Precise Motion Tracking Research Articles

Bio-Inspired Motion Emulation for Social Robots: A Real-Time Trajectory Generation and Control Approach.

Assistive robotic platforms have recently gained popularity in various healthcare applications, and their use has expanded to social settings such as education, tourism, and manufacturing. These social robots, often in the form of bio-inspired humanoid systems, provide significant psychological and physiological benefits through one-on-one interactions. To optimize the interaction between social robotic platforms and humans, it is crucial for these robots to identify and mimic human motions in real time. This research presents a motion prediction model developed using convolutional neural networks (CNNs) to efficiently determine the type of motions at the initial state. Once identified, the corresponding reactions of the robots are executed by moving their joints along specific trajectories derived through temporal alignment and stored in a pre-selected motion library. In this study, we developed a multi-axial robotic arm integrated with a motion identification model to interact with humans by emulating their movements. The robotic arm follows pre-selected trajectories for corresponding interactions, which are generated based on identified human motions. To address the nonlinearities and cross-coupled dynamics of the robotic system, we applied a control strategy for precise motion tracking. This integrated system ensures that the robotic arm can achieve adequate controlled outcomes, thus validating the feasibility of such an interactive robotic system in providing effective bio-inspired motion emulation.

The Sliding Mode Control for Piezoelectric Tip/Tilt Platform on Precision Motion Tracking

This paper presents the design of a sliding mode controller to compensate hysteresis nonlinearity and achieve precision motion tracking for a novel piezoelectric tip/tilt platform driven by a PZT actuator. The sliding mode control scheme is based on the unique physical characteristics of the piezoelectric tip/tilt platform. The proposed scheme effectively guides the platform state onto a predefined sliding surface and ensures its sustained movement along this manifold. This approach reduces tracking errors compared to conventional methodologies. The stability of the sliding mode control scheme is demonstrated by the Lyapunov theory framework. It achieves precise motion control with minimal tracking error on a piezoelectric tip/tilt platform. The properties of the controller have been confirmed through experimental tests. The proposed control scheme enhances the robust tracking and stability performance on the piezoelectric tip/tilt platform, outperforming traditional control schemes. Compared with the P562.6CD produced by PI Germany, the proposed innovative approach not only boosts the platform’s resolution by 32% but also implements a 33% reduction in cost.

Precise 6-DOF Motion Tracking of Fine Motor Skills of Fingers Based on Wearable Magnetic Induction Sensors

Precise 6-DOF Motion Tracking of Fine Motor Skills of Fingers Based on Wearable Magnetic Induction Sensors

How Positioning Wearable Haptic Interfaces on Limbs Influences Virtual Embodiment.

With increasing use of computer applications and robotic devices in our everyday life, and with the advent of metaverse, there is an urgent need of developing new types of interfaces that facilitate a more intuitive interaction in physical and virtual space. In this work, we investigate the influence of the location of haptic feedback devices on embodiment of virtual hands and user load during an interactive pick-and-place task. To do this, we conducted a user study with a 3x2 repeated measure experiment design: feedback position is varied between the distal phalanx of the index finger and the thumb, the proximal phalanx of the index finger and the thumb, and the wrist. These conditions of feedback are tested with the stimuli applied synchronously to the participant in one case, and with an additional delay of 350 ms in the second case. The results show that the location of the haptic feedback device does not affect embodiment, whereas the delay, i.e., whether the feedback is applied synchronously or asynchronously, affects embodiment. This suggests that for pick-and-place tasks, haptic feedback devices can be placed on the user's wrist without compromising performance making the hands to remain free, allowing unobstructed hand visibility for precise motion tracking, thereby improving accuracy.

Adaptive zone based active noise control for a moving target

This study focuses on enhancing active noise control (ANC) in room enclosures, specifically targeting zones of quiet (ZoQ) around aircraft passenger seats. By integrating virtual sensing and motion tracking techniques, we aim to dynamically adapt the ZoQ to moving targets. Key to our approach is the strategic placement of actuators and sensors, forming the core of the ANC system. Our methodology includes virtual sensing for ANC analysis, ZoQ optimization for varied applications, and in-depth case studies. We introduce an innovative combination of a speaker gimbal system, a vision system, and custom software for precise motion tracking, significantly improving ZoQ localization. The findings offer insights into maintaining effective ZoQs for multiple input multiple output (MIMO) local ANC configurations, laying the groundwork for adaptive ZoQ control about sound sources and desired cancellation locations. This research marks a significant step towards more effective and adaptable noise cancellation in enclosed spaces.

