Robot Navigation Control Using Multi-Object Context Prediction
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Solution Overview
Problem
Current electronic devices, such as robots, lack the ability to accurately analyze and predict movements of multiple objects in their environment, leading to potential collisions and inefficient navigation due to the inability to consider relationships between objects.
Innovation Solution
An electronic apparatus equipped with a camera, memory, and processor that identifies attribute information and environment information to determine the context of multiple objects, controlling its traveling state based on their relationships, including static and dynamic objects, to prevent collisions and optimize navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the electronic apparatus considers relationships between multiple objects and their attributes, then the accuracy of situation analysis and collision prediction is improved, but the computational complexity and processing time increase
Solution Approach 1:
The system segments the environment into multiple objects with distinct attribute profiles (static/dynamic, movable/immovable). Each object is analyzed independently for its attributes and relationships, allowing the complex scene to be broken down into manageable units that can be processed systematically without overwhelming computational burden.
Solution Approach 2:
The system performs preliminary classification of objects by their attributes (static/dynamic, movable/immovable) before conducting full situation analysis. This pre-processing step organizes data structures and identifies potential collision scenarios in advance, reducing the computational complexity of subsequent real-time analysis by focusing only on relevant object relationships.
2Reliability
If the electronic apparatus identifies and predicts movements of multiple objects based on their relationships, then the reliability of navigation is improved, but the time required for processing increases
Solution Approach 1:
The system applies different analysis depths to different objects based on their attributes and risk levels. High-priority objects (dynamic, movable, potential collision risks) receive more detailed prediction and analysis, while static, immovable objects receive minimal processing. This localized quality approach maintains navigation reliability by focusing computational resources on critical scenarios while reducing overall processing time.
Solution Approach 2:
The system skips detailed prediction for objects that cannot move or pose no collision risk, rushing through their analysis with simplified protocols. For example, immovable objects like walls or fixed furniture are registered once and excluded from continuous movement prediction, allowing the system to focus processing power on dynamic objects that require thorough analysis for safe navigation.
Data Source
AI summary
Disclosed is an electronic apparatus. The electronic apparatus includes: a camera; a memory configured to store attribute information and environment information; and a processor configured to identify a plurality of objects based on an image obtained by the camera, identify a first context of a first object, from among the plurality of objects, based on a relationship between attribute information of the plurality of objects and the environment information, and control a traveling state of the electronic apparatus based on the first context.