Robot Cleaner Stuck Recognition Using 3D Mapping and AI
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Solution Overview
Problem
Conventional robot cleaners struggle to accurately detect and adapt to changes in their environment, particularly when obstacles are removed, leading to incorrect identification of stuck areas and inefficient cleaning routes.
Innovation Solution
A robot cleaner that converts 3D image data and bumper events into surrounding map data, uses a stuck situation recognition model to infer stuck situations, and continuously updates this model through self-validation and re-labeling of training data to improve its navigation and cleaning efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the robot cleaner uses location-based recognition to avoid stuck areas, then it can efficiently navigate previously identified problem zones, but it fails to adapt when environmental conditions change and incorrectly identifies clean areas as stuck zones
Solution Approach 1:
The system performs preliminary classification of bumper events into normal and abnormal types before navigation decisions are made. By pre-processing and categorizing collision data, the robot can distinguish between legitimate stuck situations and normal cleaning operations, enabling adaptive response to environmental changes while maintaining efficient navigation away from actual problem zones.
Solution Approach 2:
The system continuously monitors and analyzes bumper event patterns during cleaning operations, using this feedback to dynamically adjust its stuck area recognition. By incorporating real-time feedback from the classification model, the robot can reclassify areas previously marked as stuck when environmental conditions change, ensuring both productivity and adaptability.
2Device complexity
If the robot cleaner relies on simple bumper event detection, then the system remains simple and computationally efficient, but it cannot accurately distinguish between normal cleaning operations and actual stuck situations
Solution Approach 1:
The bumper detection system is segmented into multiple classification categories (normal bumper events vs. abnormal bumper events). By dividing the detection task into distinct classes, the system achieves higher detection accuracy while maintaining manageable complexity through modular classification logic rather than a single complex detection algorithm.
Solution Approach 2:
The system changes the parameter of bumper event characterization from simple collision detection to classified event types. By transforming raw bumper data into categorized information (normal/abnormal), the system improves measurement precision without proportionally increasing device complexity, as the classification can be implemented through software algorithms.
Data Source
AI summary
A robot cleaner for recognizing a stuck situation through artificial intelligence includes a driving unit to drive the robot cleaner, a sensing unit configured to acquire three-dimensional (3D) image data and a bumper event, a memory configured to store a stuck situation recognition model for inferring the stuck situation of the robot cleaner, and a processor configured to convert the 3D image data and the bumper event into surrounding map image data, infer the stuck situation of the robot cleaner from the 3D image data and the bumper event using the stuck situation recognition model, and control the driving unit according to an inference result.