4D-MRI Guided Stereotactic Body Radiation Therapy for Unresectable Colorectal Liver Metastases

This study evaluated the feasibilities and outcomes following four-dimensional magnetic resonance imaging (4D-MRI) guided stereotactic body radiation therapy (SBRT) for unresectable colorectal liver metastases (CRLM). From March 2018 to January 2022, we identified 76 unresectable CRLM patients with 123 lesions who received 4D-MRI guided SBRT in our institution. 4D-MRI simulation with or without abdominal compression was conducted for all patients. The prescription dose was 50-65 Gy in 5-12 fractions. The image quality of computed tomography (CT) and MRI were compared using the Clarity Score. Clinical outcomes and toxicity profiles were evaluated. The 4D-MRI significantly improved the image quality compared with CT images (mean Clarity Score: 1.67 vs 2.88, P < 0.001). The abdominal compression significantly reduced motions in cranial-caudal direction (P = 0.03) with 2 phase T2 weighted images assessing tumor motion. The median follow-up time was 12.5 months. For 98 lesions assessed for best response, the complete response, partial response and stable disease rate were 57.1 %, 30.6 % and 12.2 %, respectively. The local control (LC) rate at 2 year was 97.3%. 46.1% of patients experienced grade 1-2 toxicities and only 2.6% patients experienced grade 3 hematologic toxicities. The 4D-MRI technique allowed precise target delineation and motion tracking in unresectable CRLM patients. High LC rate and mild toxicities were achieved. This study provided evidence for using 4D-MRI guided SBRT as an alternative treatment in unresectable CRLM.

Precise motion tracking of piezo-actuated stages via a neural network-based data-driven adaptive predictive controller

Precise motion tracking of piezo-actuated stages via a neural network-based data-driven adaptive predictive controller

Leveraging the Properties of mmWave Signals for 3D Finger Motion Tracking for Interactive IoT Applications

Wireless signals, which are mainly used for communication networks, also have the potential to extend our senses, enabling us to see behind closed doors and track moving objects through walls [3, 10]. Accordingly, there is a growing interest in the community recently to develop novel IoT applications for sensing by exploiting radio frequency signals [7]. Given the compact size of modern wireless devices, this enables ubiquitous applications in the areas of smart healthcare, sports analytics, AR/VR etc. Specifically, as these signals travel in the medium, they traverse occlusions and bounce off different objects before arriving at a receiver; hence, the reflected signals carry information about the environment. By exploiting this property, this paper shows the feasibility of tracking precise 3D finger motion using mmWave signals that are popularly used in 5G networks. Motivation and Application: This paper presents mm4Arm, a system that quantifies the performance of finger motion tracking for interactive applications using mmWave signals through a carefully designed simulation and measurement study. We considered using mmWave signal because FMCW-based radars are being used for ubiquitous applications in the areas of smart healthcare [4], sports analytics, AR/VR [17], autonomous driving [5], etc. Similar to the popular Google Soli platform [15], our main motivation is to enable wearable, mobile computing, and AR/VR applications where conventional touch interaction may be hard. Finger motion-based interfaces over the air are known to be a popular form of human-computer interaction [9, 14]. In contrast to Soli, which can only detect 11 predefined gestures, mm4Arm can perform arbitrary 3D motion tracking, thus allowing highly precise control. Decades of prior research have shown that such a finer control can enable rapid and fluid manipulation for highly intuitive interaction [16]. Therefore, regardless of the application, we focus on enabling the core motion tracking framework by solving the underlying challenges. Tracking Fingers by Observing the ForeArm: In this paper, we not only focus on tracking the 3D finger motion using mmWave reflections, but based on observations via simulations and measurements, we also identify the underlying conditions that enable precise tracking. A critical observation is that the small size of fingers does not provide stable reflections to the level required for tracking. However, the data-driven analysis reveals that it is possible to indirectly track fingers by measuring reflections from the forearm. Finger motion activation involves neuro-muscular interactions, which induce minute muscular motions in the forearm. Such muscular motion produces vibrations in the forearm. Thanks to the short wavelength of mmWave signals, the phase measurements are extremely sensitive to small vibrations (up to 0.63 μm), thus opening up opportunities for precise motion tracking. Moreover, the forearm offers a rich texture and curvature and a much bigger surface for reflections, in contrast to the small size of fingers, which facilitates robust tracking. mm4Arm analyzes such forearm vibrations for 3D finger motion tracking. We reiterate two critical observations made in this paper: (i) When 3D finger motion tracking is of interest in contrast to predefined gesture classification, the reflections obtained directly from fingers do not provide sufficient information. Very few reflections come back to the radar due to the small size of fingers and dominant specular reflections. A similar observation on specularity has been made earlier in the context of autonomous cars [6, 13]. (ii) Vibrations in the forearm during finger motion can capture rich information. Because of the large surface of the forearm and its curvature, the reflections are more stable and robust to natural variation in arm position, height, and orientation. This can be leveraged for 3D finger motion tracking.

