# A Vision-Based Micro-Manipulation System
## 2 Related Work
### Positioning Accuracy of Micro-Manipulators  
In response to the escalating demand for advanced positioning systems, ball screw-based mechanisms, driven by either servo drives or stepper motors, have emerged as an important choice, due to their capacity to deliver exceptional levels of positioning accuracy and repeatability. A pivotal approach to mitigating the transient effects associated with these systems is the formulation of mathematical models, as emphasized in prior works [12; 13]. Mathematical modeling encompasses the empirical characterization of the system behavior across a diverse spectrum of operational scenarios, thereby streamlining development efforts and facilitating the integration of contemporary control methodologies.  
Leveraging the potency of machine learning techniques is recognized as a way to attain the utmost precision in system positioning. [PERSON] et al. discuss the development of a deep reinforcement learning-based neuro-control system for magnetic positioning system applications. Two neuro-controllers were trained to control the \(X\) and \(Y\)-axis motions of the positioning system. The training was based on the Q-learning method with an actor-critic architecture, and the parameters were updated using gradient descent techniques to maximize a reward function defined in terms of positioning accuracy. The performance of the control system was verified for different setpoints and working conditions, demonstrating the effectiveness of the proposed method [14]. Another paper addresses the issue of hysteresis nonlinearity in a piezoelectric micro-positioning platform (PMP), which limits its positioning accuracy. It introduces a Krasnosel'skii-Pokrovskii (KP) model to describe the hysteresis behavior, and involves an adaptive linear neural network for real-time model identification. To compensate for hysteresis, the paper presents a feed-forward control method and a hybrid approach combining iterative learning control and fractional order Proportional-Integral-Derivative (PID) control, which is validated through experiments and significantly enhances control accuracy [15].  
[PERSON] et al. introduce a method that combines machine vision and machine learning to determine the correctness of read positioning and estimate adjusting displacements. The back propagation neural network (BPNN) achieved 100% accuracy in assessing correctness and a measuring precision of \(\pm\)0.025 mm for displacement estimation, providing an effective solution for improving the manufacturing process of aerophones [16]. [PERSON] et al. explore the challenge of manually controlling manipulator robots with more degrees of freedom than can be managed through traditional input methods. They propose a solution that combines eye tracking and computer vision to automate robot positioning toward a 3D target point. This approach significantly improves precision, reducing the average error and making it a valuable tool for robot control and rehabilitation engineering [17]. Contemporary scanning probe microscopes, including scanning and tunneling electron microscopes, have increasingly incorporated visual recognition and machine learning techniques for the extraction of intricate data from acquired images [18]. However, the utilization of these data has hitherto not extended to the autonomous localization of target locations, precise selection of measurement positions, and the rectification of inherent inaccuracies.