Mobile Robot Context Analysis for Multi-Object Collision Avoidance
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Solution Overview
Problem
Current electronic devices, such as robots, lack the ability to accurately analyze and predict movements of objects in their environment due to the failure to consider relationships between multiple objects, leading to potential collisions and inaccurate situation analysis.
Innovation Solution
An electronic apparatus equipped with a camera, memory, and processor that identifies attribute information and environment information to determine control operations, including the relationship between objects, predicting movements and adjusting its traveling state to avoid collisions and maintain appropriate modes based on context analysis.
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 prediction is improved, but the computational complexity and processing time increase
Solution Approach 1:
The processor segments the analysis by first identifying individual objects and their attributes separately, then analyzing relationships between specific pairs of objects. This divides the complex multi-object analysis into manageable sequential steps, reducing computational complexity while maintaining accuracy.
Solution Approach 2:
The system performs preliminary identification of objects and their attributes before analyzing relationships. By pre-processing and storing object attributes in structured formats, the system prepares data in advance for relationship analysis, reducing real-time computational burden while improving analysis accuracy.
2Reliability
If the electronic apparatus identifies and predicts movements of multiple objects based on relationships, then collision avoidance capability is improved, but the processing time and computational resources increase
Solution Approach 1:
The processor performs preliminary identification of object attributes and relationships before movement prediction. By pre-analyzing static attributes and relationship patterns, the system reduces real-time processing requirements while maintaining accurate collision avoidance predictions.
Solution Approach 2:
The system prioritizes processing by focusing computational resources on identifying critical relationships and predicted movements that pose collision risks, skipping or simplifying analysis of non-critical object interactions. This rushes through essential analysis quickly while maintaining safety.
3Measurement precision
If the electronic apparatus analyzes relationships between dynamic and static objects, then the accuracy of predicted movement is improved, but the complexity of object identification and classification increases
Solution Approach 1:
The processor segments objects into dynamic and static categories based on identified attributes, then applies relationship analysis specifically to relevant object pairs. This segmentation simplifies the identification process by creating clear classification criteria while maintaining accurate movement prediction for dynamic objects.
Data Source
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AI summary
An electronic apparatus includes a camera; a memory configured to store attribute information and environment information; a processor configured to identify a plurality of objects based on an image obtained by the camera, to 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 to control a traveling state of the electronic apparatus based on the first context.