3D Eye-to-Hand Pose Compensation for Robotic Accuracy
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Solution Overview
Problem
Current robotic systems face challenges in achieving sub-millimeter accuracy and repeatability due to positioning/movement errors and measurement errors in 3D eye-to-hand coordination, which limits their effectiveness in manufacturing tasks, especially when handling small components in confined spaces.
Innovation Solution
A robotic system that includes a machine-vision module, a robotic arm, and an error-compensation module using a neural network to determine an error matrix, which correlates the camera-instructed pose to the controller-desired pose, allowing real-time compensation for pose errors and improving accuracy by training the neural network with supervised learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional robotic control systems are used without error compensation, then the system structure remains simple, but the positional accuracy and repeatability deteriorate due to positioning errors and measurement errors in 3D eye-to-hand coordination
Solution Approach 1:
An error-compensation module is introduced as an intermediary component between the robotic controller and the machine-vision module. This module contains a neural network that processes camera-instructed poses and outputs corrected controller-desired poses, effectively mediating the coordination between vision and control to eliminate positioning errors without requiring fundamental changes to the robotic system architecture
Solution Approach 2:
The patent replaces traditional mechanical error compensation methods with a software-based neural network approach. Instead of using complex mechanical calibration mechanisms or physical adjustment devices, the system uses a trained neural network to computationally correct pose errors, substituting mechanical complexity with intelligent algorithmic processing
2Manufacturing precision
If the robotic arm is designed for high accuracy in confined spaces, then the manufacturing precision improves, but the device complexity increases due to additional sensors and control mechanisms
Solution Approach 1:
The error-compensation module serves multiple functions simultaneously: it corrects positioning errors, compensates for measurement errors from the vision system, and adapts to different operational conditions. This single multi-functional module replaces what would otherwise require multiple separate sensors, actuators, and control mechanisms, achieving high accuracy without proportionally increasing device complexity
3Manufacturing precision
If extensive calibration is performed to reduce positioning errors, then the manufacturing precision improves, but the time required for setup and maintenance increases
Solution Approach 1:
The neural network is trained in advance during an offline calibration phase to learn the error patterns of the robotic system. Once trained, the network can rapidly compensate for positioning errors during actual operation without requiring time-consuming real-time calibration procedures. The preliminary training action enables fast error correction during production
Solution Approach 2:
The system implements continuous feedback through the machine-vision module that monitors the actual position of the end-effector and feeds this information back to the error-compensation module. The neural network uses this feedback to dynamically adjust pose corrections, enabling adaptive error compensation that maintains high accuracy without requiring repeated manual calibration
Data Source
AI summary
One embodiment can provide a robotic system. The system can include a machine-vision module, a robotic arm comprising an end-effector, a robotic controller configured to control movements of the robotic arm, and an error-compensation module configured to compensate for pose errors of the robotic arm by determining a controller-desired pose corresponding to a camera-instructed pose of the end-effector such that, when the robotic controller controls the movements of the robotic arm based on the controller-desired pose, the end-effector achieves, as observed by the machine-vision module, the camera-instructed pose. The error-compensation module can include a machine learning model configured to output an error matrix that correlates the camera-instructed pose to the controller-desired pose.