Robust Motion Tracking Research Articles

Co2iAR: Co-located audio-visual enabled mobile collaborative industrial AR wiring harness assembly

Existing augmented reality (AR) assembly mainly provides visual instructions for operators from a first-person perspective, and it is hard to share individual working intents for co-located workers on the shop floor, especially for large-scale product assembly task that requires multiple operators working together. To bridge this gap for practical deployments, this paper proposes Co2iAR, a co-located audio-visual enabled mobile collaborative AR assembly. Firstly, according to the stereo visual-inertial fusion strategy, robust and accurate self-contained motion tracking is achieved for the resource-constrained mobile AR platform, followed by a co-located alignment from multiple mobile AR clients on the shop floor. Then, a lightweight text-aware network for online wiring harness character recognition is proposed, as well as the audio-based confirming strategy, enabling natural audio-visual interaction among co-located workers within a shared immersive workplace, which can also monitor the current wiring assembly status and activate the step-by-step tutorials automatically. The novelty of this work is focused on the deployment of audio-visual aware interaction using the same device that is being used to deploy the co-located collaborative AR work instructions, establishing shared operating intents among multiple co-located workers. Finally, comprehensive experiments are carried out on the collaborative performance among multiple AR clients, and results illustrate that the proposed Co2iAR can alleviate the cognitive load and achieve superior performance for the co-located AR assembly tasks, providing a more human-centric collaborative assembly performance.

VR interactive input system based on INS and binocular vision fusion

In virtual reality interactive input systems, real-time and accurate spatial position and pose measurement is key to achieving a natural user interface. This study explores the application of inertial measurement units and binocular vision fusion technology in virtual reality interactive input systems, with the aim of improving the tracking accuracy of the system through optimized pose models and visual algorithms. A virtual reality measurement technology that integrates inertial measurement units and binocular vision is proposed by using an improved Kalman filtering algorithm to process inertial measurement units data, and combining it with the SURF algorithm optimized binocular vision system. Experimental results showed that fusion technology could reduce sensor noise and bias, improve the accuracy of pose estimation, and promote stable and robust motion tracking in virtual reality systems. In the angle range of -90 ° to 90 °, the average absolute error of the fusion system compared to the simple inertial measurement units pose calculation decreased from 3.371 ° to 1.369 ° In the experiment of measuring distance with a binocular vision system, the average absolute error value decreased to 1.532 mm, and the error range of the marker within the distance range of 500 mm to 650 mm was controlled within -1.3 mm to 1.2 mm. This study provides an effective solution for achieving high-precision virtual reality interactive input systems and is meaningful for the advancement of virtual reality technology.

Design of a Wearable Real-Time Hand Motion Tracking System Using an Array of Soft Polymer Acoustic Waveguides.

Robust hand motion tracking holds promise for improved human-machine interaction in diverse fields, including virtual reality, and automated sign language translation. However, current wearable hand motion tracking approaches are typically limited in detection performance, wearability, and durability. This article presents a hand motion tracking system using multiple soft polymer acoustic waveguides (SPAWs). The innovative use of SPAWs as strain sensors offers several advantages that address the limitations. SPAWs are easily manufactured by casting a soft polymer shaped as a soft acoustic waveguide and containing a commercially available small ceramic piezoelectric transducer. When used as strain sensors, SPAWs demonstrate high stretchability (up to 100%), high linearity (R2 > 0.996 in all quasi-static, dynamic, and durability tensile tests), negligible hysteresis (<0.7410% under strain of up to 100%), excellent repeatability, and outstanding durability (up to 100,000 cycles). SPAWs also show high accuracy for continuous finger angle estimation (average root-mean-square errors [RMSE] <2.00°) at various flexion-extension speeds. Finally, a hand-tracking system is designed based on a SPAW array. An example application is developed to demonstrate the performance of SPAWs in real-time hand motion tracking in a three-dimensional (3D) virtual environment. To our knowledge, the system detailed in this article is the first to use soft acoustic waveguides to capture human motion. This work is part of an ongoing effort to develop soft sensors using both time and frequency domains, with the goal of extracting decoupled signals from simple sensing structures. As such, it represents a novel and promising path toward soft, simple, and wearable multimodal sensors.

Siamese pyramidal deep learning network for strain estimation in 3D cardiac cine-MR.

