Visual Servoing With Hardware-Accelerated Neural Error Estimation
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Solution Overview
Problem
Visual servoing systems face challenges in computing configuration errors from visual information due to noise and disturbances, and require high computational costs for real-time control, limiting their practical implementation and robustness.
Innovation Solution
Implementing a machine learning-based configuration error estimation using a hardware accelerator for visual servoing, which enables low-latency image processing and adaptable control loops.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional image processing methods are used in visual servoing control loops, then the system can process visual information, but the computational cost is too high and latency is too large for real-time control
Solution Approach 1:
The patent replaces conventional software-based image processing with a dedicated hardware accelerator that implements neural network inference in hardware. This substitution of mechanical/computational approach enables real-time processing by performing parallel computations at hardware speed, achieving microsecond-level latency compared to milliseconds with software methods.
Solution Approach 2:
The patent introduces a hardware accelerator as an intermediary component between the visual sensor and the control system. This dedicated processing unit acts as a mediator that performs neural network-based feature extraction and configuration error estimation, offloading the computationally intensive tasks from the main control processor and enabling real-time operation.
2Reliability
If hand-engineered feature detectors are used for visual feature extraction, then the system can identify visual features, but the robustness is insufficient and engineering effort is large
Solution Approach 1:
The patent replaces hand-engineered feature detectors with a neural network-based approach implemented in hardware. Instead of manually designing and tuning feature extraction algorithms, the system uses a trained neural network model that automatically learns robust feature representations from data, significantly improving robustness while reducing the need for manual engineering.
Solution Approach 2:
The patent changes the fundamental approach to feature detection by transitioning from fixed hand-crafted features to adaptive neural network features. The neural network model can be trained on specific datasets and adjusted to different tasks, allowing the system to adapt to various visual environments and object types without requiring complete re-engineering of the feature detection pipeline.
Data Source
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AI summary
A system controller for visual servoing includes a technology module with dedicated hardware acceleration for deep neural network that retrieves a desired configuration of a workpiece object being manipulated by a robotic device and receives visual feedback information from one or more sensors on or near the robotic device that includes a current configuration of the workpiece object. The hardware accelerator executes a machine learning model trained to process the visual feedback information and determine a configuration error based on a difference between the current configuration of the workpiece object and the desired configuration of the workpiece object. A servo control module adapts a servo control signal to the robotic device for manipulation of the workpiece object in response to the configuration error.