Robot Context Learning for Unable-to-Move Obstacle Avoidance
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Solution Overview
Problem
Existing robotic systems face challenges in autonomously navigating and avoiding obstacles, particularly when they become stuck due to external elements, leading to inefficient and unsafe movement.
Innovation Solution
An electronic apparatus equipped with sensors and a processor that captures surrounding images, generates context data during unable-to-move situations, and uses data recognition models to learn and predict such events, allowing the robot to adjust its path and avoid future obstacles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the robot moves autonomously without learning from past situations, then the movement speed is fast, but the robot frequently gets stuck due to inability to predict obstacles
Solution Approach 1:
The electronic apparatus performs preliminary actions by capturing surrounding images and generating context data before unable-to-move situations occur. The system learns from historical context data stored in memory, enabling predictive capabilities that prevent future stuck situations rather than merely reacting to them.
Solution Approach 2:
The system implements feedback mechanisms by storing context data from past unable-to-move situations and using machine learning models to analyze this feedback. The learned patterns are then applied to predict and avoid similar situations, creating a continuous improvement loop that enhances movement reliability over time.
2Measurement precision
If the robot captures and processes surrounding images continuously, then the obstacle detection accuracy is improved, but the energy consumption increases
Solution Approach 1:
The system applies partial action by capturing surrounding images selectively rather than continuously. Images are captured specifically when unable-to-move situations occur or are anticipated, providing sufficient data for accurate situation recognition while avoiding the energy waste of continuous imaging in normal movement conditions.
Solution Approach 2:
The electronic apparatus performs self-service by using its own captured images and stored context data to improve its navigation capabilities. The system learns from its own operational experiences stored in memory, reducing the need for external intervention or additional sensing resources.
3Adaptability or versatility
If the robot uses complex learning algorithms to predict obstacles, then the navigation intelligence is improved, but the device complexity increases
Solution Approach 1:
The system performs preliminary learning by training machine learning models offline using stored context data before actual navigation tasks. This preliminary preparation enables the robot to make accurate predictions during operation without requiring complex real-time processing, thus improving navigation adaptability while keeping operational device complexity manageable.
Solution Approach 2:
The electronic apparatus creates simplified representations of complex environments by generating context data that captures essential features of unable-to-move situations. These copied representations are stored and used for prediction, allowing the system to handle complex navigation challenges without requiring equally complex processing hardware during operation.
Data Source
AI summary
An electronic apparatus and an operating method are provided. The electronic apparatus includes a storage, at least one sensor, and at least one processor configured to execute stored instructions to while the electronic apparatus is moving, capture a surrounding image by using the at least one sensor, when an unable-to-move situation occurs while the electronic apparatus is moving, generate context data including a surrounding image captured within a predetermined time from a time when the unable-to-move situation has occurred, store, in the storage, the generated context data corresponding to the unable-to-move situation having occurred, and learn the stored context data by using one or more data recognition models.