Human mobile robot interaction in the retail environment

As technology advances, Human-Robot Interaction (HRI) is boosting overall system efficiency and productivity. However, allowing robots to be present closely with humans will inevitably put higher demands on precise human motion tracking and prediction. Datasets that contain both humans and robots operating in the shared space are receiving growing attention as they may facilitate a variety of robotics and human-systems research. Datasets that track HRI with rich information other than video images during daily activities are rarely seen. In this paper, we introduce a novel dataset that focuses on social navigation between humans and robots in a future-oriented Wholesale and Retail Trade (WRT) environment (https://uf-retail-cobot-dataset.github.io/). Eight participants performed the tasks that are commonly undertaken by consumers and retail workers. More than 260 minutes of data were collected, including robot and human trajectories, human full-body motion capture, eye gaze directions, and other contextual information. Comprehensive descriptions of each category of data stream, as well as potential use cases are included. Furthermore, analysis with multiple data sources and future directions are discussed.

Real-time correction of image rotation with adaptive optics scanning light ophthalmoscopy.

Fixational eye motion includes typical translation and torsion. In the registration of images from adaptive optics scanning light ophthalmoscopy (AOSLO), image rotation due to eye torsion and/or head rotation is often ignored because (a) the amount of rotation is trivial compared to translation within a short duration of imaging or recording time and (b) computational cost increases substantially when the registration algorithm involves simultaneous detection of rotation and translation. However, it becomes critically important under cases such as long exposure, functional measurements, and precise motion tracking. We developed a fast method to detect and correct rotation from AOSLO images, together with the detection of strip-level motion translation. The computational cost for rotation detection and correction alone is about 5 ms/frame (512×512 pixels) on an nVidia GTX960M GPU. Image quality is compared with and without rotation correction from 10 healthy human subjects and 8 diseased eyes with a total of 180 videos. The results show that residual image motions between the reference images and the registered images with rotation correction are a fraction of those without rotation correction, and the ratio is 0.74-0.89 at the image center and 0.37-0.51 at the four corners of the images.

Design and Development of a New Rotating Electromagnetic Field Generation System for Driving Microrobots

This article presents the design, optimization, and testing of a new rotating electromagnetic field generation system for actuating magnetic microrobots. Its unique feature lies in that the proposed six-coil electromagnetic actuation system produces a uniform rotating magnetic field with a large 3-D workspace, which is enabled by conducting an architectural optimization of the system in consideration of both electric issues and geometry constraints. To maintain a low working temperature, each coil is fabricated with silicone grease for filling the gap among the copper wires and integrated with a water cooling fan for reducing the effect of coil heating. An analytical model is established to determine the dominant structural parameters, which are optimally tuned by resorting to genetic algorithm (GA) to achieve the best actuation performance. A prototype magnetic field generation system is developed for experimental testing. Experimental studies are carried out by driving various magnetic microrobots, including helical microswimmers and microparticles. Results indicate that the developed system can effectively actuate the microrobots with precision motion tracking, validating the effectiveness of the developed rotating magnetic field generation system.

