Motion Prediction Algorithm Research Articles

Advances in Powered Ankle-Foot Prostheses.

We present a review of recent developments in powered ankle-foot prostheses (PAFPs), with emphasis on actuation, high- and low-level control strategies, and pneumatic, hydraulic, and electromechanical actuators. A high-level control strategy based on finite-state machines, combined with low-level control that drives the ankle torque, is the most common control strategy. On the other hand, brushless direct-current motors along with an energy storage and release mechanism are commonly used to reduce the overall size of the actuators and increase PAFP autonomy. Most designs have been evaluated experimentally, showing acceptable results in walking velocity and gait symmetry. Future research must focus on reducing weight, increasing energy efficiency, improving gait phase classification and/or intent of motion-prediction algorithms, updating low-level control of torque and position, and developing the ability of the patient to walk on sloped surfaces and negotiate stairs.

Motion prediction of a non-cooperative space target

Capturing a non-cooperative space target is a tremendously challenging research topic. Effective acquisition of motion information of the space target is the premise to realize target capture. In this paper, motion prediction of a free-floating non-cooperative target in space is studied and a motion prediction algorithm is proposed. In order to predict the motion of the free-floating non-cooperative target, dynamic parameters of the target must be firstly identified (estimated), such as inertia, angular momentum and kinetic energy and so on; then the predicted motion of the target can be acquired by substituting these identified parameters into the Euler’s equations of the target. Accurate prediction needs precise identification. This paper presents an effective method to identify these dynamic parameters of a free-floating non-cooperative target. This method is based on two steps, (1) the rough estimation of the parameters is computed using the motion observation data to the target, and (2) the best estimation of the parameters is found by an optimization method. In the optimization problem, the objective function is based on the difference between the observed and the predicted motion, and the interior-point method (IPM) is chosen as the optimization algorithm, which starts at the rough estimate obtained in the first step and finds a global minimum to the objective function with the guidance of objective function’s gradient. So the speed of IPM searching for the global minimum is fast, and an accurate identification can be obtained in time. The numerical results show that the proposed motion prediction algorithm is able to predict the motion of the target.

