Hardware-Accelerated Visual Servoing for Low-Latency Error Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Visual Servoing (VS) techniques face challenges in computing configuration errors from visual information due to noise and disturbances, and require computationally expensive online image processing, limiting their practical implementation in robotic control systems.
Innovation Solution
A machine learning-based system utilizing a deep neural network driven by a hardware accelerator for visual servoing, which processes visual content to determine a low-dimensional configuration error, enabling real-time adaptable control with low latency and high sampling rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional visual servoing with hand-designed feature detectors is used, then the system can extract visual features from images, but the computation is expensive and latency is high due to online image processing requirements
Solution Approach 1:
The system performs preliminary action by pre-training a deep neural network offline to learn visual feature representations and configuration error mappings. During runtime, the pre-trained network performs fast inference without requiring expensive online image processing, thus reducing control loop latency while maintaining accurate configuration error estimation.
Solution Approach 2:
The patent substitutes traditional mechanical image processing pipelines (hand-designed feature detectors, filters, and extraction algorithms) with a deep neural network-based computational system. This substitution enables parallel processing on GPUs or TPUs, dramatically reducing computation time and latency while improving measurement precision through learned features.
2Measurement precision
If deep learning models are used for configuration error estimation, then measurement precision improves, but device complexity increases due to hardware accelerator requirements
Solution Approach 1:
The system achieves universality by designing a modular deep neural network architecture that can handle multiple visual servoing tasks through a single unified model. The network processes various image inputs and outputs configuration errors for different robotic operations, reducing the need for task-specific hardware and simplifying the overall system while maintaining high measurement precision.
3Speed
If online image processing is performed in the control loop, then real-time feedback is achieved, but computational cost increases significantly
Solution Approach 1:
The system performs preliminary action by pre-computing and storing learned feature representations and error mappings during an offline training phase. During real-time control, the system only performs lightweight inference using the pre-trained model, dramatically reducing computational energy consumption while maintaining high sampling rates for real-time feedback.
Solution Approach 2:
The patent changes parameters by optimizing the deep neural network for efficient inference (e.g., quantization, pruning, knowledge distillation) and deploying it on energy-efficient hardware accelerators. This allows the system to achieve high sampling rates with significantly reduced energy consumption compared to conventional online image processing methods.
Data Source
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.