Adaptive sliding mode control with hysteresis compensation-based neuroevolution for motion tracking of piezoelectric actuator

In this paper, an adaptive sliding mode control with hysteresis compensation-based neuroevolution (ACNE) is proposed for precise motion tracking of the piezoelectric actuator (PEA) in the presence of uncertainties, disturbances, and nonlinearity hysteresis characteristics. Firstly, a new memetic differential evolution (MeDE) algorithm is proposed to optimize the weights of a 3-layer neural network (called neuroevolution or NE). In MeDE, a differential evolution algorithm is used as a global search scheme and the Jaya algorithm is used as local search exploitation. Secondly, an inverse hysteresis model of PEA is identified by the neuroevolution model to provide a feed-forward NE control signal to compensate for the hysteresis behavior of PEA system. Thirdly, an adaptive neural sliding mode control plus feedforward NE (ACNE) control is designed to enhance the quality control and guarantee asymptotical stability for PEA system. Based on the Lyapunov method, the stability of the closed-loop system is analyzed and proved. Finally, the experimental Thorlabs piezoelectric actuator (PEA) is set up to verify the robustness and effectiveness of the proposed approach. Results show that the identified MeDE-Neuroevolution model has successfully applied to model the inverse hysteretic of PEA system and the performance of MeDE has better than Jaya, DE, and PSO in terms of best, worst, average, and standard deviation. Furthermore, in motion tracking control, the performance of proposed ACNE control has more accurate than a classical PID control, a feedforward control, a hybrid feedback–feedforward control, and an adaptive neural sliding mode control without a compensator.

Robust Hybrid Control Using Recurrent Wavelet- Neural-Network Sliding-Mode Controller for Two- Axis Motion Control System

In this paper, a robust hybrid control system (RHCS) for achieving high precision motion tracking performance of a two-axis motion control system is proposed. The proposed AHCS incorporating a recurrent wavelet-neuralnetwork controller (RWNNC) and a sliding-mode controller (SMC) to construct a RRWNNSMC. The two-axis motion control system is an x-y table of a computer numerical control machine that is driven by two field-oriented controlled permanent-magnet synchronous motors (PMSMs) servo drives. The RWNNC is used as the main motion tracking controller to mimic a perfect computed torque control law and the SMC controller is designed with adaptive bound estimation algorithm to compensate for the approximation error between the RWNNC and the ideal controller. The on-line learning algorithms of the connective weights, translations and dilations of the RWNNC are derived using Lyapunov stability analysis. A computer simulation and an experimental are developed to validate the effectiveness of the proposed RHCS. All control algorithms are implemented in a TMS320C31 DSP-based control computer. The simulation and experimental results using star and four leaves contours are provided to show the effectiveness of the RHCS. The motion tracking performance is significantly improved using the proposed RHCS and robustness to parameter variations, external disturbances, cross-coupled interference and frictional torque can be obtained as well for the two-axis motion control system.

Synchronization Control of a Dual-Cylinder Lifting Gantry of Segment Erector in Shield Tunneling Machine under Unbalance Loads

Segment assembling is one of the principle processes during tunnel construction using shield tunneling machines. The segment erector is a robotic manipulator powered by a hydraulic system to assemble prefabricated concrete segments onto the excavated tunnel surface. Nowadays, automation of the segment erector has become one of the definite developing trends to further improve the efficiency and safety during construction; thus, closed-loop motion control is an essential technology. Within the segment erector, the lifting gantry is driven by dual cylinders to lift heavy segments in the radial direction. Different from the dual-cylinder mechanism used in other machines such as forklifts, the lifting gantry usually works at an inclined angle, leading to unbalanced loads on the two sides. Although strong guide rails are applied to ensure synchronization, the gantry still occasionally suffers from chattering, “pull-and-drag”, or even being stuck in practice. Therefore, precise motion tracking control as well as high-level synchronization of the dual cylinders have become essential for the lifting gantry. In this study, a complete dynamics model of the dual-cylinder lifting gantry is constructed, considering the linear motion as well as the additional rotational motion of the crossbeam, which reveals the essence of poor synchronization. Then, a two-level synchronization control scheme is synthesized. The thrust allocation is designed to coordinate the dual cylinders and keep the rotational angle of the crossbeam within a small range. The motion tracking controller is designed based on the adaptive robust control theory to guarantee the linear motion tracking precision. The theoretical performance is analyzed with corresponding proof. Finally, comparative simulations are conducted and the results show that the proposed scheme achieves high-precision motion tracking performance and simultaneous high-level synchronization of dual cylinders under unbalanced loads.

