Robot Cleaner Mapping for Autonomous Path Planning
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Solution Overview
Problem
Existing robotic cleaners require manual configuration for each cleaning operation, making them inconvenient for users, as they need to determine operation type, area, and objects individually, and lack efficient mapping for effective cleaning.
Innovation Solution
A robotic cleaner system that generates and uses maps indicating cleaning complete state, activity of dynamic objects, reachability, cleaning state, and difficult-to-clean areas, allowing for autonomous path planning and operation based on sensor data, including 3D mapping and classification of objects for optimal cleaning decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual configuration is required for each cleaning operation, then operation control is precise, but user convenience deteriorates
Solution Approach 1:
The robotic cleaner automatically generates multiple types of maps (cleaning complete state map, activity map, reachability map, cleaning state map, difficult-to-clean area map) and autonomously plans cleaning paths without requiring manual user configuration. The system serves itself by using its own sensor data to make cleaning decisions, eliminating the need for users to individually determine operation types, areas, and objects.
2Reliability
If multiple maps are generated for comprehensive cleaning information, then cleaning effectiveness is improved, but system complexity increases
Solution Approach 1:
The cleaning information is segmented into five distinct maps, each serving a specific function: cleaning complete state map (tracks cleaned areas), activity map (identifies dynamic objects), reachability map (determines accessible areas), cleaning state map (monitors current cleaning status), and difficult-to-clean area map (identifies challenging regions). This segmentation allows the system to manage complex information in an organized, modular manner while improving cleaning effectiveness.
Solution Approach 2:
The system performs preliminary mapping and analysis before actual cleaning operations. By pre-generating all necessary maps and identifying difficult-to-clean areas in advance, the robotic cleaner can plan optimal cleaning paths and prioritize areas that need attention, improving overall cleaning effectiveness without increasing operational complexity during execution.
3Ease of operation
If autonomous path planning is implemented, then operation convenience is improved, but path optimization accuracy may deteriorate
Solution Approach 1:
The autonomous path planning system continuously uses feedback from multiple maps to optimize cleaning paths. The cleaning complete state map provides feedback on already cleaned areas, the reachability map indicates accessible regions, and the difficult-to-clean area map highlights priority zones. This multi-source feedback enables the system to dynamically adjust paths while maintaining high accuracy in optimizing cleaning efficiency.
Data Source
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AI summary
An operation of a robotic cleaner includes generating at least one map including information regarding a space to be cleaned by using information measured by at least one sensor. The operation also includes setting a cleaning path by using the at least one map, and cleaning according to the cleaning path.