In brief

PAGE 1192 Researchers in Korea have developed a bi-directional deformable block-based motion-estimation algorithm for accurate motion prediction in 360-degree videos. A significant improvement in visual quality is demonstrated with this approach in comparison to conventional motion estimation techniques. The proposed algorithm can be used for virtual reality applications, motion estimation and frame-rate up conversion. PAGE 1180 Researchers in Australia have fabricated a non-invasive optical sensor for measuring knee movement in 3D space. The triangular sensor works by measuring the angle-of-arrival of optical signals and is integrated with a 2D image processing procedure. The reported sensor is shown to measure knee movement with a high precision that is within the clinically acceptable limit. Improved visual quality in 360-degree videos using a new motion-estimation algorithm. PAGE 1200 The development of sensors for rapid detection of hydrogen gas is currently a hot topic in renewable fuel research. Work from Korea proposes a Pt-functionalised hydrogen sensor developed on an AlGaN/GaN-on-Si heterostructure platform. The sensor was demonstrated to have high sensitivity and a rapid detection response time of 3 seconds in 4% hydrogen concentration. A novel non-invasive sensor design for measuring knee movement. PAGE 1203 A Decoupled Empty-Substrate Integrated Waveguide (DESIW) has been made by researchers in Spain. The DESIW design is a continuous conductive metal structure that enables AC/DC decoupling with integrated devices, reducing unwanted power losses. Measured scattering and transmission parameters show that the design is highly competitive with alternative technologies. The authors present a new hydrogen sensor with rapid detection response time. PAGE 1226 Lens-based millimeter-wave massive MIMO is a key technique for improving data transfer rates and efficiencies in 5G wireless communications. Work from China introduces an adaptive neighbourhood search algorithm for beam selection, which reduces the energy consumption and hardware costs in mm wave MIMO based systems. The proposed beam selection approach achieves near optimal performance with low complexity. A novel DESIW has been designed and manufactured to improve AC/DC decoupling efficiencies. PAGE 1178 The ability to control a wheelchair's movement through the act of thinking has been achieved by researchers in China. The design is controlled by a wireless dry EEG headset acquisition system and has five steering behaviours controlled by the brain-computer interface. A new adaptive neighbourhood search algorithm for efficient beam selection in 5G wireless technology is proposed. PAGE 1169 Work from China proposes a new approach to providing an antenna for slim electronic devices. Using the existing infrastructure of a laptop computer, the design integrates ground-planes and metal hinges to achieve high radiation efficiency without the physical presence of an antenna structure. The proposed design reduces the physical space taken up by an antenna with reduced manufacturing costs. Five steering behaviours are reported in a newly designed brain-controlled wheelchair. PAGE 1196 The reconstruction of images using compressive sensing can produce restoration precision errors resulting in blurring and low resolution. Researchers in China have developed a compressive sensing algorithm that uses a vector quantisation error to suppress the described precision errors. Experimental results demonstrate the algorithm outperforms previously reported compressive sensing methods in aspects of perception and peak signal-to-noise ratios. Designing an antenna within the hinges of slim electronic devices. PAGE 1209 The miniaturisation of a wideband bandpass filter is achieved using stepped impedance by researchers in India. The device also incorporates dual-mode resonators and three types of inter digital capacitors to achieve frequency selectivity. The authors demonstrate the device to display wide stopband properties suitable for terrestrial trunked radio applications. Vector quantisation error parameters are used to remove precision errors in compressive sensing methods. PAGE 1174 Work from Turkey presents a novel design of C-band Fabry-Perot resonator antenna with improved impedance and bandwidth gaining radiation characteristics. The active element of the antenna is a square patch with four crossing points. The antenna is reported to be suitable for a broad range of applications including satellite communication. A miniaturised wideband bandpass filter has been designed with functional frequency selectivity. A new Fabry-Perot resonator antenna design is proposed for satellite communication applications.

Chance-constrained target tracking using sensors with bounded fan-shaped sensing regions

We present a robust target tracking algorithm for a mobile robot. It is assumed that a mobile robot carries a sensor with a fan-shaped field of view and finite sensing range. The goal of the proposed tracking algorithm is to minimize the probability of losing a target. If the distribution of the next position of a moving target is available as a Gaussian distribution from a motion prediction algorithm, the proposed algorithm can guarantee the tracking success probability. In addition, the proposed method minimizes the moving distance of the mobile robot based on the chosen bound on the tracking success probability. While the considered problem is a non-convex optimization problem, we derive a closed-form solution when the heading is fixed and develop a real-time algorithm for solving the considered target tracking problem. We also present a robust target tracking algorithm for aerial robots in 3D. The performance of the proposed method is evaluated extensively in simulation. The proposed algorithm has been successful applied in field experiments using Pioneer mobile robot with a Microsoft Kinect sensor for following a pedestrian.

A particle filter motion prediction algorithm based on an autoregressive model for real-time MRI-guided radiotherapy of lung cancer