Strain represents the quantification of regional tissue deformation within a given area. Myocardial strain has demonstrated considerable utility as an indicator for the assessment of cardiac function. Notably, it exhibits greater sensitivity in detecting subtle myocardial abnormalities compared to conventional cardiac function indices, like left ventricle ejection fraction (LVEF). Nonetheless, the estimation of strain poses considerable challenges due to the necessity for precise tracking of myocardial motion throughout the complete cardiac cycle. This study introduces a novel deep learning-based pipeline, designed to automatically and accurately estimate myocardial strain from three-dimensional (3D) cine-MR images. Consequently, our investigation presents a comprehensive pipeline for the precise quantification of local and global myocardial strain. This pipeline incorporates a supervised Convolutional Neural Network (CNN) for accurate segmentation of the cardiac muscle and an unsupervised CNN for robust left ventricle motion tracking, enabling the estimation of strain in both artificial phantoms and real cine-MR images. Our investigation involved a comprehensive comparison of our findings with those obtained from two commonly utilized commercial software in this field. This analysis encompassed the examination of both intra- and inter-user variability. The proposed pipeline exhibited demonstrable reliability and reduced divergence levels when compared to alternative systems. Additionally, our approach is entirely independent of previous user data, effectively eliminating any potential user bias that could influence the strain analyses.

Multi-Modal Sensor Fusion and Object Tracking for Autonomous Racing

Reliable detection and tracking of surrounding objects are indispensable for comprehensive motion prediction and planning of autonomous vehicles. Due to the limitations of individual sensors, the fusion of multiple sensor modalities is required to improve the overall detection capabilities. Additionally, robust motion tracking is essential for reducing the effect of sensor noise and improving state estimation accuracy. The reliability of the autonomous vehicle software becomes even more relevant in complex, adversarial high-speed scenarios at the vehicle handling limits in autonomous racing. In this paper, we present a modular multi-modal sensor fusion and tracking method for high-speed applications. The method is based on the Extended Kalman Filter (EKF) and is capable of fusing heterogeneous detection inputs to track surrounding objects consistently. A novel delay compensation approach enables to reduce the influence of the perception software latency and to output an updated object list. It is the first fusion and tracking method validated in high-speed real-world scenarios at the Indy Autonomous Challenge 2021 and the Autonomous Challenge at CES (AC@CES) 2022, proving its robustness and computational efficiency on embedded systems. It does not require any labeled data and achieves position tracking residuals below 0.1 m. The related code is available as open-source software at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/TUMFTM/FusionTracking</uri> .

Live-view 4D GRASP MRI: A framework for robust real-time respiratory motion tracking with a sub-second imaging latency.

To propose a framework called live-view golden-angle radial sparse parallel (GRASP) MRI for low-latency and high-fidelity real-time volumetric MRI. Live-view GRASP MRI has two stages. The first one is called an off-view stage and the second one is called a live-view stage. In the off-view stage, 3D k-space data and 2D navigators are acquired alternatively using a new navi-stack-of-stars sampling scheme. A 4D motion database is then generated that contains time-resolved MR images at a sub-second temporal resolution, and each image is linked to a 2D navigator. In the live-view stage, only 2D navigators are acquired. At each time point, a live-view 2D navigator is matched to all the off-view 2D navigators. A 3D image that is linked to the best-matched off-view 2D navigator is then selected for this time point. This framework places the typical acquisition and reconstruction burden of MRI in the off-view stage, enabling low-latency real-time 3D imaging in the live-view stage. The accuracy of live-view GRASP MRI and the robustness of 2D navigators for characterizing respiratory variations and/or body movements were assessed. Live-view GRASP MRI can efficiently generate real-time volumetric images that match well with the ground-truth references, with an imaging latency below 500 ms. Compared to 1D navigators, 2D navigators enable more reliable characterization of respiratory variations and/or body movements that may occur throughout the two imaging stages. Live-view GRASP MRI represents a novel, accurate, and robust framework for real-time volumetric imaging, which can potentially be applied for motion adaptive radiotherapy on MRI-Linac.

DisPad

The last few decades have witnessed an emerging trend of wearable soft sensors; however, there are important signal-processing challenges for soft sensors that still limit their practical deployment. They are error-prone when displaced, resulting in significant deviations from their ideal sensor output. In this work, we propose a novel prototype that integrates an elbow pad with a sparse network of soft sensors. Our prototype is fully bio-compatible, stretchable, and wearable. We develop a learning-based method to predict the elbow orientation angle and achieve an average tracking error of 9.82 degrees for single-user multi-motion experiments. With transfer learning, our method achieves the average tracking errors of 10.98 degrees and 11.81 degrees across different motion types and users, respectively. Our core contributions lie in a solution that realizes robust and stable human joint motion tracking across different device displacements.

