Method for Controlling Cleaning Based on Dense Obstacles
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Solution Overview
Problem
Sweeping robots often miss cleaning regions around densely distributed obstacles like chair feet and table feet due to their narrow passable areas, leading to incomplete cleaning coverage.
Innovation Solution
A method that involves marking dense obstacle points in a grid map, planning navigation paths that avoid other obstacles, and controlling the robot to travel circles around detected obstacles to ensure thorough cleaning, with adaptive navigation paths and collision detection to handle map drift and positioning errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the sweeping robot uses conventional cleaning paths, then the cleaning coverage includes common corners and regions around furniture, but the narrow passable regions between densely distributed obstacles are missed
Solution Approach 1:
The system performs preliminary identification of dense obstacle regions before cleaning operations. The controller detects multiple obstacles in close proximity and pre-plans special cleaning paths for these regions, ensuring that narrow passable areas are included in the cleaning route before the robot attempts cleaning, thus avoiding missed regions.
Solution Approach 2:
The cleaning path planning is made dynamic and adaptive based on real-time obstacle detection. When the robot encounters dense obstacle regions during cleaning, the controller dynamically adjusts the navigation path to include circular cleaning patterns around obstacles, transforming the static cleaning route into a flexible adaptive path that ensures complete coverage.
2Reliability
If the robot navigates to clean around each obstacle individually, then complete cleaning coverage is achieved, but the cleaning time and navigation complexity increase
Solution Approach 1:
The cleaning task is segmented into standard cleaning regions and dense obstacle regions. The controller identifies and separates dense obstacle areas from the general cleaning area, applying different cleaning strategies to each segment. This segmentation allows efficient standard cleaning for open areas while targeting only the necessary circular patterns for dense obstacle regions, improving overall efficiency.
Solution Approach 2:
Different cleaning qualities and patterns are applied to different regions. Standard regions receive conventional linear cleaning paths, while dense obstacle regions receive enhanced circular cleaning patterns. This local differentiation ensures thorough cleaning where needed without wasting time applying the same intensive method to all areas, thus maintaining productivity.
3Productivity
If the robot follows pre-planned navigation paths, then navigation efficiency is improved, but map drift and positioning errors cause the robot to miss the actual obstacle positions
Solution Approach 1:
The system implements real-time feedback through obstacle detection sensors during cleaning operations. When the robot encounters an obstacle during navigation, the controller detects the collision or proximity and uses this feedback to dynamically adjust the navigation path, ensuring the robot correctly identifies and circles around the actual obstacle position despite any initial positioning drift.
Data Source
AI summary
The present disclosure discloses a method for controlling cleaning based on dense obstacles. The method includes: step 1, marking dense obstacle points according to a number feature of grids of obstacles framed by a sliding rectangular frame which is preset in a grid map, and then proceeding to step 2; the step 2, planning navigation paths of which an end point position is corresponding to one dense obstacle point and which does not pass grids of other obstacles, and then proceeding to step 3; and the step 3, controlling the cleaning robot to complete traveling a circle around an obstacle currently in collision in a process of navigating the cleaning robot to an actual position corresponding to each of the dense obstacle points according to the navigation paths planned in the step 2, and marking a cleaning state of a grid corresponding to the obstacle currently in collision.