Vision-Guided Robot Training via Augmented Reality Overlay
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Solution Overview
Problem
Industrial robots with pre-defined visual models struggle to recognize new types of objects and require technical expertise for training, limiting their adaptability and versatility in various situations.
Innovation Solution
A vision-guided robot system that enables intuitive interactions with human trainers using augmented-reality techniques, allowing the robot to learn and adapt by overlaying computer graphics onto real-world data, and receiving feedback to modify its performance without requiring technical knowledge from the trainer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pre-defined visual models are used for object recognition, then the robot can reliably identify objects within prescribed classes, but the robot cannot recognize new types of objects and lacks adaptability
Solution Approach 1:
The system pre-defines multiple visual models covering different object classes and characteristics before operation. These models are prepared in advance and stored in the system, enabling the robot to quickly match and recognize objects without requiring real-time learning of basic object properties.
Solution Approach 2:
The system dynamically selects and switches between different pre-defined visual models based on the characteristics of the object being observed. The machine vision system can adaptively choose the most appropriate model for the current situation, allowing reliable recognition across various object types while maintaining the ability to handle new objects by selecting from the available model set.
2Ease of manufacture
If custom-developed vision software with pre-defined models is used, then object identification within prescribed classes is facilitated, but the system complexity increases and requires technical expertise for training
Solution Approach 1:
The machine vision system is designed with universal visual models that can identify multiple types of objects across different classes using a single integrated system. The pre-defined models cover various object characteristics, allowing the same vision software to handle diverse objects without requiring separate custom programs for each object type.
Solution Approach 2:
The system automatically performs object identification by comparing observed objects against the pre-defined visual models without requiring manual intervention or technical expertise for training. The robot independently selects appropriate models and executes recognition tasks, eliminating the need for operators to understand computer vision algorithms or adjust model parameters.
3Adaptability or versatility
If the robot is designed to handle a wide variety of situations, then versatility is improved, but the visual-recognition needs cannot be met with pre-defined models alone
Solution Approach 1:
The visual model system is segmented into multiple specialized models, each optimized for specific object classes or characteristics. This segmentation allows the system to cover a wide variety of situations by dividing the complex recognition task into manageable model categories, ensuring comprehensive visual model coverage across different object types while maintaining specialized recognition capabilities for each segment.
Data Source
AI summary
Via intuitive interactions with a user, robots may be trained to perform tasks such as visually detecting and identifying physical objects and/or manipulating objects. In some embodiments, training is facilitated by the robot's simulation of task-execution using augmented-reality techniques.