Mobile Robot Map Area Segmentation for Accurate Work Zone Navigation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Mobile robots, such as cleaning robots, face inefficiencies in task paths and operational efficiency due to the inability to accurately identify designated work areas, primarily caused by environmental factors.
Innovation Solution
A method for area dividing in a map for a mobile robot, which involves processing an initial map to obtain a target area-dividing map, allowing for accurate identification of work areas and reasonable planning of task paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If mobile robots operate in complex environments without accurate area identification, then they can perform basic tasks, but their operational efficiency and task path optimization deteriorate
Solution Approach 1:
The patent divides the map into multiple binary maps representing different areas (e.g., cleaning areas, charging areas, forbidden areas). This segmentation allows the robot to identify and process different work zones independently, improving area identification accuracy and enabling optimized task paths for each specific area type.
Solution Approach 2:
The system performs preliminary map processing to generate area-divided binary maps before task execution. By pre-identifying and marking different work areas in advance, the robot can plan efficient task paths without real-time decision delays, thereby improving operational efficiency.
2Adaptability or versatility
If mobile robots use simple mapping without area division, then the system complexity remains low, but the ability to identify designated work areas and plan task paths deteriorates
Solution Approach 1:
The patent segments the overall map into multiple binary maps, each representing a specific functional area. This segmentation enhances the robot's adaptability to different work area types while keeping each individual binary map relatively simple to process, balancing versatility and complexity.
Solution Approach 2:
Different areas in the map are assigned different properties and meanings (e.g., cleaning areas vs. charging areas). This local quality differentiation allows the robot to apply area-specific processing and path planning strategies, improving work area identification capability without requiring complete reprocessing of the entire map for each task type.
Data Source
AI summary
A method for area dividing in a map for a mobile robot includes: obtaining a target binary map of a target area; obtaining a first area map and a second area map according to the target binary map; obtaining a target area-dividing map of the target area according to the first area map and the second area map; and controlling the mobile robot according to the target area-dividing map.