This work introduces a 2D motion prediction algorithm for lung tumors applied to dynamic MR images that combines intrafractional tumor deformations. It is developed in the context of MR-linac treatments and evaluates uniform and patient-specific margins about the gross tumor volume to optimize the tumor coverage. Seven early stage non-small cell lung cancer patients were imaged in treatment position with a 1.5 T magnetic resonance using a balanced steady-state free precession sequence for one minute at a rate of four images per second and were instructed to breathe normally. The particle filter, in combination with the autoregressive model, is used to sequentially track and predict the tumor position 250 ms in the future from the current image. In addition, the autocontour extracted from a previous study (Bourque et al 2016 Med. Phys. 43 5161–9) is projected to the predicted position and various margins are evaluated. Averaged over all patients, the root mean square errors are (1.3 ± 0.5) mm and (2.0 ± 0.8) mm with and without prediction, respectively, and the difference in centroid position is (1.1 ± 0.4) mm with the prediction. The addition of the prediction algorithm leads to inferior errors for all cases. With such predictor, enlarging the propagated contour by a uniform 2 mm margin leads to a minimum recall of 97% over the entire patient population. Considering patient-specific margins based on of the Gaussian distribution around the mean error, the margins vary from 0.8 to 3.0 mm in anterior–posterior direction and from 1.2 to 3.2 mm in the superior–inferior direction. This study concludes that the advantage of such prediction algorithm highly depends on the tumor motion characteristics. For both uniform and patient-specific margins evaluation, smaller treatment margins could be used when combined to an accurate tracking and motion prediction algorithm. These results could offer guidance for future MR-guided lung tumor treatments.

AdaBoost Artificial Neural Network for Stock Market Predicting

In this work, we propose a new direction of stock index movement prediction algorithm, coined the Ada-ANN forecasting model, which exploits AdaBoost theory and ANN to fulfill the predicting task. ANNs are employed as the weak forecasting machines to construct one strong forecaster. Technical indicators from Chinese stock market and international stock markets such as S&P 500, NSADAQ, and DJIA are selected as the predicting independent variables for the period under investigation. Numerical results are compared and analyzed between strong forecasting machine and the weak one. Experimental results show that the Ada-ANN model works better than its rival for predicting direction of stock index movement.

A Particle Filter–Based Motion Prediction Algorithm for Lung Tumors Using Dynamic Magnetic Resonance Imaging

A Particle Filter–Based Motion Prediction Algorithm for Lung Tumors Using Dynamic Magnetic Resonance Imaging

SU‐C‐BRA‐02: A Lung Tumor Autocontouring Algorithm Based On Particle Filter for Dynamic Magnetic Resonance

Purpose:This study proposes a novel autocontouring algorithm for lung tumors based on particle filter. It is developed in the context of MR‐linac treatments and is validated on in‐vivo dynamic magnetic resonance images.Methods:A sequential Monte Carlo method called particle filter is combined with Otsu's thresholding technique to contour lung tumors on dynamic MR images. Four non‐small cell lung cancer (NSCLC) patients were imaged with a 1.5 T MR (MAGNETOM Aera, Siemens Healthcare, Erlangen, Germany) for one minute at a rate of 4 images per second. A pretreatment image‐processing step includes a manual contour of the tumor, the tumor's displacement, and its descriptive statistics. During treatment, the contours are automatically generated by thresholding the image in a window surrounding the center of mass of the particles. The Dice similarity coefficient (DSC), the precision, the recall, the Hausdorff distance (HD) and the difference in centroid positions (Δd) are calculated to compare with the expert's contours.Results:This autocontouring algorithm presents continuous adaptability and is independent of pre‐treatment training. The number of particles is proportional to the area of the tumor and increases the computational time at a rate of 2 ms for every 500 particles, whereas the contouring step adds a constant 14 ms. The contours’ comparison is obtained with a mean DSC of 0.89 to 0.91, mean precision of 0.88 to 0.92, mean recall of 0.88 to 0.95 and mean Δd of 0.6 to 2.3 mm.Conclusion:This work presents a new autocontouring algorithm for NSCLC patients on dynamic MR images. Combining the autocontours with a motion prediction algorithm presents a promising method for lung tumor online tracking treatments.

