Robot Cleaner Map Segmentation for Adaptive Cleaning Mode Control
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Solution Overview
Problem
Existing robot cleaners face challenges in efficiently cleaning complex environments with multiple obstacles and uneven floors, leading to prolonged cleaning times and user frustration. Additionally, methods relying on sensor information are limited by sensor insufficiency and environmental variability.
Innovation Solution
A robot cleaner that generates a robust map usable without sensors, identifies cleaning obstruction areas from an existing map using driving state information, and adapts to these areas by setting new cleaning modes and performing cleaning operations in varied modes to optimize efficiency and time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If a robot cleaner cleans all areas by considering the coverage of the entire cleaning area, then the cleaning coverage is improved, but the cleaning completion time is lengthened in complex environments
Solution Approach 1:
The cleaning area is segmented into multiple zones based on driving state information, with each zone having different cleaning priorities. The controller divides the overall cleaning task into sub-tasks for different zones, allowing the robot to clean high-priority areas first while maintaining comprehensive coverage eventually.
Solution Approach 2:
The cleaning mode is made dynamic and adaptable based on real-time driving state information. The controller adjusts cleaning parameters such as speed, suction power, and path planning according to the specific characteristics of each cleaning zone, optimizing the balance between coverage and time consumption.
2Measurement precision
If sensor information is used to generate a map, then the mapping accuracy is improved, but the system becomes limited by sensor insufficiency and environmental variables
Solution Approach 1:
Driving state information serves as an intermediary data source between the robot's movement and the map generation process. Instead of relying solely on sensor data that may be insufficient or affected by environmental variables, the system uses driving state information (position, speed, direction, acceleration) to generate and update the map, providing a more robust solution.
Solution Approach 2:
The robot uses its own driving state information, which is inherently available during normal operation, to generate and update the cleaning area map. This self-service approach eliminates the need for additional specialized sensors or external assistance, making the system more versatile and adaptable to various environmental conditions.
3Ease of operation
If the robot cleaner performs cleaning in a uniform mode across all areas, then the operation simplicity is maintained, but the cleaning efficiency is reduced in different environmental conditions
Solution Approach 1:
Different cleaning modes are assigned to different cleaning zones based on their specific characteristics identified from driving state information. For example, areas with obstacles may use slower, more thorough cleaning modes, while open areas use faster, more efficient modes. This local customization optimizes cleaning efficiency without requiring complex user intervention.
Solution Approach 2:
The cleaning parameters (speed, suction power, path interval, etc.) are dynamically changed based on the cleaning zone characteristics. The controller automatically adjusts these parameters for different areas, maintaining operation simplicity while significantly improving overall cleaning efficiency through adaptive parameter optimization.
Data Source
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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.