Asynchronous AR Training System for Spatial Task Instruction
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Solution Overview
Problem
Current training methods for spatial tasks in industrial environments, such as one-on-one instructions, paper/sketch-based instructions, and video-based instructions, are inefficient in terms of time, cost, and scalability, and the adoption of augmented reality (AR) is hindered by the technical skills, expertise, and costs required for content creation.
Innovation Solution
The development of an AR system, ProcessAR, which captures and processes real-world workspace images to identify physical objects and render virtual objects in 3D space, allowing users to interact with virtual objects and record procedural tasks, thereby reducing the complexity and cost of AR content creation and enabling asynchronous learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional one-on-one training is used for spatial tasks, then training reliability is maintained, but training time and cost increase significantly
Solution Approach 1:
The patent creates virtual copies of physical objects and training environments that can be replicated and distributed to multiple users simultaneously. These digital twins allow trainees to practice procedures without requiring expert instructors, maintaining training quality while enabling asynchronous self-paced learning that reduces time loss.
Solution Approach 2:
The system pre-renders virtual objects and procedural instructions before training sessions begin. All training materials, including 3D models, assembly procedures, and interactive elements, are prepared in advance, allowing users to access completed training modules without waiting for instructor preparation or setup time.
2Manufacturing precision
If AR content is created with high technical expertise, then content quality improves, but development cost and complexity increase
Solution Approach 1:
The patent implements automated systems that allow users to create and author AR content without requiring expert technical knowledge. The system automatically captures images, generates 3D models, and structures procedural content through intuitive interfaces, enabling domain experts to create high-quality training materials without needing AR development expertise.
Solution Approach 2:
Manual processes requiring expert intervention are replaced with automated computational systems. Image processing algorithms automatically generate 3D models from photographs, and procedural content is structured through automated parsing and organization, eliminating the need for manual 3D modeling and content authoring by experts.
3Measurement precision
If detailed 3D models are rendered for all physical objects, then spatial accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system renders detailed 3D models only for objects that are currently relevant to the training procedure being viewed. Less critical objects use simplified representations, balancing spatial accuracy requirements with processing efficiency. This selective rendering approach maintains precision where needed while reducing overall computational burden.
4Productivity
If AR training content is created asynchronously, then scalability improves, but interactivity and feedback mechanisms are reduced
Solution Approach 1:
The patent incorporates automated feedback systems that evaluate user actions against correct procedures and provide immediate guidance. The system tracks user interactions with virtual objects, compares them to expected outcomes, and delivers corrective feedback through the AR interface, maintaining interactivity and instructional quality without requiring real-time instructor involvement.
Data Source
AI summary
A method of operating an augmented reality (AR) system includes capturing images of a first real-world workspace using a camera of a first head mounted AR device of the AR system being worn by a first user, processing the images using a first processor of the AR system to identify physical objects in the first real-world workspace and detect 3D positions of the identified physical objects in a 3D space corresponding to the first real-world workspace, rendering virtual objects representing the identified physical objects on the display of the first head mounted AR device at the respective 3D positions for the identified physical objects, manipulating a first one of the virtual objects using at least one hand-held controller of the AR system in a manner that mimics a performance of a first procedural task using the physical object associated with the first one of the virtual objects, recording the manipulation of the first one of the virtual objects that mimics the performance of the first procedural task as first augmented reality content, and storing the first augmented reality content in a memory of the AR system.