Robot Cleaner Sub-Area Path Segmentation for Fast Map Updates
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Solution Overview
Problem
Robot cleaners face inefficiencies in calculating full travel paths and maintaining optimal cleaning performance as the area to be cleaned increases, requiring extensive memory and time to regenerate maps, leading to suboptimal travel and cleaning efficiency.
Innovation Solution
A robot cleaner that divides the cleaning area into sub-areas, calculates full paths based on connection points between sub-areas, and updates paths without regenerating the entire map, using a control unit to manage travel paths, connection points, and obstacle detection for efficient navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If the robot cleaner uses a traditional method to calculate the full travel path based on the entire cleaning area map, then the cleaning coverage can be comprehensive, but the calculation time increases significantly when the cleaning area extends
Solution Approach 1:
The patent divides the entire cleaning area into multiple sub-areas, each with its own pre-calculated travel path. Instead of calculating the full path across the entire area, the system only needs to calculate connection paths between sub-areas, significantly reducing computation time while maintaining comprehensive cleaning coverage.
2Reliability
If the robot cleaner stores a complete map of the entire cleaning area, then the full path calculation can be accurate, but the memory capacity requirement increases
Solution Approach 1:
The patent segments the complete cleaning map into multiple sub-area maps, each stored separately in memory. This segmentation reduces the memory burden while maintaining the accuracy needed for path calculation within each sub-area and for determining connection points between them.
Solution Approach 2:
The patent pre-calculates and stores travel paths for individual sub-areas before the robot begins cleaning. This preliminary action eliminates the need to recalculate entire paths when the cleaning area extends, as the system only needs to add new sub-areas and calculate connection paths between existing and new sub-areas.
3Reliability
If the robot cleaner regenerates the entire cleaning map when the area extends, then the map remains complete and accurate, but the time and computational resources are wasted
Solution Approach 1:
The patent pre-calculates travel paths for sub-areas as they are discovered and adds them to the existing map structure. When the cleaning area extends, the system only needs to generate travel paths for the new sub-areas and calculate connection points to existing sub-areas, rather than regenerating the entire map from scratch.
Solution Approach 2:
The patent extracts only the necessary new information (new sub-area maps and connection paths) when the cleaning area extends, rather than processing the entire map again. This extraction approach maintains map completeness while minimizing computational overhead.
4Productivity
If the robot cleaner calculates the shortest distance across the entire cleaning area, then the travel efficiency is optimized, but the calculation complexity increases with area size
Solution Approach 1:
The patent segments the shortest path calculation into two parts: pre-calculated paths within sub-areas and connection paths between sub-areas. This segmentation reduces calculation complexity by breaking down the large-scale optimization problem into smaller, more manageable sub-problems that can be solved independently and then combined.
Data Source
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AI summary
Disclosed are a robot cleaner and a method for controlling the same. The robot cleaner and method of the present invention involve dividing the whole area to be cleaned into sub-areas, and easily calculating a full path using travel paths in the sub-areas and connection points between sub-areas, and in the event the whole area to be cleaned is extended or an area which has not been cleaned is found, do not involve regenerating the whole map for cleaning, but rather easily updating the full path using the pre-stored travel path in the sub-areas and the connection points between sub-areas.