Apparatus for localizing cleaning robot, cleaning robot, and controlling method of cleaning robot
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Solution Overview
Problem
Existing cleaning robot localization methods, such as Monte Carlo localization, are inefficient when the number of particles is small, leading to increased calculation time and unnecessary movement, and may fail to accurately determine the traveling direction, resulting in missed cleaning areas.
Innovation Solution
A cleaning robot system that includes a data acquisition unit, a local map acquisition unit, and a processor to quickly and accurately determine the current position and traveling direction by matching actual sensor data with a local map, using a ray casting method and angle histogram analysis to classify straight lines and calculate the main segment angle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of particles is increased to improve localization accuracy, then measurement precision is improved, but calculation time is increased
Solution Approach 1:
The system performs preliminary actions by extracting straight lines from the map before localization, organizing them into angle histograms, and identifying main segment angles in advance. When localization is needed, the robot only needs to compare sensor data with pre-processed map features rather than processing the entire map, significantly reducing calculation time while maintaining accuracy
Solution Approach 2:
The invention extracts only the essential features (straight lines and their angles) from the complex map data, separating these key localization features from unnecessary map details. This extraction creates a simplified representation that retains localization accuracy while reducing computational complexity
2Adaptability or versatility
If the cleaning robot moves to an arbitrary position by user, then adaptability is improved, but localization accuracy deteriorates due to unnecessary movement
Solution Approach 1:
The system uses feedback by continuously comparing sensor data (distances to walls/objects) with map data to verify and correct the robot's position estimate. After the robot is moved to an arbitrary position, the localization unit immediately compares current sensor readings with the pre-processed map features to accurately determine the new position without requiring movement for recalibration
Solution Approach 2:
The invention replaces the mechanical localization approach (moving the robot to converge particles) with an information-based approach (comparing sensor data with pre-processed map features). This substitution eliminates unnecessary physical movement while achieving accurate localization through data processing
3Ease of operation
If the cleaning robot determines traveling direction using conventional methods, then ease of operation is maintained, but productivity decreases due to missed cleaning areas
Solution Approach 1:
The system performs preliminary action by pre-processing the map to extract straight lines and calculate main segment angles before the robot needs to determine its traveling direction. This pre-computation allows the robot to quickly align with the main cleaning direction by simply following the pre-calculated angle, improving productivity without complicating the control operation
Solution Approach 2:
The invention changes the parameter used for direction determination from generic navigation angles to specific main segment angles derived from the map's geometric features. By using the angle of the longest straight line segment in the cleaning area, the system optimizes the traveling direction parameter to maximize cleaning coverage efficiency
Data Source
AI summary
A cleaning robot includes a data acquisition unit that acquires actual sensor data by measuring a distance from a current position to an object to be measured; a local map acquisition unit that acquires a local map by scanning the vicinity of the current position based on an environmental map stored in advance; and a processor that determines coordinates of the current position for the local map by performing matching between the local map and the actual sensor data, and determines a traveling direction based on the current position by calculating a main segment angle of a line segment existing in the local map.