Autonomous Cleaning Robot Feedback Mapping for Missed Areas
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Solution Overview
Problem
Autonomous mobile objects, such as cleaning robots, face challenges in effectively evaluating and compensating for incomplete operations due to unrecorded obstacles and varying dirt levels, leading to inefficient cleaning and increased costs.
Innovation Solution
Incorporating a detection unit to create an operation result map indicating cleaned areas, a display unit to show this map, and a setting unit to determine the next movement area based on the map, allowing the robot to prioritize cleaning of under-cleaned areas and avoid obstacles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If the autonomous mobile object preferentially travels through areas with less frequent travel history, then the operation coverage is improved, but cleaning efficiency deteriorates when obstacles are present
Solution Approach 1:
The system implements feedback by detecting whether an area has been adequately cleaned (using dirt sensors or operation logs) and adjusting the travel plan accordingly. The autonomous mobile object evaluates cleaning results from previous operations and uses this feedback to determine whether to prioritize revisiting certain areas, thereby improving both coverage and efficiency dynamically.
Solution Approach 2:
The travel priority strategy is made dynamic rather than static. The system adjusts travel priorities in real-time based on detected obstacles, cleaning results, and area dirt levels. This allows the autonomous mobile object to adapt its path planning to current environmental conditions, resolving the contradiction between coverage and efficiency.
2Measurement precision
If the autonomous mobile object adds dirt sensor to detect actual dirt levels, then cleaning accuracy is improved, but device cost increases
Solution Approach 1:
Instead of using expensive dirt sensors, the system creates a virtual copy or model of the dirt distribution map based on operation logs and environmental data. This digital representation allows the system to estimate and prioritize cleaning areas without requiring physical sensing hardware, thereby maintaining accuracy while reducing cost.
Solution Approach 2:
The system introduces an intermediary layer of inference and estimation between the absence of dirt sensors and the need for cleaning accuracy. By using operation history, obstacle detection data, and environmental models as intermediaries, the system can accurately identify areas needing cleaning without direct sensor measurement, avoiding the cost increase.
3Reliability
If the autonomous mobile object revisits areas with obstacles, then operation completeness is improved, but operation time increases
Solution Approach 1:
The system performs preliminary detection of obstacles and area accessibility before finalizing the travel plan. By anticipating which areas can be cleaned and which cannot (due to persistent obstacles), the system pre-adjusts the operation schedule to avoid unnecessary revisits to inaccessible areas, thereby maintaining completeness while reducing wasted time.
Solution Approach 2:
The system applies partial action by selectively revisiting only those areas that are both incomplete and accessible, rather than uniformly revisiting all areas. This targeted approach ensures operation completeness for reachable areas while avoiding excessive time consumption on areas blocked by obstacles.
Data Source
AI summary
The extent of the movement area of an autonomous mobile object is appropriately fed back for the extent of the movement area in the next operation at a low cost. An autonomous mobile object (1) includes an operation result map creation unit (21) that creates, on the basis of a log of the position of a cleaning brush (9), an operation result map in which a cleaned area is indicated, and a next-cleaning-area setting unit (22) that sets a next cleaning area on the basis of the operation result map displayed on an operation panel (13).