Robot Training by Motion Capture for 3D Printed Part Handling
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Solution Overview
Problem
Conventional robotic systems lack the capability to be quickly programmed to perform complex and intricate tasks, especially when handling 3D printed objects that vary in size and dimension, requiring significant programming efforts for precise tool handling.
Innovation Solution
A method and system that utilize a trainable robotic system equipped with sensors and a virtual reality environment to capture and process movement data from a user operating physical tools, allowing the robot to learn and replicate the necessary movements for handling various tools and objects, including 3D printed parts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional robotic systems are programmed to handle 3D printed objects with precise tool handling, then manufacturing precision is improved, but programming time and complexity increase significantly
Solution Approach 1:
The system captures human operator movements using motion tracking technology and creates digital copies of these movements for robot programming. This allows the robot to replicate precise human tool handling techniques without requiring extensive manual programming, thus maintaining manufacturing precision while significantly reducing programming time
Solution Approach 2:
The patent replaces traditional mechanical programming approaches with sensor-based motion capture systems. Instead of manually programming robot movements through complex coordinate systems and paths, the system uses optical or magnetic sensors to automatically record and transfer human movements to the robot, eliminating the time-consuming programming process while preserving precision
2Adaptability or versatility
If robotic systems are programmed to adapt to different 3D printed objects of varying sizes and dimensions, then adaptability is improved, but programming complexity increases
Solution Approach 1:
The system enables dynamic adaptation by capturing human movements that naturally adjust to different object characteristics. When humans work with varying 3D printed objects, their movements automatically adapt to size and dimension changes, and this adaptive behavior is recorded and transferred to the robot, allowing it to handle different objects without reprogramming
Solution Approach 2:
The robotic system achieves self-adaptation through motion capture technology. Instead of requiring programmers to manually configure the robot for each new object type, the system allows human operators to demonstrate the required adaptations, and the robot automatically learns and replicates these adaptive movements, making the system self-configuring
3Measurement precision
If more sensors and training equipment are added to the robotic system, then training accuracy is improved, but device complexity increases
Solution Approach 1:
The motion tracking sensors serve multiple functions: they capture position, orientation, velocity, and acceleration data simultaneously, and can track both the operator's body movements and tool movements. This multi-functionality allows high measurement precision without proportionally increasing system complexity, as a single sensor system accomplishes multiple measurement tasks
Data Source
AI summary
The present disclosure provides systems and methods for training a robot. The systems and methods may provide a robotic system. The robotic system may comprise a trainable robot and a sensor. The sensor may be attached to at least one physical tool. The method may comprise using the sensor to capture movement from a user operating the at least one physical tool. The method may include using at least the movement captured to train the robot, such that upon training, the robot may be trained to perform at least the movement.