Vision-Based Robot Pose Control with Neural Compensation
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Solution Overview
Problem
Achieving high precision and accuracy in robot pose control is challenging due to variations in actuator response and model inaccuracies, particularly when handling heavy loads, which often requires expensive components and excludes certain materials and movement generation devices like tendon mechanisms.
Innovation Solution
A vision-based robot control system using a real-time feedback loop with a multi-stage neural network that processes images from cameras to generate control signals, compensating for actuator variations and model inaccuracies through a recurrent neural network that incorporates past responses to predict future movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If expensive precision components are used to achieve high precision and accuracy in robot pose control, then precision and accuracy improve, but device complexity and cost increase
Solution Approach 1:
The patent replaces traditional mechanical precision control systems with a vision-based software control system. Instead of relying on expensive precision mechanical components and encoders, the system uses camera images processed through neural networks to detect robot pose and generate control signals, achieving high precision through software rather than hardware
Solution Approach 2:
The patent creates a virtual model (digital twin) of the robot that mirrors the physical robot's movements. The neural network learns the relationship between camera images and robot pose by training on data from the virtual model, allowing the system to achieve precision without requiring expensive physical precision components
2Manufacturing precision
If traditional control methods with encoders and servo motors are used, then pose control accuracy improves, but the system excludes certain materials and movement generation devices like tendon mechanisms
Solution Approach 1:
The patent replaces encoder-based mechanical feedback systems with vision-based optical feedback. This substitution allows the use of tendon mechanisms and other materials that are incompatible with traditional encoders, as the system only requires visual detection of pose rather than direct mechanical measurement
Solution Approach 2:
The vision-based control system provides universal compatibility with various movement generation devices and materials. The same camera and neural network system can control different robot configurations including tendon-driven manipulators, flexible robots, and traditional rigid robots, making the system highly adaptable
3Manufacturing precision
If high precision components are used to account for actuator variations and model inaccuracies, then pose accuracy improves, but cost and complexity increase
Solution Approach 1:
The patent implements a closed-loop feedback system where camera images continuously provide information about actual robot pose. The neural network compares detected pose with desired pose and generates corrective control signals, automatically compensating for actuator variations and model inaccuracies without requiring expensive precision components
Solution Approach 2:
The system performs self-calibration and self-correction through the neural network's learning capability. The network automatically adapts to the specific robot system and operating conditions, compensating for variations and inaccuracies without requiring external calibration procedures or additional precision components
Data Source
AI summary
A software compensated robotic system makes use of recurrent neural networks and image processing to control operation and/or movement of an end effector. Images are used to compensate for variations in the response of the robotic system to command signals. This compensation allows for the use of components having lower reproducibility, precision and/or accuracy that would otherwise be practical.