Motion Platform Research Articles

B-Spline Wavelet Neural-Network-Based Adaptive Control for Linear-Motor-Driven Systems via a Novel Gradient Descent Algorithm

This paper proposes a gradient descent (GD) algorithm-based B-Spline wavelet neural network (GDBSWNN) learning adaptive controller for linear motor (LM) systems under system uncertainties and actuator saturation constraints. A recursive least squares (RLS) algorithm-based indirect adaptive strategy is used to effectively estimate model parameters, which can guarantee they converge to true values. A novel GDBSWNN compensator is proposed to estimate the remaining complex uncertainties, where weights are updated by online GD training. An auxiliary system is integrated into the control scheme to address the saturation problem, which guarantees stability and satisfactory control performance when saturation occurs. In addition, a stability analysis is presented to prove that all signals of the closed-loop system are bounded using the Lyapunov theory. Experiments have been conducted on an LM-driven motion platform, where different controllers have been tested, demonstrating the effectiveness and advantages of the proposed approach.

The Duckbot: A system for automated imaging and manipulation of duckweed.

Laboratory automation can boost precision and reproducibility of science workflows. However, current laboratory automation systems are difficult to modify for custom applications. Automating new experiment workflows therefore requires development of one-off research platforms, a process which requires significant time, resources, and experience. In this work, we investigate systems to lower the threshold to automation for plant biologists. Our approach establishes a direct connection with a generic motion platform to support experiment development and execution from a computational notebook environment. Specifically, we investigate the use of the open-source tool-changing motion platform Jubilee controlled using Jupyter notebooks. We present the Duckbot, a machine customized for automating laboratory research workflows with duckweed, a common multicellular plant. The Duckbot comprises (1) a set of end-effectors relevant for plant biology, (2) software modules which provide flexible control of these tools, and (3) computational notebooks which make use of these tools to automate duckweed experiments. We demonstrate the Duckbot's functionality by automating a particular laboratory research workflow, namely, duckweed growth assays. The Duckbot supports setting up sample plates with duckweed and growth media, gathering image data, and conducting relevant data analysis. We discuss the opportunities and limitations for developing custom laboratory automation with this platform and provide instructions on usage and customization.

Optimal data-driven control of manufacturing processes using reinforcement learning: an application to wire arc additive manufacturing

AbstractNowadays, artificial intelligence (AI) has become a crucial Key Enabling Technology with extensive application in diverse industrial sectors. Recently, considerable focus has been directed towards utilizing AI for the development of optimal control in industrial processes. In particular, reinforcement learning (RL) techniques have made significant advancements, enabling their application to data-driven problem-solving for the control of complex systems. Since industrial manufacturing processes can be treated as MIMO non-linear systems, RL can be used to develop complex data-driven intelligent decision-making or control systems. In this work, the workflow for developing a RL application for industrial manufacturing processes, including reward function setup, development of reduced order models and control policy construction, is addressed, and a new process-based reward function is proposed. To showcase the proposed approach, a case study is developed with reference to a wire arc additive manufacturing (WAAM) process. Based on experimental tests, a Reduced Order Model of the system is obtained and a Deep Deterministic Policy Gradient Controller is trained with aim to produce a simple geometry. Particular attention is given to the sim-to-real process by developing a WAAM simulator which allows to simulate the process in a realistic environment and to generate the code to be deployed on the motion platform controller.

A Manipulator Pose Planning Algorithm Based on Matrix Information Geometry

In an automatic ultrasonic testing system constituted by an ultrasonic probe and a six-axis manipulator, the manipulator needs to run from a static state to the target velocity. To prevent equipment damage caused by sudden acceleration or deceleration, it is necessary to plan the position and pose of the end effector of the manipulator at each detected point. In this manuscript, an algorithm for planning the position and pose of the manipulator is proposed based on the information geometry structure of special orthogonal groups. As the linear operation of the orthogonal matrix corresponding to the manipulator pose is not closed, the manipulator pose at each detected point was calculated using the straightness of the Lie algebra of the special orthogonal group. The matrix information geometry algorithm enabled not only the manipulator to accelerate and decelerate uniformly along the detection trajectory, but also the angular acceleration of the end effector to accelerate uniformly at first, then keep a uniform velocity, and finally decelerate uniformly. The platform motion experiments with the Turin TKB070S six-axis manipulator are carried out to verify the effectiveness of the matrix information geometry algorithm for planning the pose of the manipulator.

