Cleaning control method based on dense obstacles
Find Innovative SolutionsGenerate Solutions
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 marks dense obstacle points in a grid map, plans navigation paths to avoid other obstacles, and controls the robot to travel circles around detected obstacles, ensuring thorough cleaning while navigating through dense obstacle zones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the robot uses conventional cleaning paths, then cleaning efficiency is maintained, but cleaning coverage is incomplete in dense obstacle regions
Solution Approach 1:
The system performs preliminary identification of dense obstacle regions before cleaning operations. By analyzing obstacle distribution patterns in advance and pre-planning cleaning paths that specifically target these dense regions, the system ensures complete cleaning coverage without compromising overall cleaning efficiency. The preliminary action involves detecting obstacle density, identifying regions that require special attention, and preparing appropriate cleaning strategies before the robot begins its cleaning task.
2Reliability
If the robot avoids all obstacles, then collision is prevented, but cleaning of regions around obstacles is missed
Solution Approach 1:
The system applies different cleaning behaviors to different spatial regions based on local obstacle density characteristics. In dense obstacle regions, the robot executes specialized cleaning patterns that navigate around obstacles to clean previously missed areas. In sparse regions, conventional cleaning paths are used. This local differentiation allows the robot to maintain collision avoidance while ensuring complete cleaning coverage in critical dense regions.
3Manufacturing precision
If the robot uses complex navigation algorithms, then cleaning coverage improves, but computational resources and time increase
Solution Approach 1:
The cleaning space is segmented into multiple regions based on obstacle density characteristics. Dense obstacle regions are identified and separated from sparse regions. For each segment, simplified and appropriate cleaning paths are generated rather than applying complex algorithms to the entire space. This segmentation reduces computational burden while ensuring that dense regions receive the specialized attention they need for complete cleaning coverage.
4Manufacturing precision
If the robot cleans every region systematically, then cleaning coverage is complete, but cleaning time increases
Solution Approach 1:
The system applies excessive cleaning action specifically to dense obstacle regions where missed cleaning is most likely to occur, while using standard cleaning actions in sparse regions. This partial application of enhanced cleaning ensures complete coverage in critical areas without unnecessarily increasing cleaning time across the entire environment. The excessive action is localized to where it is most needed.
Data Source
Figure 1
Figure 2~3
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, according to the dense obstacle points marked in the grid map, 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, according to an obstacle collision detected by the cleaning robot, 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, and the cleaning robot keeps conducting cleaning operations while traveling around the obstacle currently in collision.