Pathfinding cost-based cleaning path planning method, chip, and cleaning robot
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Solution Overview
Problem
Current cleaning robots face inefficiencies in path planning, leading to repeated cleaning of some areas and missed areas due to global coverage methods, which increases navigation time and reduces the intelligence and user experience of the cleaning process.
Innovation Solution
A cleaning path planning method based on pathfinding cost, where the robot configures cleaning lines and divides the area into sub-blocks, sets candidate entrance/exit positions based on pathfinding costs, and plans a bow-shaped cleaning path to efficiently traverse and clean all sub-blocks, reducing unnecessary turns and travel distance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If global coverage cleaning method is used, then all areas are eventually cleaned, but some areas are repeatedly cleaned and some areas are missed, increasing navigation time
Solution Approach 1:
The cleaning area is divided into multiple sub-blocks with clear boundaries. The robot plans and executes cleaning for each sub-block separately, tracking cleaned and uncleaned areas within each sub-block. This segmentation eliminates the confusion of global coverage methods where some areas are repeatedly cleaned while others are missed, as each sub-block is systematically processed once.
Solution Approach 2:
The robot pre-divides the cleaning area into sub-blocks and pre-plans cleaning paths for each sub-block before execution. The cleaning path planning method pre-calculates optimal paths considering sub-block boundaries and cleaning states, allowing the robot to efficiently navigate and clean without redundant movements or missed areas during actual execution.
2Productivity
If the cleaning area is divided into sub-blocks with bow-shaped paths, then the cleaning path becomes more structured and efficient, but the path planning algorithm becomes more complex
Solution Approach 1:
The cleaning area is segmented into sub-blocks with defined boundaries, and the cleaning path is segmented into bow-shaped segments that systematically cover each sub-block. This segmentation transforms the complex global path planning problem into simpler local path planning within each sub-block, where bow-shaped paths provide a standardized, efficient cleaning pattern.
Solution Approach 2:
The path planning algorithm dynamically adjusts the cleaning sequence and paths based on the robot's current position, the state of cleaned areas, and the boundaries of sub-blocks. The bow-shaped paths are dynamically generated and adjusted to optimize cleaning efficiency while maintaining systematic coverage, balancing algorithm complexity with cleaning performance.
3Productivity
If the robot uses systematic sub-block cleaning with clear boundaries, then redundant cleaning is reduced, but the robot requires more sophisticated localization and boundary detection capabilities
Solution Approach 1:
The cleaning area is divided into sub-blocks with clear boundaries that the robot can detect and recognize. These boundaries serve as reference markers for localization, allowing the robot to determine its position and the cleaning state of each sub-block. The segmented structure makes boundary detection more manageable compared to navigating an undifferentiated global cleaning area.
Solution Approach 2:
The sub-block boundaries act as intermediaries between the robot's localization system and the cleaning task. By detecting and using these boundaries as reference points, the robot can systematically track which sub-blocks have been cleaned and which remain, reducing the complexity of overall area monitoring and improving cleaning efficiency.
Data Source
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AI summary
Disclosed are a cleaning path planning method based on a pathfinding cost, a chip, and a cleaning robot. The cleaning path planning method includes finding candidate entrances/exits in a rational navigation order in different sub-blocks according to pathfinding costs in different directions of coordinate axes as cleaning entrance positions of the corresponding sub-blocks, thereby obtaining a finally-planned cleaning path in a cleaning area upon combination and connection, shortening a navigation path distance between different sub-blocks, reducing a time for the robot to plan a path between areas in a room, reducing a calculation amount, and improving a cleaning effect of the mobile robot.