Robot Navigation Using Region-Based Neural Models and Sparse Maps
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Solution Overview
Problem
Existing autonomous navigation techniques for robots require significant processing, memory, and bandwidth due to the need for detailed maps of environments, which is costly and raises privacy concerns, especially when third-party robots navigate through unfamiliar premises.
Innovation Solution
The approach involves subdividing the environment into small, overlapping navigation regions with associated neural network models, allowing robots to navigate based on limited information such as distance and orientation relative to region center points, reducing the need for extensive data processing and storage, and directing image sensors to minimize capture of sensitive information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed environmental maps are used for autonomous navigation, then navigation accuracy is improved, but computational requirements and memory capacity increase significantly
Solution Approach 1:
The patent divides the environment into discrete navigation regions with associated center points. Instead of processing continuous detailed maps, the robot navigates by identifying and moving toward region center points, significantly reducing computational complexity while maintaining navigation functionality.
Solution Approach 2:
The patent extracts only the essential navigation information (region center points and boundaries) from the full environmental map, discarding unnecessary detailed information. This extraction allows the robot to navigate effectively with minimal data, reducing both memory requirements and processing power needs.
2Loss of information
If detailed environmental maps are stored in memory, then complete environmental information is available, but memory capacity requirements increase
Solution Approach 1:
The patent extracts only the critical elements needed for navigation (region center points and boundary definitions) from the complete environmental map, storing only this essential information in memory. This allows the system to maintain navigation capability with minimal memory usage.
Solution Approach 2:
The environment is segmented into discrete regions, and only the essential parameters of each region (center point coordinates and boundary information) are stored. This segmentation approach reduces the total information that must be retained in memory while preserving navigation functionality.
3Measurement precision
If image sensors capture comprehensive environmental data, then navigation accuracy is improved, but privacy concerns increase due to capture of sensitive information
Solution Approach 1:
The patent directs image sensors to capture images only of specific local features (region center points and navigation boundaries) rather than comprehensive environmental data. This localized imaging approach provides sufficient navigation information while minimizing capture of sensitive or private information in the broader environment.
Data Source
AI summary
Methods and apparatus to facilitate autonomous navigation of robotic devices. An example autonomous robot includes a region model analyzer to: analyze a first image of an environment based on a first neural network model, the first image captured by an image sensor of the robot when the robot is in a first region of the environment; and analyze a second image of the environment based on a second neural network model, the second image captured by the image sensor when the robot is in a second region of the environment, the second neural network associated with the second region. The example robot further includes a movement controller to: autonomously control movement of the robot within the first region toward the second region based on the analysis of the first image; and autonomously control movement of the robot within the second region based on the analysis of the second image.