Exponential Fusion of Interpolated Frames Network (EFIF-Net): Advancing Multi-Frame Image Super-Resolution with Convolutional Neural Networks.

Convolutional neural networks (CNNs) have become instrumental in advancing multi-frame image super-resolution (SR), a technique that merges multiple low-resolution images of the same scene into a high-resolution image. In this paper, a novel deep learning multi-frame SR algorithm is introduced. The proposed CNN model, named Exponential Fusion of Interpolated Frames Network (EFIF-Net), seamlessly integrates fusion and restoration within an end-to-end network. Key features of the new EFIF-Net include a custom exponentially weighted fusion (EWF) layer for image fusion and a modification of the Residual Channel Attention Network for restoration to deblur the fused image. Input frames are registered with subpixel accuracy using an affine motion model to capture the camera platform motion. The frames are externally upsampled using single-image interpolation. The interpolated frames are then fused with the custom EWF layer, employing subpixel registration information to give more weight to pixels with less interpolation error. Realistic image acquisition conditions are simulated to generate training and testing datasets with corresponding ground truths. The observation model captures optical degradation from diffraction and detector integration from the sensor. The experimental results demonstrate the efficacy of EFIF-Net using both simulated and real camera data. The real camera results use authentic, unaltered camera data without artificial downsampling or degradation.

Numerical Investigation into the Dynamic Responses of Floating Photovoltaic Platform and Mooring Line Structures under Freak Waves

Floating photovoltaics (PVs) are progressively constructed in the ocean sea; therefore, the effect that freak waves have on their structural design needs to be considered. This paper developed a dedicated numerical model coupling the floating PV platform and mooring line structures to investigate their dynamic responses under freak waves. A feasible superposition approach is presented to generate freak wave sequences via the combination of transient waves and random waves. A large floating PV platform moored by twenty lines for a water depth of 45 m was designed in detail according to the actually measured ocean environmental and geological conditions. The global time domain analyses of the floating PV mooring structures were implemented to obtain dynamic responses, including PV platform motions and the mooring line configuration and tension under freak waves. A comparison of the response results with those caused by random waves was conducted to illustrate the intuitive evidence of the freak wave effects, which offer a significant reference for the preliminary design of the floating PV platform and mooring line structures.

Augmenting Robot-Assisted Pattern Cutting with Periodic Perturbations-Can We Make Dry Lab Training More Realistic?

Teleoperated robot-assisted minimally-invasive surgery (RAMIS) offers many advantages over open surgery, but RAMIS training still requires optimization. Existing motor learning theories could improve RAMIS training. However, there is a gap between current knowledge based on simple movements and training approaches required for the more complicated work of RAMIS surgeons. Here, we studied how surgeons cope with time-dependent perturbations. We used the da Vinci Research Kit and investigated the effect of time-dependent force and motion perturbations on learning a circular pattern-cutting surgical task. Fifty-four participants were assigned to two experiments, with two groups for each: a control group trained without perturbations and an experimental group trained with 1Hz perturbations. In the first experiment, force perturbations alternatingly pushed participants' hands inwards and outwards in the radial direction. In the second experiment, the perturbation constituted a periodic up-and-down motion of the task platform. Participants trained with perturbations learned how to overcome them and improve their performances during training without impairing them after the perturbations were removed. Moreover, training with motion perturbations provided participants with an advantage when encountering the same or other perturbations after training, compared to training without perturbations. Periodic perturbations can enhance RAMIS training without impeding the learning of the perturbed task. Our results demonstrate that using challenging training tasks that include perturbations can better prepare surgical trainees for the dynamic environment they will face with patients in the operating room.

Active Disturbance Rejection Fractional-Order Independent Control of Time Delay Systems With Application to Air Floating Motion Platform

Active Disturbance Rejection Fractional-Order Independent Control of Time Delay Systems With Application to Air Floating Motion Platform

The Remove-the-Mask Open-Source head and neck Surface-Guided radiation therapy system

