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
1Measurement precision
If traditional inverse kinematics control is used with expensive precision components, then high precision and accuracy in robot pose control is achieved, but device complexity and cost increase
Solution Approach 1:
The patent replaces traditional mechanical precision control systems (servo motors, encoders, gear boxes) with a vision-based control system using cameras and neural networks. The system captures images of the robot, processes them through a neural network to determine actual pose, and uses this visual feedback to control movement, eliminating the need for expensive precision mechanical components while achieving comparable or superior accuracy.
Solution Approach 2:
The patent implements a visual feedback loop where cameras continuously capture the robot's position and orientation, the neural network processes these images to determine actual pose, and this information feeds back to adjust control signals. This closed-loop visual feedback system compensates for uncertainties in the robot model and actuator variations, achieving high precision without expensive precision components.
2Measurement precision
If traditional inverse kinematics control is used with expensive precision components, then high precision and accuracy in robot pose control is achieved, but cost increases
Solution Approach 1:
The patent replaces expensive, durable precision components (servo motors, encoders) with cheaper alternatives (standard motors, cameras, neural network software). The system uses inexpensive visual sensors and computational algorithms instead of costly mechanical precision devices, significantly reducing manufacturing cost while maintaining control precision.
Solution Approach 2:
The patent substitutes mechanical precision control systems with a software-based vision control system. Instead of relying on expensive precision hardware, the system uses image processing and neural networks to achieve accurate pose control, dramatically reducing component costs.
3Adaptability or versatility
If tendon mechanisms are used for movement generation, then adaptability and material choices improve, but precision and accuracy deteriorate due to actuator response variations
Solution Approach 1:
The patent implements visual feedback that continuously monitors the robot's actual pose and compares it to the desired pose. The neural network processes this visual information and generates corrected control signals that compensate for actuator variations in tendon mechanisms. This feedback loop maintains precision despite using flexible, adaptive materials like tendons that exhibit response variations.
Solution Approach 2:
The system allows the robot to self-correct its positioning errors by using visual feedback from cameras. The neural network analyzes the captured images, determines the discrepancy between actual and desired pose, and automatically adjusts control signals to compensate for actuator variations, enabling tendon mechanisms to achieve precise control without requiring ultra-precise 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.