Ensemble framework based real-time respiratory motion prediction for adaptive radiotherapy applications

Successful treatment of tumors with motion-adaptive radiotherapy requires accurate prediction of respiratory motion, ideally with a prediction horizon larger than the latency in radiotherapy system. Accurate prediction of respiratory motion is however a non-trivial task due to the presence of irregularities and intra-trace variabilities, such as baseline drift and temporal changes in fundamental frequency pattern. In this paper, to enhance the accuracy of the respiratory motion prediction, we propose a stacked regression ensemble framework that integrates heterogeneous respiratory motion prediction algorithms. We further address two crucial issues for developing a successful ensemble framework: (1) selection of appropriate prediction methods to ensemble (level-0 methods) among the best existing prediction methods; and (2) finding a suitable generalization approach that can successfully exploit the relative advantages of the chosen level-0 methods. The efficacy of the developed ensemble framework is assessed with real respiratory motion traces acquired from 31 patients undergoing treatment. Results show that the developed ensemble framework improves the prediction performance significantly compared to the best existing methods.

TH-AB-303-03: Real-Time Error Estimation for Real-Time Motion Prediction

Purpose: A critical component of real-time MLC tracking is to incorporate motion prediction in order to account for system latency, the time between the occurrence of motion and MLC leaf repositioning, so as to avoid geometric and dosimetric errors. In this work, we address a major limitation of current motion prediction algorithms – estimating the inherent error in prediction in real-time. We propose using an ensemble of real-time predictors to reduce overall prediction error, and provide a quantitative metric to trigger beam holds based on a user-defined error threshold. Methods: An ensemble of eighteen kernel density estimation (KDE)-based estimators was implemented in parallel and deployed on motion traces recorded from ten lung cancer patients. For each trace, the ensemble generated a sequence of predictions for a look-ahead time of 250ms, typical for many real-time systems. Each predictor, and the ensemble as a whole, was evaluated based on their root-mean-square error (RMSE) with respect to the original data, and assigned a beam hold score between 0 (perfect) and 1 to quantify the number of false beam holds and false beam releases. Results: The average execution time of the KDE-ensemble was 16ms, well below the 250ms look-ahead. For all traces, the ensemble produced a lower RMSE and hold score than any of the individual constituent predictors. For the worst-case motion trace, the minimum RMSE(hold score) for individual predictors was 0.912cm(0.340) compared to 0.854cm(0.305) for the KDE-ensemble predictor. The hold score for individual estimators was ∼0.6 when error estimates were not used to assert beam holds. Conclusion: The KDE-ensemble approach results in improved motion prediction. More importantly, it also provides real-time estimations of prediction error, thus addressing a major gap in current motion prediction algorithms. Such error prediction can be utilized to assert beam-holds during MLC tracking, preventing serious geometric and dosimetric errors. Funding for this work was provided by Elekta Limited.

A simulation based method for vehicle motion prediction

The movement of a vehicle is much affected by surrounding environments such as road shapes and other traffic participants. This paper proposes a new vehicle motion prediction method to predict future motion of an on-road vehicle which is observed by a stereo camera system mounted on a moving vehicle. Our proposed algorithm considers not only the history movement of the observed vehicle, but also the environment configuration around the vehicle. To find feasible paths under a dynamic road environment, the Rapidly-Exploring Random Tree (RRT) is used. A simulation based method is then applied to generate future trajectories by combining results from RRT and a motion prediction algorithm modelled as a Gaussian Mixture Model (GMM). Our experiments show that our approach can predict future motion of a vehicle accurately, and outperforms previous works where only motion history is considered for motion prediction.

Using an external surrogate for predictor model training in real-time motion management of lung tumors.

