Mobile Robot Positioning with Static-Dynamic Map Alignment
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Solution Overview
Problem
Existing autonomous navigation systems for mobile robots in dynamic environments, such as manufacturing facilities, fail to accurately account for changes in the location of large objects, leading to inaccuracies in self-positioning and navigation.
Innovation Solution
Divide the global map into static and dynamic maps, using external data from manufacturing systems to create real-time maps that include inspection targets, allowing for precise alignment and integration of absolute and relative positions to generate a combined map for accurate navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single global map is used for navigation, then the system is simple to manage, but navigation accuracy deteriorates when objects in the environment change position
Solution Approach 1:
The patent divides the global map into multiple sub-maps (first sub-map, second sub-map, third sub-map, etc.), where each sub-map represents a specific region or aspect of the environment. This segmentation allows the system to manage complexity while maintaining high navigation accuracy by updating only the relevant sub-maps when objects change position, rather than regenerating the entire global map.
2Measurement precision
If the entire global map is regenerated in real-time, then navigation accuracy is maintained, but computational time and processing resources increase
Solution Approach 1:
The patent performs preliminary division of the global map into multiple sub-maps in advance, organizing them by region or function. This preliminary structure enables the system to quickly update only the specific sub-maps that contain changed objects, rather than processing the entire global map, thereby reducing computational time while maintaining accuracy.
Solution Approach 2:
The patent implements dynamic map management where sub-maps are selectively updated based on detected changes in the environment. When an object's position changes, only the relevant sub-map is regenerated and merged, rather than updating the entire global map. This dynamic approach maintains position estimation accuracy while minimizing processing time and resource consumption.
3Extent of automation
If SLAM is used for autonomous navigation, then the robot can operate autonomously, but the system cannot account for changes in target location
Solution Approach 1:
The patent incorporates feedback mechanisms where the robot detects changes in object positions during navigation and uses this information to update the relevant sub-maps. This feedback loop enables the autonomous navigation system to adapt to dynamic environments by continuously refining its map representation based on actual environmental changes, thereby maintaining both autonomy and adaptability.
Data Source
AI summary
Systems and methods that enhance the navigation of autonomous mobile robots in dynamic environments, such as inspection yards or manufacturing facilities in which positions and states of objects frequently change. This is achieved by integrating static maps, which provide information about permanent structures and areas, with dynamic maps, which focus on zones with movable objects and inspection zones where the robot performs tasks like monitoring or object inspection. Various embodiments utilize simultaneous localization and mapping (SLAM) techniques to generate maps that account for frequently changing elements in the environments. Real-time map generation and updates comprise splitting an environmental map into static and dynamic regions and calculating the robot's absolute position in the environment and its relative position to objects of interest. This dual estimation leverages different map datasets to perform efficient and accurate self-position estimation, thus ensuring consistent and reliable navigation based on real-time monitoring of the robot's surroundings.