Performance evaluation of online motion synchronizing systems for patient-specific respiratory movements with helical tomotherapy

Background and purpose. The introduction of online motion synchronizing system on helical tomotherapy paves way for robust motion tracking. A recent upgrade launches modifications on both hardware and software of the kV tracking system. An evaluation on the kV subsystems, prior (Version1) and post upgrade (Version2), was performed to compare tracking accuracy by means of fiducial tracking error and resulted root-mean-square (RMS). Impacts influenced by various patient-specific breathing pattern regularities and target movements were also investigated to refine motion tracking error estimations upon future selection of possible candidates. Materials and methods. Respiratory patterns from twenty-five lung cases were imported individually into a commercial dynamic platform model. Situating a phantom implanted with gold fiducial markers on the platform, superior-inferior (SI) movements of corresponding targets were simulated. Each case was delivered via an identical treatment plan in Version1 and was repeated in Version2. Motion tracking accuracy, by means of discrepancies between subsystem predicted model and raw data motion recorded in patient CT simulation, was analyzed statistically. Wilcoxon signed ranked test was employed to evaluate the difference in tracking error range between the two versions. Statistical model was fitted to inspect the dependence of internal target movement towards fiducial tracking errors. Results. A small difference of ±1 mm was exhibited in 99% of fiducial tracking errors for all cases experimented under both versions. RMS errors were all below 0.5 mm. Version2 demonstrated a greater extremity in fiducial tracking error (p = 0.04). A positive correlation was depicted between internal target amplitudes and 95% interval of fiducial tracking errors (p < 0.02). Overall, irregular respiratory patterns tended to have greater fiducial tracking errors. Conclusions. The excellent tracking performance in both kV subsystem versions offers motion compensations benefits, yet Version1 outperformed Version2 in fiducial tracking accuracy. It is noticeable that greater magnitude in internal target movement and irregular breathing patterns yield greater tracking error.

Deep learning based markerless motion tracking as a clinical tool for movement disorders: Utility, feasibility and early experience

Clinical assessments of movement disorders currently rely on the administration of rating scales, which, while clinimetrically validated and reliable, depend on clinicians’ subjective analyses, resulting in interrater differences. Intraoperative microelectrode recording for deep brain stimulation targeting similarly relies on clinicians’ subjective evaluations of movement-related neural activity. Digital motion tracking can improve the diagnosis, assessment, and treatment of movement disorders by generating objective, standardized measures of patients’ kinematics. Motion tracking with concurrent neural recording also enables motor neuroscience studies to elucidate the neurophysiology underlying movements. Despite these promises, motion tracking has seen limited adoption in clinical settings due to the drawbacks of conventional motion tracking systems and practical limitations associated with clinical settings. However, recent advances in deep learning based computer vision algorithms have made accurate, robust markerless motion tracking viable in any setting where digital video can be captured. Here, we review and discuss the potential clinical applications and technical limitations of deep learning based markerless motion tracking methods with a focus on DeepLabCut (DLC), an open-source software package that has been extensively applied in animal neuroscience research. We first provide a general overview of DLC, discuss its present usage, and describe the advantages that DLC confers over other motion tracking methods for clinical use. We then present our preliminary results from three ongoing studies that demonstrate the use of DLC for 1) movement disorder patient assessment and diagnosis, 2) intraoperative motor mapping for deep brain stimulation targeting and 3) intraoperative neural and kinematic recording for basic human motor neuroscience.