Precise prediction of respiratory motion is a prerequisite for real-time motion compensation techniques such as beam, dynamic couch, or dynamic multileaf collimator tracking. Collection of tumor motion data to train the prediction model is required for most algorithms. To avoid exposure of patients to additional dose from imaging during this procedure, the feasibility of training a linear respiratory motion prediction model with an external surrogate signal is investigated and its performance benchmarked against training the model with tumor positions directly. The authors implement a lung tumor motion prediction algorithm based on linear ridge regression that is suitable to overcome system latencies up to about 300 ms. Its performance is investigated on a data set of 91 patient breathing trajectories recorded from fiducial marker tracking during radiotherapy delivery to the lung of ten patients. The expected 3D geometric error is quantified as a function of predictor lookahead time, signal sampling frequency and history vector length. Additionally, adaptive model retraining is evaluated, i.e., repeatedly updating the prediction model after initial training. Training length for this is gradually increased with incoming (internal) data availability. To assess practical feasibility model calculation times as well as various minimum data lengths for retraining are evaluated. Relative performance of model training with external surrogate motion data versus tumor motion data is evaluated. However, an internal-external motion correlation model is not utilized, i.e., prediction is solely driven by internal motion in both cases. Similar prediction performance was achieved for training the model with external surrogate data versus internal (tumor motion) data. Adaptive model retraining can substantially boost performance in the case of external surrogate training while it has little impact for training with internal motion data. A minimum adaptive retraining data length of 8 s and history vector length of 3 s achieve maximal performance. Sampling frequency appears to have little impact on performance confirming previously published work. By using the linear predictor, a relative geometric 3D error reduction of about 50% was achieved (using adaptive retraining, a history vector length of 3 s and with results averaged over all investigated lookahead times and signal sampling frequencies). The absolute mean error could be reduced from (2.0 ± 1.6) mm when using no prediction at all to (0.9 ± 0.8) mm and (1.0 ± 0.9) mm when using the predictor trained with internal tumor motion training data and external surrogate motion training data, respectively (for a typical lookahead time of 250 ms and sampling frequency of 15 Hz). A linear prediction model can reduce latency induced tracking errors by an average of about 50% in real-time image guided radiotherapy systems with system latencies of up to 300 ms. Training a linear model for lung tumor motion prediction with an external surrogate signal alone is feasible and results in similar performance as training with (internal) tumor motion. Particularly for scenarios where motion data are extracted from fluoroscopic imaging with ionizing radiation, this may alleviate the need for additional imaging dose during the collection of model training data.

TU‐F‐17A‐07: Real‐Time Personalized Margins

Purpose:To maximize normal tissue sparing for treatments requiring motion encompassing margins. Motion mitigation techniques including DMLC or couch tracking can freeze tumor motion within the treatment aperture potentially allowing for smaller treatment margins and thus better sparing of normal tissue. To enable for a safe application of this concept in the clinic we propose adapting margins dynamically in real‐time during radiotherapy delivery based on personalized tumor localization confidence. To demonstrate technical feasibility we present a phantom study.Methods:We utilize a realistic anthropomorphic dynamic thorax phantom with a lung tumor model embedded close to the spine. The tumor, a 3D‐printout of a patient's GTV, is moved 15mm peak‐to‐peak by diaphragm compression and monitored by continuous EPID imaging in real‐time. Two treatment apertures are created for each beam, one representing ITV ‐based and the other GTV‐based margin expansion. A soft tissue localization (STiL) algorithm utilizing the continuous EPID images is employed to freeze tumor motion within the treatment aperture by means of DMLC tracking. Depending on a tracking confidence measure (TCM), the treatment aperture is adjusted between the ITV and the GTV leaf.Results:We successfully demonstrate real‐time personalized margin adjustment in a phantom study. We measured a system latency of about 250 ms which we compensated by utilizing a respiratory motion prediction algorithm (ridge regression). With prediction in place we observe tracking accuracies better than 1mm. For TCM=0 (as during startup) an ITV‐based treatment aperture is chosen, for TCM=1 a GTV‐based aperture and for 0<TCM<1 a linear interpolation between the two apertures is chosen.Conclusions:We have proposed and demonstrated a concept to safely shrink treatment margins during radiotherapy and adapt to tracking confidence in real‐time. Normal tissue sparing is maximized. The worst case scenario results in delivering a plan with standard margins used in the clinic today.

