Vision-Guided Robotic Control for Actuator Variation 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 by incorporating past responses into future predictions through a recurrent neural network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expensive components and traditional control methods are used, then high precision and accuracy in robot pose control is achieved, but system cost increases and device complexity increases
Solution Approach 1:
The patent replaces traditional mechanical control systems with a vision-based software control system. Instead of relying on expensive precision mechanical components and complex hardware feedback systems, the invention uses computer vision to capture robot movement images, processes them through neural networks to generate control signals, and achieves precise pose control through software-based compensation for actuator variations and model inaccuracies.
Solution Approach 2:
The patent implements a real-time feedback loop where the vision system continuously captures images of the robot's actual movement, the neural network processes these images to determine deviations from desired pose, and control signals are adjusted accordingly. This closed-loop feedback mechanism enables high precision control without requiring expensive precision components, as the system dynamically compensates for variations in actuator response and model inaccuracies.
2Measurement precision
If traditional control methods with expensive components are used, then high precision and accuracy is achieved, but adaptability to different materials and movement generation devices is reduced
Solution Approach 1:
The patent creates a universal control system that can work with various movement generation devices and materials. The vision-based neural network controller is device-agnostic, meaning it can control different types of actuators and work with various materials without requiring specialized hardware. The software compensates for device-specific variations, allowing the same control system to adapt to tendon mechanisms, traditional actuators, and future device types equally well.
Solution Approach 2:
The patent changes the control parameters from fixed mechanical specifications to dynamic visual feedback parameters. Instead of relying on predetermined mechanical models and fixed control parameters that limit adaptability, the system uses real-time image data and neural network processing to dynamically adjust control signals. This allows the system to adapt to different materials and devices by learning their characteristics through visual feedback rather than requiring pre-programmed parameters for each device type.
3Measurement precision
If real-time vision-based feedback control is implemented, then adaptability and precision are improved, but processing time and computational requirements increase
Solution Approach 1:
The patent performs preliminary action by pre-training the neural network offline with large datasets of robot movements and actuator characteristics. This pre-training phase captures the essential patterns and relationships needed for control, allowing the deployed system to make rapid real-time decisions based on previously learned knowledge. The computationally intensive work is done beforehand, enabling fast real-time inference during actual robot operation.
Solution Approach 2:
The patent segments the control system into distinct functional modules: image capture, neural network processing, control signal generation, and feedback compensation. This modular architecture allows each component to be optimized independently and enables parallel processing where possible. The segmentation also facilitates efficient resource allocation, with the vision processing and control computation distributed across appropriate hardware resources to minimize processing time.
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.