Neural Adaptive Robust Motion-Tracking Control for Robotic Manipulator Systems

This paper deals with the motion trajectory tracking control problem based on output feedback and artificial neural networks for anthropomorphic manipulator robots under disturbed operating scenarios. This class of manipulator robots constitutes nonlinear dynamic systems subjected to disturbance torques induced mainly by work payload. Parametric uncertainty and possible dynamic modeling errors stand for other kind of disturbances that can deteriorate the efficiency and robustness of the tracking of controlled nonlinear robotic system trajectories. In fact, the presence of unknown dynamic disturbances is unavoidable in industrial robotic engineering systems. Therefore, for high-precision applications, such as laser cutting, marking, or welding, effective control schemes should be designed to guarantee adequate motion profile tracking planned on this class of disturbed nonlinear robotic system. In this context, a new adaptive robust motion trajectory tracking control scheme based on output feedback and artificial neural networks of anthropomorphic manipulator robots is presented. Three-layer B-spline artificial neural networks and time-series modeling are properly exploited in the design of novel adaptive robust motion tracking controllers for robotic applications of laser manufacturing. In this way, dependency on detailed nonlinear mathematical modeling of robotic systems is considerably reduced, and real-time estimation of uncertain dynamic disturbances is not required. Furthermore, several cases studies to demonstrate the motion planning tracking control robustness for a class of MIMO nonlinear robotic systems are described. blue Insights for the extension of the introduced output-feedback adaptive neural control design approach for other architecture of nonlinear robotic systems are depicted.

Robust Output-Feedback Control for Spacecraft Proximity Operations With Forbidden Zone

This article seeks to address the robust output-feedback motion tracking control problem for spacecraft close-range proximity maneuvers under the influence of forbidden zone, external disturbances, and input saturation. First, two superquadric cuboids are constructed in the body-fixed frame of the target to represent the forbidden zone in final proximity. Then, an adaptive output-feedback control scheme is proposed under the framework of extended state observer technique and sliding mode control methodology. The presented scheme can achieve concurrent position and attitude tracking without prior information of the velocity-level states, and avoid any unwanted collisions between the two spacecraft. Finally, simulation scenarios are investigated to show the effectiveness of the proposed control scheme.

Long term and robust 6DoF motion tracking for highly dynamic stereo endoscopy videos.

Real-time augmented reality (AR) for minimally invasive surgery without extra tracking devices is a valuable yet challenging task, especially considering dynamic surgery environments. Multiple different motions between target organs are induced by respiration, cardiac motion or operative tools, and often must be characterized by a moving, manually positioned endoscope. Therefore, a 6DoF motion tracking method that takes advantage of the latest 2D target tracking methods and non-linear pose optimization and tracking loss retrieval in SLAM technologies is proposed and can be embedded into such an AR system. Specifically, the SiamMask deep learning-based target tracking method is incorporated to roughly exclude motion distractions and enable frame matching. This algorithm's light computation cost makes it possible for the proposed method to run in real-time. A global map and a set of keyframes as in ORB-SLAM are maintained for pose optimization and tracking loss retrieval. The stereo matching and frame matching methods are improved and a new strategy to select reference frames is introduced to make the first-time motion estimation of every arriving frame as accurate as possible. Experiments on both a clinical laparoscopic partial nephrectomy dataset and an ex-vivo porcine kidney dataset are conducted. The results show that the proposed method gives a more robust and accurate performance compared with ORB-SLAM2 in the presence of motion distractions or motion blur; however, heavy smoke still remains a big factor that reduces the tracking accuracy.

Adaptive Robust Motion Control of Quadrotor Systems Using Artificial Neural Networks and Particle Swarm Optimization

Most of the mechanical dynamic systems are subjected to parametric uncertainty, unmodeled dynamics, and undesired external vibrating disturbances while are motion controlled. In this regard, new adaptive and robust, advanced control theories have been developed to efficiently regulate the motion trajectories of these dynamic systems while dealing with several kinds of variable disturbances. In this work, a novel adaptive robust neural control design approach for efficient motion trajectory tracking control tasks for a considerably disturbed non-linear under-actuated quadrotor system is introduced. Self-adaptive disturbance signal modeling based on Taylor-series expansions to handle dynamic uncertainty is adopted. Dynamic compensators of planned motion tracking errors are then used for designing a baseline controller with adaptive capabilities provided by three layers B-spline artificial neural networks (Bs-ANN). In the presented adaptive robust control scheme, measurements of position signals are only required. Moreover, real-time accurate estimation of time-varying disturbances and time derivatives of error signals are unnecessary. Integral reconstructors of velocity error signals are properly integrated in the output error signal feedback control scheme. In addition, the appropriate combination of several mathematical tools, such as particle swarm optimization (PSO), Bézier polynomials, artificial neural networks, and Taylor-series expansions, are advantageously exploited in the proposed control design perspective. In this fashion, the present contribution introduces a new adaptive desired motion tracking control solution based on B-spline neural networks, along with dynamic tracking error compensators for quadrotor non-linear systems. Several numeric experiments were performed to assess and highlight the effectiveness of the adaptive robust motion tracking control for a quadrotor unmanned aerial vehicle while subjected to undesired vibrating disturbances. Experiments include important scenarios that commonly face the quadrotors as path and trajectory tracking, take-off and landing, variations of the quadrotor nominal mass and basic navigation. Obtained results evidence a satisfactory quadrotor motion control while acceptable attenuation levels of vibrating disturbances are exhibited.

