implements partial use of multiple navigation methods based on situational requirements. Rather than continuously running all navigation algorithms at full capacity, the system selectively activates appropriate methods based on distance to charging station, current navigation phase, and detection confidence levels. This partial action approach maintains high navigation reliability while reducing unnecessary computational energy consumption during routine operations.
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
Enables precise and cost-effective navigation to charging stations, supporting self-learning and adaptable navigation under varying conditions, while allowing for a more compact and less conspicuous charging station design.
Implementation Method 1
the charging station has a reference marker 4 which is visible and by way of which the cleaning robot 2 can be controlled to move towards the charging station 3
Data Source
AI summary
A cleaning system includes a self-driving cleaning robot and a charging station with a reference marker. The cleaning robot has an optical detection unit by way of which it detects the reference marker. The cleaning robot has a computing unit that is connected for communication purposes to the detection unit and a control unit for controlling the cleaning robot. The computing unit controls the approach of the cleaning robot to the charging station in a distance-dependent and in a detection-dependent manner using a histogram of oriented gradients and using the reference marker. A HOG method and a reference marker method can thus be used in combination for improved navigation.