Map based training and interface for mobile robots
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Solution Overview
Problem
Existing autonomous cleaning robots lack efficient training and control systems that allow users to selectively and dynamically target specific areas for cleaning, leading to inefficient cleaning operations.
Innovation Solution
A mobile application that enables users to generate and edit a master map from individual training missions, allowing for on-demand cleaning by selecting specific rooms or areas for the robot to clean, with features like map storage, deletion, and customizable room labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If the autonomous cleaning robot cleans the entire area during each mission, then the cleaning coverage is maximized, but the cleaning efficiency for targeted areas is reduced
Solution Approach 1:
The master map is segmented into multiple training maps, each representing a specific area or room. Users can select individual training maps or combinations thereof to define cleaning zones, allowing the robot to clean only selected areas rather than the entire environment, thus improving cleaning efficiency for targeted regions while maintaining the ability to cover the full area when needed
Solution Approach 2:
The cleaning zones are made dynamic and configurable through the mobile application interface. Users can dynamically select, add, or remove training maps from the cleaning zone definition before each mission, allowing flexible adaptation of cleaning coverage based on specific needs such as focusing on high-traffic areas or specific rooms
2Measurement precision
If the system requires multiple training runs to generate accurate maps, then the navigation precision is improved, but the setup time increases
Solution Approach 1:
The system performs preliminary mapping actions through multiple training runs to generate accurate training maps before actual cleaning missions. These training maps are stored and can be reviewed, allowing users to verify map accuracy and make adjustments if needed, thus ensuring high navigation precision while providing flexibility in the setup process
Solution Approach 2:
Multiple training maps generated from separate training runs are merged to create a comprehensive master map. This merging process consolidates data from multiple sources to improve overall navigation precision and create a complete environmental model that can be used for subsequent cleaning operations
3Ease of operation
If the mobile application provides extensive map editing and selection features, then the user control is enhanced, but the interface complexity increases
Solution Approach 1:
The interface is segmented into distinct functional sections: training run initiation, map display and review, training map selection, and mission configuration. Each section presents only the relevant controls and information for that specific task, reducing cognitive load and making the interface more manageable despite the comprehensive functionality available
Solution Approach 2:
The system uses visual representations (copies) of the physical environment through maps and graphical interfaces. These visual copies allow users to interact with a simplified digital representation of the cleaning zones and training maps, making it easier to understand and control the robot's behavior without directly managing complex underlying data structures
Data Source
AI summary
A method of operating an autonomous cleaning robot is described. The method includes initiating a training run of the autonomous cleaning robot and receiving, at a mobile device, location data from the autonomous cleaning robot as the autonomous cleaning robot navigates an area. The method also includes presenting, on a display of the mobile device, a training map depicting portions of the area traversed by the autonomous cleaning robot during the training run and presenting, on the display of the mobile device, an interface configured to allow the training map to be stored or deleted. The method also includes initiating additional training runs to produce additional training maps and presenting a master map generated based on a plurality of stored training maps.