AI Moving Robot Map Updating for Dynamic Environment Navigation
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Solution Overview
Problem
Existing moving robot technologies face challenges in accurately generating and updating maps in dynamic environments due to changes in illumination, sunlight, and object positions, leading to reduced position recognition accuracy and inability to cope with environmental changes.
Innovation Solution
A method for controlling moving robots that involves learning and updating maps by checking nodes within a reference distance, determining correlations, and registering nodes based on environmental changes, ensuring the map reflects meaningful changes while preventing updates from small changes, thus enhancing position estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the robot continuously updates the map based on current sensor data, then the map can reflect the latest environment, but position recognition accuracy deteriorates due to environmental changes such as illumination variations and object position changes
Solution Approach 1:
The system performs preliminary actions by acquiring and storing multiple images of the same location under different environmental conditions before position recognition is needed. This allows the robot to have pre-prepared reference data that accounts for various illumination and environmental states, enabling accurate position recognition despite environmental changes when the actual position estimation is performed
Solution Approach 2:
The system uses feedback by comparing current sensor data with previously stored reference data from multiple environmental conditions. The position estimation module receives feedback from this comparison to determine the robot's current position, allowing the system to adapt to environmental changes while maintaining recognition accuracy through iterative refinement
2Adaptability or versatility
If the robot stores multiple maps to account for environmental changes, then adaptability improves, but device complexity increases
Solution Approach 1:
The system merges multiple images of the same location taken under different environmental conditions into a unified reference dataset. Instead of maintaining separate maps for different conditions, the system combines them into a single comprehensive reference that the position estimation module can query, reducing complexity while maintaining adaptability
Solution Approach 2:
The system creates a universal reference system that serves multiple functions: it can recognize positions under various illumination conditions, adapt to environmental changes, and provide consistent position estimation across different scenarios. This single multi-functional reference system replaces the need for multiple specialized maps
Data Source
AI summary
A controlling method for an artificial intelligence moving robot according to an aspect of the present disclosure includes: checking nodes within a predetermined reference distance from a node corresponding to a current position; determining whether there is a correlation between the nodes within the reference distance and the node corresponding to the current position; determining whether the nodes within the reference distance are nodes of a previously learned map when there is no correlation; and registering the node corresponding to the current position on the map when the nodes within the reference distance are determined as nodes of the previously learned map, thereby being able to generate a map in which the environment of a traveling section and environmental changes are appropriately reflected.