Robust Inertial Motion Tracking through Deep Sensor Fusion across Smart Earbuds and Smartphone

IMU based inertial tracking plays an indispensable role in many mobility centric tasks, such as robotic control, indoor navigation and virtual reality gaming. Despite its mature application in rigid machine mobility (e.g., robot and aircraft), tracking human users via mobile devices remains a fundamental challenge due to the intractable gait/posture patterns. Recent data-driven models have tackled sensor drifting, one key issue that plagues inertial tracking. However, these systems still assume the devices are held or attached to the user body with a relatively fixed posture. In practice, natural body activities may rotate/translate the device which may be mistaken as whole body movement. Such motion artifacts remain as the dominating factor that fails existing inertial tracing systems in practical uncontrolled settings. Inspired by the observation that human heads induces far less intensive movement relative to the body during walking, compared to other parts, we propose a novel multi-stage sensor fusion pipeline called DeepIT, which realizes inertial tracking by synthesizing the IMU measurements from a smartphone and an associated earbud. DeepIT introduces a data-driven reliability aware attention model, which assesses the reliability of each IMU and opportunistically synthesizes their data to mitigate the impacts of motion noise. Furthermore, DeepIT uses a reliability aware magnetometer compensation scheme to combat the angular drifting problem caused by unrestricted motion artifacts. We validate DeepIT on the first large-scale inertial navigation dataset involving both smartphone and earbud IMUs. The evaluation results show that DeepIT achieves multiple folds of accuracy improvement on the challenging uncontrolled natural walking scenarios, compared with state-of-the-art closed-form and data-driven models.

Precise robust motion tracking of a piezoactuated micropuncture mechanism with sliding mode control

This paper proposes a novel model-based control scheme to achieve the precise robust motion control of a piezoactuated micropuncture mechanism for cell injection. Using the Bouc–Wen model, the hysteretic dynamic model of the micropuncture mechanism is constructed, and its local optimization is conducted to facilitate engineering applications. On the basis of this model, a controller that synthesizes a fast nonsingular terminal sliding mode (FNTSM) control and time-delay estimation (TDE) is constructed. The control law for FNTSM has the advantages of continuous output, absence of chatter, and finite-time convergence of tracking error. The unknown quantity for TDE technology can be estimated and compensated online to reduce the FNTSM gain. Experiments on the micropuncture mechanism demonstrate that the developed control scheme provides smaller tracking error than the delay-control strategy based on the linear-error dynamic model or the model-free control scheme (e.g., Jin and Hsia’s controller). Micropuncture experiments on zebrafish embryo are successfully completed. Moreover, from the practical aspects, the control scheme developed herein can be effectively implemented in other types of micro-operation mechanisms driven by piezoelectric actuators.

Robust Footstep Planning and LQR Control for Dynamic Quadrupedal Locomotion

In this paper, we aim to improve the robustness of dynamic quadrupedal locomotion through two aspects: 1) fast model predictive foothold planning, and 2) applying LQR to projected inverse dynamic control for robust motion tracking. In our proposed planning and control framework, foothold plans are updated at 400 Hz considering the current robot state and an LQR controller generates optimal feedback gains for motion tracking. The LQR optimal gain matrix with non-zero off-diagonal elements leverages the coupling of dynamics to compensate for system underactuation. Meanwhile, the projected inverse dynamic control complements the LQR to satisfy inequality constraints. In addition to these contributions, we show robustness of our control framework to unmodeled adaptive feet. Experiments on the quadruped ANYmal demonstrate the effectiveness of the proposed method for robust dynamic locomotion given external disturbances and environmental uncertainties.

