Robot Vacuum Dirt Detection and Shortest-Path Cleaning
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Solution Overview
Problem
Existing automatic cleaning devices fail to thoroughly clean complex environments due to misjudgment by detection mechanisms and inefficient cleaning paths, leading to incomplete cleaning and prolonged cleaning times.
Innovation Solution
A cleaning path guidance method combined with a dirt detection mechanism that uses simultaneous localization and mapping (SLAM) to determine the current position of the device, evaluates dirt levels in grids, and generates a shortest path to clean all dirty grids sequentially, optimizing cleaning efficiency and reducing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a simple mode-switch guidance mechanism is used, then the device structure is simple, but the cleaning thoroughness is insufficient for complex environments
Solution Approach 1:
The patent implements feedback by using a dirt detection mechanism to continuously monitor dirt levels in each grid, then using this information to dynamically adjust and optimize the cleaning path selection, ensuring thorough cleaning in complex environments while maintaining reasonable system complexity
Solution Approach 2:
The patent transforms the static mode-switch guidance mechanism into a dynamic system that adapts to real-time dirt distribution conditions. The cleaning path is no longer fixed but dynamically optimized based on detected dirt levels, allowing the system to handle complex environments effectively
2Reliability
If AI-integrated mode-switch guidance is used to select optimal cleaning paths, then cleaning thoroughness improves, but cleaning time increases
Solution Approach 1:
The patent applies local quality by focusing cleaning efforts on grids with higher dirt levels. Instead of uniformly cleaning all grids, the system identifies and prioritizes dirty areas, allocating more cleaning resources to where they are most needed, thus reducing overall cleaning time while maintaining thoroughness
Solution Approach 2:
The patent changes the parameter of path selection from fixed mode-based choices to dynamically optimized paths based on dirt level parameters. The system calculates optimal cleaning sequences by considering dirt levels as key parameters, enabling faster cleaning of high-dirt areas while maintaining comprehensive coverage
3Reliability
If systematic navigation with SLAM is used to plan optimal cleaning strategies, then cleaning coverage improves, but detection accuracy decreases due to misjudgment
Solution Approach 1:
The patent applies preliminary action by performing multiple detections and preliminary assessments of dirt levels before finalizing the cleaning path. The system detects dirt levels in advance, processes this information, and plans cleaning routes that account for potential detection uncertainties, ensuring thorough coverage even when individual detections have errors
Data Source
AI summary
A cleaning path guidance method combined with a dirt detection mechanism is performed in an automatic cleaning device to generate a cleaning path, so as to guide the automatic cleaning device to clean an area to be cleaned, in which plural grids are defined in the area. The method includes: moving the automatic cleaning device in the area to clear dirt away, and continuously detecting a flow of the dirt cleared away to obtain a dirt level of a current gird; if the dirt level of the current gird exceeds a threshold, marking the grid as a dirty grid; performing an algorithm and finding a shortest path passing through all dirty grids as a cleaning path according to the marked dirty grids; and moving the automatic cleaning device to pass through each dirty grid according to the clean path, so as to clean each dirty gird sequentially.