Background and PurposeSurface Guided Radiotherapy (SGRT) for head and neck radiotherapy is challenging as obstructions are common and non-rigid facial motion can compromise surface accuracy. The purpose of this work was to develop and benchmark the Remove the Mask (RtM) SGRT system, an open-source system especially designed to address the challenges faced in radiotherapy of head and neck cancer. Materials and MethodsThe accuracy of the RtM SGRT system was benchmarked using a head phantom positioned on a robotic motion platform capable of sub-millimetre accuracy which was used to induce unidirectional shifts and to reproduce three real head motion traces. We also assessed the accuracy of the system in ten humans volunteers. The ground truth motion of the volunteers was obtained using a commercial motion capture system with an accuracy < 0.3 mm. ResultsThe mean tracking error of the RtM SGRT system for the ten volunteers was of −0.1 ± 0.4 mm −0.6 ± 0.6 mm and 0.3 ± 0.2 mm, and 0.0 ± 0.2° 0.0 ± 0.1° and 0.0 ± 0.2° for translations and rotations along the left–right, superior-inferior and anterior-posterior axes respectively and we also found similar results in measurements with the head phantom. Forced facial motion was associated with lower tracking accuracy. The RtM SGRT system achieved submillimetre accuracy. ConclusionThe RtM SGRT system is a low-cost, easy to build and open-source SGRT system that can achieve an accuracy that meets international commissioning guidelines. Its open-source and modular design allows for the development and easy translation of novel surface tracking techniques.

Perpendicularity measurement of multi-axis motion platform based on laser interferometry

Perpendicularity measurement of multi-axis motion platform based on laser interferometry

Design and Development of a Miniaturized Roto-Translational Motion Platform for Micro Aerial Vehicle Identification

Design and Development of a Miniaturized Roto-Translational Motion Platform for Micro Aerial Vehicle Identification

Analysis of Repetitive Positioning Accuracy of Parallel Mechanisms Based on Monte Carlo Method

Based on the inverse kinematics model of the Stewart parallel mechanism, a motion platform pose error model was constructed through differential transformation. The error model shows that the motion platform pose error comes from the length error of 6 struts and the position error of 12 hinge centers, with a total of 42 error terms. Monte Carlo simulation was used to analyze the effects of struts length error on the pose error of the moving platform. The results show that when the length error of the struts follows a normal distribution, the posture error of the moving platform approximately follows a normal distribution. Monte Carlo simulation is a highly effective error analysis method that can analyze the error distribution pattern of the mechanism before processing and assembly, which provides a method for error analysis of Stewart parallel mechanisms.

Lorentz Magnetic Bearings for Laser Massive Transfer Composite Motion Platforms

Lorentz Magnetic Bearings for Laser Massive Transfer Composite Motion Platforms

Design and Testing of an Active Balancing Board for Exergames

In recent years, there has been a growing recognition of the importance of balance and coordination in maintaining overall health, particularly among the elderly population. As societies around the world experience demographic shifts with an increasing number of elderly individuals, age-related declines in balance and coordination can lead to an elevated risk of falls and fall-related injuries, making it a significant public health concern. The need for effective balance rehabilitation and fall prevention strategies has become paramount. This paper presents a motion platform designed for balance rehabilitation exercises using exergames. The system consists of a 3 degrees of freedom (3DOF) platform actuated by four servomotors controlled by Omron-DeltaTau real-time motion controller. As a novelty with respect to other State of Art (SoA) actuated platforms, in the present work the movements of the user's body, which determine the platform motion, are tracked by exploiting a markerless motion capture system with an RGB camera and pose-estimation algorithm MediaPipe. The human displacement is used to generate the input for the roll rotation of the platform according to a one d.o.f. transfer function, whose parameters can be set to get different levels of difficulties in the exercise. The device offers therefore the flexibility to set different parameters to modify the level of the users’ training. The proposed device has the potential for a larger variety of rehabilitation activities and exergames. This paper describes the elements adopted for the implementation of this human-in-the-loop system, presents some preliminary tests to explore the influence of transfer function parameters on human and platform movements, and concludes with future development possibilities.

Three-Dimensional Sensation Using a Small Motion Platform Moving on a Flat Surface

Three-Dimensional Sensation Using a Small Motion Platform Moving on a Flat Surface

Adaptive Nonlinear Power Regulation Control of Floating Wind Turbines With Platform Motion Reduction

Adaptive Nonlinear Power Regulation Control of Floating Wind Turbines With Platform Motion Reduction

Analysis and compensation of stop-and-go approximation in high-resolution spaceborne synthetic aperture radar

ABSTRACT With the advancement of spaceborne synthetic aperture radar (SAR) resolution, the conventional stop-and-go approximation-based motion model is no longer suitable for high-resolution imaging algorithms. This paper conducts a comprehensive analysis of the approximation errors resulting from the stop-and-go approximation and proposes corresponding compensation methods. Firstly, an accurate echo model based on continuous platform motion is derived from the SAR geometry configuration. The impact of these errors on range and azimuth imaging is analysed from a spectral perspective, and the applicable boundary conditions are examined. Subsequently, compensation methods are proposed to eliminate the introduced approximation errors, addressing both”fast time” and”slow time” effects, thus enabling high-resolution imaging through improvements in traditional imaging algorithms. Experimental simulations validate the effectiveness of the error analysis and compensation methods, using X-band low Earth orbit SAR as an illustrative example. The proposed methods are not only compatible with existing mature imaging algorithms, but the analytical approach and compensation methods are also applicable to SAR systems operating on different carrier frequencies, orbits, and platforms.

