Robot Cleaner Corridor Delocalization Detection for Navigation
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Solution Overview
Problem
Self-propelled cleaning devices face challenges in maintaining accurate position determination due to delocalization situations, particularly in corridors where sensors provide limited information, leading to delayed detection of position loss and imprecise navigation.
Innovation Solution
A method that identifies a straight main line in measurement data and analyzes structures orthogonal to it, using a threshold value to rapidly detect delocalization situations, allowing timely switching to alternative positioning methods like odometry to prevent position loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If covariance of particle filter distribution is used to detect delocalization situation, then delocalization can be detected, but detection is time-consuming and position is lost by the time of detection
Solution Approach 1:
The patent replaces the complex particle filter covariance calculation with a simplified geometric analysis of sensor measurement data. Instead of using probabilistic methods requiring extensive computation, the invention analyzes the spatial distribution of detected features directly to identify corridor configurations, achieving rapid delocalization detection without position loss.
Solution Approach 2:
The patent continuously monitors the geometric arrangement of sensor features and pre-identifies corridor-like patterns before delocalization occurs. By maintaining ready detection of spatial configurations that precede delocalization, the system can switch to alternative positioning methods proactively, preventing position loss rather than reacting after it occurs.
2Measurement precision
If sensor measurement data is used for positioning, then position can be determined, but in corridors with parallel walls and no other objects, position along the corridor length becomes undeterminable
Solution Approach 1:
The patent implements dynamic switching between different positioning methods based on the detected environment type. When corridors are detected through geometric analysis of sensor data, the system transitions from sensor-based positioning to odometry-based positioning, maintaining accurate position determination across diverse environments including corridors where sensor data alone is insufficient.
3Extent of automation
If cleaning device navigates autonomously in unknown environment, then mapping can be performed, but position determination becomes impossible when only parallel walls are within sensor range
Solution Approach 1:
The patent prepares for potential position information loss by continuously monitoring sensor data geometry for corridor patterns before delocalization occurs. By detecting the impending loss of position determinability through geometric analysis and proactively switching to odometry-based positioning, the system cushions against complete position information loss and maintains autonomous navigation capability.
Data Source
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AI summary
A self-propelled cleaning device and a method for operating a self-propelled cleaning device are proposed, wherein the cleaning device navigates autonomously in an environment and determines its position in the environment using a navigation device. The cleaning device performs a detection method for detecting a delocalization situation. A delocalization situation is a situation in which a loss of determinability of the position along at least one axis is imminent or has occurred and/or in which the cleaning device is located in a corridor or is at risk of entering a corridor.In the detection method, a straight main line is identified in measurement data of the navigation device and analyzed to determine whether the measurement data contain a structure extending transversely to the main line, wherein an extension of structures in the measurement data orthogonal to the main line is determined and compared with a particularly predefined limit value, and that it is evaluated as a delocalization situation if the measurement data do not contain a structure whose extension is greater than the limit value.