Motion tracking of left myocardium in cardiac cine magnetic resonance image based on displacement flow U-Net and variational autoencoder

The high morbidity and mortality of cardiovascular diseases (CVDs) seriously affects the quality of human life. How to asses cardiac function, assist in the diagnosis and treatment of clinical CVDs and evaluate prognosis is a problem to be solved urgently. In response to this issue, based on previous work of Cardiac Cine Magnetic Resonance (CCMR) image segmentation of the left myocardium (LVM), a robust and accurate LVM motion tracking method (DispFlow_UNet_Flow) with using the displacement flow UNet (DispFlow_UNet) and biomechanics-informed variational autoencoder (VAE) is proposed in this paper. The following are the main research contents: (1) building a compressed excitation residual U-net network to accurately segment LVM, calculating the ventricular volume and myocardial mass according to the segmentation results, and then evaluating the overall cardiac function; (2) reconstructing the dense displacement field (DDF) based on the proposed motion tracking method, and obtaining the LVM dense displacement field by combining the LVM segmentation mask; (3) contrasting and evaluating the motion tracking results by using the true displacement vector field of simulated data and clinical data sets. All the results show that the tracking algorithm proposed in this paper has high precision and generalization capability.

A fast navigator (fastNAV) for prospective respiratory motion correction in first-pass myocardial perfusion imaging.

PurposeTo develop and evaluate a fast respiratory navigator (fastNAV) for cardiac MR perfusion imaging with subject‐specific prospective slice tracking.MethodsA fastNAV was developed for dynamic contrast‐enhanced cardiac MR perfusion imaging by combining spatially nonselective saturation with slice‐selective tip‐up and slice‐selective excitation pulses. The excitation slice was angulated from the tip‐up slice in the transverse plane to overlap only in the right hemidiaphragm for suppression of signal outside the right hemidiaphragm. A calibration scan was developed to enable the estimation of subject‐specific tracking factors. Perfusion imaging using subject‐specific fastNAV‐based slice tracking was then compared to a conventional sequence (ie, without slice tracking) in 10 patients under free‐breathing conditions. Respiratory motion in perfusion images was quantitatively assessed by measuring the average overlap of the left ventricle across images (avDice, 0:no overlap/1:perfect overlap) and the average displacement of the center of mass of the left ventricle (avCoM). Image quality was subjectively assessed using a 4‐point scoring system (1: poor, 4: excellent).ResultsThe fastNAV calibration was successfully performed in all subjects (average tracking factor of 0.46 ± 0.13, R = 0.94 ± 0.03). Prospective motion correction using fastNAV led to higher avDice (0.94 ± 0.02 vs. 0.90 ± 0.03, P < .001) and reduced avCoM (4.03 ± 0.84 vs. 5.22 ± 1.22, P < .001). There were no statistically significant differences between the 2 sequences in terms of image quality (both sequences: median = 3 and interquartile range = 3‐4, P = 1).ConclusionfastNAV enables fast and robust right hemidiaphragm motion tracking in a perfusion sequence. In combination with subject‐specific slice tracking, fastNAV reduces the effect of respiratory motion during free‐breathing cardiac MR perfusion imaging.

Robust adaptive motion tracking of piezoelectric actuated stages using online neural-network-based sliding mode control

Robust and precise motion tracking for micro-electro-mechanical systems in the presence of inherent nonlinearity and external disturbance is of great importance in many applications. Due to high sensitivity to environmental variations, the entire model or some parameters of the system tend to change unexpectedly. Existing offline nonlinearity models are computationally intensive and may be not suitable under system perturbations. In this work, for a class of piezoelectric actuated (PEA) system, a new online neural-network-based sliding mode control (OLNN-SMC) scheme is developed to obtain robust adaptive precision motions. The nonlinearity of the PEA system is identified online and compensated using singularity-free neural networks (NNs). To alleviate the residual NN approximation errors and meanwhile maintain robust stability under external disturbance, a feedback sliding-mode is synthesized into the control law. Considering unknown and varying disturbances, an adaptive mechanism is designed to achieve robust adaptive motion tracking. The controller is implemented and evaluated through experiments on a PEA platform. Results show that the proposed OLNN-SMC is superior to existing proportional-integral-derivative control with disturbance observer (PID+DOB) and adaptive sliding mode control (ASMC) in terms of sinusoidal tracking and disturbance rejection. In particular, the root-mean-square (RMS) errors for sinusoidal tracking at 0.1–10 Hz using the proposed OLNN-SMC are reduced by 83.5% compared with the cases using PID+DOB or ASMC.

A Robust and High-Precision Three-Dimensional Motion Tracking Algorithm for Gymnasts

A Robust and High-Precision Three-Dimensional Motion Tracking Algorithm for Gymnasts