Disain CAD Mekanisme Paralel Tiga Derajat Translasi Murni Dengan Rantai Kinematik Kombinasi Sambungan Revolut dan Prismatik

&#x0D; &#x0D; &#x0D; &#x0D; In this study it was discussed kinematic design of a 3-RRRPP translational parallel mechanism. The design was carried out by using simulation performed using CAD software. Motion platform was analyzed based on screw and reciprocal screw theory. The evaluation mechanism performance was determined by introducing the stages of research in the form to determine mechanism parts such as base, links, and platform. The work was continued with the assembly process of each part. Furthermore, kinematic response represented by displacement of platform positions was figured out by a CAD simulation. Based on the results of the simulation, it was successfully designed the mechanism performing the translational motion of the Platform.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;

A computational framework for the co-optimization of platform hydrodynamic and passive structural control of floating offshore wind turbines

Floating offshore wind turbines extract rich and stable wind energy in the deep sea. However, they face severe sea conditions and high maintenance costs, and a promising method is needed to generate a high-performing, reliable, and robust design. Traditional sequential methods overlook the coupling between sub-systems and may miss the global optimum. The control co-design method, in contrast, focuses on the integrated optimization of the whole system. However, implementing co-simulation and co-optimization for co-design is challenging. In this study, a control co-design framework is developed and validated to couple the platform hydrodynamics with multi-physical wind turbines for integrated simulations and optimization. Based on this framework, the floating platform and tuned mass damper are co-optimized by establishing an efficient Kriging-enhanced Genetic algorithm. A case study is conducted using a 10 MW floating offshore wind turbine for demonstration. The platform shape and tuned mass dampers are parameterized by five and three design variables, respectively. To minimize platform mass, two formulations are proposed, that is, multi- and single-objective, for scenarios with and without consideration of structural loads. These formulas are solved under pre-defined turbulent wind and irregular wave conditions. Compared to the baseline model, the optimal design significantly reduces platform mass, enhancing both power generation and platform motion stability. After involving structural load in optimization, the optimal platform mass, energy production, and platform motion stability are degraded. These findings demonstrate the effectiveness of the co-design framework in optimizing platform geometry and passive structural control.

Analysis of Positioning Accuracy in Case of Design Errors in the Installation of Mecanum Wheels of the Mobile Platform

Introduction. Mobile robots capable of omnidirectional movement are widely used in various fields of human activity. To provide high accuracy of positioning of omnidirectional platforms with mecanum wheels, it is required to develop their detailed mathematical models used in the construction of a motion control system. Due to the complicated design of the mecanum wheels, various errors may occur during the construction of omnidirectional platforms, including the error of installing such wheels on the platform. Its effect on the accuracy of the platform movement has not been studied before. This work aims at assessing the positioning errors that arise due to the presence of design errors in the installation of mecanum wheels, and analyzing the effect of these errors on the accuracy of program motion testing when using control at the kinematic level.Materials and Methods. The analysis of positioning accuracy was based on mathematical modeling of the platform kinematics, taking into account structural errors in the installation of mecanum wheels. To describe the relationship between the angular speeds of rotation of the wheels and the speeds of the platform, the conditions of nonslip of the contact points on the support surface were used. Numerical calculations were carried out in the Wolfram Mathematica package.Results. A formula was obtained for estimating errors in platform pseudovelocities under program control formed at the kinematic level. The estimation of the errors of the platform speeds for simple movements was carried out. According to the calculation results, it has been shown that the speed errors are significant for robots with mecanum wheels operating autonomously.Discussion and Conclusion. The calculation results demonstrated the significant impact of wheel installation errors on the positioning accuracy of the mecanum-platform, and confirmed the need to take into account these design errors when creating autonomous mecanum-platforms. The constructed model of the robot's kinematics makes it possible to predict errors in platform speeds that arise under program control, as well as deviations of the coordinates of the geometric center of the platform from the program motion. The proposed kinematic model can be used to improve the positioning accuracy through forming a platform motion control that compensates for the influence of wheel installation errors.