Robot and control method thereof
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Solution Overview
Problem
Existing robot navigation systems fail to intuitively map environments and accurately identify positions of objects like home appliances and furniture, making it difficult for users to understand the structure of the environment.
Innovation Solution
A robot equipped with a camera and processor that captures multiple images from different positions, uses artificial intelligence models to identify object recognition regions, determines candidate positions based on image analysis, and statistically clusters positions to accurately locate objects on a map.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a robot uses traditional mapping methods (SLAM with LiDAR or vision SLAM with camera), then the robot can generate a map of the environment, but the map is not intuitive and object positions are not recognizable
Solution Approach 1:
The patent creates a virtual copy of the real environment by capturing multiple images from different positions and generating a two-dimensional map that replicates the spatial relationships and object positions. This virtual map copy provides intuitive visualization while maintaining accurate position information, resolving the contradiction between measurement precision and information intuitiveness.
Solution Approach 2:
The patent transforms three-dimensional spatial information from multiple images into a two-dimensional intuitive map representation. By projecting object positions and environmental structures onto a 2D plane with accurate coordinate mapping, the system maintains positional precision while enhancing visual intuitiveness for user understanding.
2Measurement precision
If a robot captures multiple images from different positions to identify object positions, then the position identification accuracy improves, but the system complexity increases
Solution Approach 1:
The patent employs artificial intelligence models that automatically perform object recognition and position identification without requiring complex manual processing. The AI model self-services the task of analyzing multiple images, extracting object positions, and mapping them to the environment map, thereby improving precision while keeping the system architecture relatively simple.
Solution Approach 2:
The patent replaces complex mechanical or algorithmic image processing systems with artificial intelligence-based recognition. The AI model substitutes traditional computer vision algorithms, providing more accurate object position identification with simpler and more adaptable processing architecture.
3Measurement precision
If a robot uses AI models to identify object recognition regions in images, then the object detection accuracy improves, but the processing time increases
Solution Approach 1:
The patent performs preliminary actions by capturing multiple images in advance from different robot positions before final object position determination. This allows the AI model to process pre-captured images rather than requiring real-time processing, thereby maintaining high recognition accuracy while reducing time loss through batch processing of previously captured images.
Data Source
AI summary
A method of controlling a robot includes obtaining a first image and a second image of a plurality of objects, the first and second image being captured from different positions; obtaining, from the first and second images, a plurality of candidate positions corresponding to each of the plurality of objects, based on a capturing position of each of the first and second images and a direction to each of the plurality of objects from each capturing position; obtaining distance information between each capturing position and each of the plurality of objects in the first and second images by analyzing the first and second images; and identifying a position of each of the plurality of objects from among the plurality of candidate positions based on the distance information.