Automated Detection and Tracking of Adult Pacific Lampreys in Underwater Video Collected at Snake and Columbia River Fishways

AbstractTo reduce staffing demands and enhance observational accuracy, we designed, implemented, and tested a computerized system for processing underwater video clips captured by static cameras and removing “quiet” frames in which no activity is detected. When the system detects activity, it tracks and counts the moving object. The implementation uses an adaptive background‐subtraction algorithm for detection and motion prediction for tracking. The system can be used to reduce the amount of video that must be reviewed by personnel, and it can produce total fish passage counts through the monitored area. The automated system reduced the total number of video hours requiring review by an average of 87.5% for count window videos and 83.5% for videos of picketed leads and lamprey passage orifices. The software detected 98.6% of the 144 Pacific Lampreys Entosphenus tridentatus that were observed in 185 h of raw video. Because the system is fully automated, monitoring requirements are negligible, and the cost reduction for fish monitoring is proportional to the number of quiet frames removed.Received March 27, 2013; accepted September 23, 2013Published online January 30, 2014

A Quasi-Spherical Triangle-Based Approach for Efficient 3-D Soft-Tissue Motion Tracking

Recently, model-based visual-tracking techniques have been developed for measuring physiological motion in robot-assisted minimally invasive surgery. However, the tracking of living tissue surfaces in 3-D space is very challenging. Linear models are difficult to fit complex tissue dynamics, while current nonlinear models generally suffer from complex implementation and excessive computational burden. Instrument occlusion is another challenging issue which often causes tracking failure. In this study, we propose a novel deformable model suitable for real-time 3-D tissue tracking based on a quasi-spherical triangle. The model is parameterized by three vertices of the triangle with a curving parameter so that the warped surface can be computed efficiently using matrix operations. An efficient second-order minimization technique is employed to estimate model parameters, and the Jacobian matrix associated with the proposed model is derived. To alleviate the effects of illumination, a triangle-based illumination model is incorporated into the tracking scheme. A new motion prediction algorithm is developed by exploring the peak-valley characteristics of motion signals to handle the occlusion problem. The performance of the proposed method is validated using phantom heart data and in vivo videos acquired by the daVinci surgical robotic platform.

Heart Motion Prediction in Robotic-Assisted Beating Heart Surgery: A Nonlinear Fast Adaptive Approach

Off-pump Coronary Artery Bypass Graft (CABG) surgery outperforms traditional on-pump surgery because the assisted robotic tools can alleviate the relative motion between the beating heart and robotic tools. Therefore, it is possible for the surgeon to operate on the beating heart and thus lessens post surgery complications for the patients. Due to the highly irregular and non-stationary nature of heart motion, it is critical that the beating heart motion is predicted in the model-based track control procedures. It is technically preferable to model heart motion in a nonlinear way because the characteristic analysis of 3D heart motion data through Bi-spectral analysis and Fourier methods demonstrates the involved nonlinearity of heart motion. We propose an adaptive nonlinear heart motion model based on the Volterra Series in this paper. We also design a fast lattice structure to achieve computational-efficiency for real-time online predictions. We argue that the quadratic term of the Volterra Series can improve the prediction accuracy by covering sharp change points and including the motion with sufficient detail. The experiment results indicate that the adaptive nonlinear heart motion prediction algorithm outperforms the autoregressive (AR) and the time-varying Fourier-series models in terms of the root mean square of the prediction error and the prediction error in extreme cases.

Efficient object motion prediction using fuzzy Petri net based modelling in a robot navigational environment

Predicting the next instance position of a moving object in a dynamic navigational environment is a critical issue as it involves uncertainty. This paper proposes a fuzzy rule-based motion prediction algorithm for predicting the next instance position of a moving object. The algorithm is robust in handling the uncertain data of real-life situation. The fuzzy rule base modeling is done using Fuzzy Petri Net (FPN) formalism. The prediction algorithm is tested for real-life bench-marked data sets and compared with existing motion prediction techniques. The performance of the algorithm is comparable to the existing prediction methods.

