Robot cleaner and method for operating same
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Solution Overview
Problem
Existing robot cleaners face challenges in efficiently navigating and cleaning complex environments with obstacles and uneven floors, leading to prolonged cleaning times due to reliance on sensors that are not robust to environmental variables.
Innovation Solution
A robot cleaner that generates a robust map without relying on sensors by dividing cleaning areas into different zones based on driving state information, allowing for varied cleaning modes and sensor adjustments to optimize navigation and cleaning efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If robot cleaners rely on sensors to generate maps and navigate cleaning areas, then they can perform autonomous cleaning operations, but the accuracy and reliability deteriorate due to various environmental variables and insufficient sensor performance
Solution Approach 1:
The patent replaces sensor-based environmental detection with a mechanical approach using driving state information. Instead of relying on sensors to detect obstacles and generate maps, the system uses the robot cleaner's own driving data (wheel rotations, motor currents, vibration signals) to infer cleaning obstruction areas, thereby eliminating sensor reliability issues while maintaining navigation capability
Solution Approach 2:
The patent creates a virtual map of cleaning obstruction areas by copying and processing driving state information. Rather than using sensors to directly perceive the environment, the system reconstructs environmental features by analyzing patterns in driving data, effectively creating a digital twin of the cleaning environment based on mechanical operation records
2Productivity
If robot cleaners clean all areas uniformly considering coverage of the entire cleaning area, then complete cleaning is achieved, but cleaning completion time is lengthened in complex environments with obstacles or uneven floors
Solution Approach 1:
The patent segments the cleaning area into different types based on driving state information: normal cleaning areas and cleaning obstruction areas. This segmentation allows the robot to apply different cleaning strategies to different zones, avoiding uniform treatment of all areas and thereby reducing overall cleaning time while maintaining completeness
Solution Approach 2:
The patent implements dynamic cleaning mode switching based on the identified cleaning obstruction areas. The robot automatically adjusts its cleaning behavior (speed, path planning, re-cleaning frequency) when entering obstruction areas versus normal areas, creating a dynamic cleaning process that adapts to environmental complexity and optimizes completion time
3Adaptability or versatility
If robot cleaners use sensors to detect cleaning obstruction areas, then they can identify complex driving areas, but the system complexity increases and sensor insufficiency reduces effectiveness
Solution Approach 1:
The patent enables the robot cleaner to self-diagnose and self-adapt by using its own driving state information to identify cleaning obstruction areas. The system serves itself by using its operational data to understand the environment, eliminating the need for external sensors and reducing system complexity while maintaining or improving adaptability
Data Source
AI summary
A robot cleaner according to an embodiment of the present invention comprises: a travelling part for moving a body; a memory for storing travel state information of the traveling part, which is recorded while a cleaning operation is performed on the basis of a first map; and a control part for discriminately detecting a first area and a second area divided from a plurality of cleaning areas corresponding to the first map, on the basis of the stored travel state information. Moreover, the control part may generate a second map by removing one of the first area and the second area from the first map, and then control the travelling part to perform a cleaning operation in a changed cleaning mode on the basis of the generated second map.