Development of Real-Time Couch-Based Tracking System (CBTS) For Image Guided Radiotherapy: A Feasibility Study

Intra-fraction motions due to respiration in the thoracic and abdominal regions have been observed. Respiration-induced tumor motion can result in radiation-induced toxicity in the healthy tissue. The aim of this study is to evaluate the mechanical accuracy of couch-based tracking system (CBTS) according to the respiration data with measurement the accuracy and delay time of CBTS for clinical application. Position data of the 3 axes using AlignRT (Vision RT, UK) were obtained. The surface position data of the patient is transferred to the serial port in MCU through the RS-232C communication system. MCU generated an optimized control signal and produced the motor control signals in real time using a PID algorithm. And CBTS tracks the surface position data of three directions using three motors. Current couch position information according to the 3 axes was fed back to tracking program using the linear encoder and linear strip. The regular respiratory motion data is a sine wave representing artificial respiration with a constant cycle. The respiration data was recorded at a sampling period of 10 ms for 60 s. We evaluated the mechanical movement of CBTS according to the sine wave and volunteer data. The CBTS has a mechanical delay time between the respiration data and tracking data, and an inherent processing time that the respiration data is transferred to the serial port within micro controller unit (MCU). We can calculate the processing time using the data transfer rate of RS-232C. The overall delay time of CBTS was calculated as the sum of two delay time. The test was repeated at intervals of 60 seconds for 3 minutes. Then we calculated root mean square (rms) error, average error, and standard deviation error of accuracy in 3 directions (X, Y and Z directions) based on the phase difference between the respiration data and tracking data. The result of analyzing the acquisition-correction data shows that the root mean square error and standard deviation of measurement in 3 axes (X, Y, and Z directions) is 2.94 ± 1.67 mm, 2.99 ± 1.69 mm, 5.05 ± 3.02 mm, respectively. Overall delay time of CBTS is calculated about 2.34 × 10−4 sec as the sum of the inherent processing time and the average mechanical delay time of CBTS. This novel CBTS has the potential for clinical application for tumor tracking in radiation therapy. The effect of the delay time needs to be further evaluated from the view-point of dosimetric impact and plan degradation. Further studies are necessary to reduce the delay time, to investigate the novel tumor motion prediction algorithm for real-tracking radiotherapy.

Motion prediction and supervisory control of the macro–micro parallel manipulator system

SUMMARYThis paper deals with the motion prediction and control of the macro–micro parallel manipulator system for a 500-m-aperture spherical radio telescope (FAST). Firstly, based on principles of parallel mechanism, a decoupled tracking and prediction algorithm to predict the position and orientation of the movable macro parallel manipulator is presented in this paper. Then, taken as the upper layer supervisory controller in the joint space of the micro parallel manipulator, the adaptive interaction PID controller utilizing the adaptive interaction algorithm to adjust the parameters of a canonical PID controller is discussed. In addition, the digital servo filters with feedforward are employed in the linear actuators as the lower layer controllers. Experimental results of a one-tenth scale FAST field model validate the effectiveness of the supervisory controller and the motion prediction algorithm.

Fusión Borrosa de Estimadores para Aplicaciones de Control Basado en Imagen

Vision-based control is an important issue in robotics, and for this task, motion prediction algorithms are used to estimate the location of targets or visual features given by a camera. Some of the most extended filters are: Kalman filter; alpha-beta/gamma filters; AKF; SKF; etc. but only some of them are considered as real-time filters in the bibliography. In this paper, a real-time implementation of a new complex filter (called FMF) is presented, which can be used in applications with strong time requirements providing a better prediction. For this purpose, a FPGA device, parallelization and pipelining techniques are used to accomplish time requirements. The viability, robustness and feasibility of the proposed filter are validated in an experimental vision-based control application.