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.

PID-type sliding mode-based adaptive motion control of a 2-DOF piezoelectric ultrasonic motor driven stage

This paper presents a new scheme of adaptive sliding mode control (ASMC) for a piezoelectric ultrasonic motor driven X–Y stage to meet the demand of precision motion tracking while addressing the problems of unknown nonlinear friction and model uncertainties. The system model with Coulomb friction and unilateral coupling effect is first investigated. Then the controller is designed with adaptive laws synthesized to obtain the unknown model parameters for handling parametric uncertainties and offsetting friction force. The robust control term acts as a high gain feedback control to make the output track the desired trajectory fast for guaranteed robust performance. Based on a PID-type sliding mode, the control scheme has a simple structure to be implemented and the control parameters can be easily tuned. Theoretical stability analysis of the proposed novel ASMC is accomplished using a Lyapunov framework. Furthermore, the proposed control scheme is applied to an X–Y stage and the results prove that the proposed control method is effective in achieving excellent tracking performance.

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.

Neural networks-based model predictive control for precision motion tracking of a micropositioning system

Micropositioning systems are widely employed in industrial applications. Nonminimum-phase (NMP) is a normal phenomenon in micropositioning system, which leads to a great challenge for control system design. Model predictive control (MPC) is effective in handling the NMP problem. However, the parameter tuning of MPC is quite complicated and time-consuming using traditional methods for motion tracking control implementation. In this paper, an efficient neural networks (NN) model is established to optimize the MPC controller parameters including the prediction horizon, control horizon, and weighting factor. With the developed NN model, the motion tracking process of the micropositioning system is more intelligent and adaptive. The effectiveness of the presented novel NN-MPC control strategy has been verified by conducting extensive simulation studies. Furthermore, the results demonstrate that the NN-MPC scheme has good robustness under model parameter variation and noise condition.

Modelling the Soft Robot Kyma Based on Real‐Time Finite Element Method

AbstractModelling soft robots is a non‐trivial task since their behaviours rely on their morphology, materials and surrounding elements. These robots are very useful to safely interact with their environment and because of their inherent flexibility and adaptability skills. However, they are usually very hard to model because of their intrinsic non‐linearities. This fact presents a unique challenge in the computer graphics and simulation scopes. Current trends in these fields tend to narrow the gap between virtual and real environments. This work will explain a challenging modelling process for a cable‐driven soft robot called Kyma. For this purpose, the real‐time Finite Element Method (FEM) is applied using the Simulation Open Framework Architecture. And two methods are implemented and compared to optimize the model efficiency: a heuristic one and the Model Order Reduction. Both models are also validated with the real robot using a precise motion tracking system. Moreover, an analysis of robot–object interactions is proposed to test the compliance of the presented soft manipulator. As a result, the real‐time FEM emerges as a good solution to accurately and efficiently model the presented robot while also allowing to study the interactions with its environment.

Robust Tracking Control of Spherical Motion Platform for Virtual Reality

This article presents to develop a robust control system of spherical motion platform (SMP) capable of controlling six-degree-of-freedom motion, particularly unlimited rotational motion for better virtual reality. The SMP is the new design of a motion manipulator/simulator driven by the frictional force between a set of spherical wheels and cockpit sphere. However, it has intrinsic difficulties in control due to numerous uncertainties, including slipping motion between spherical wheels and cockpit sphere. Inaccurate motion control may degrade human perception as well as tracking performance. In this article, sliding mode control based on a disturbance observer is developed for the SMP to achieve precise motion tracking performance and provide a more realistic environment regardless of kinematic and dynamic uncertainties. Numerical simulation and experimental results with proportional-integral-differential and the robust controllers are compared for step and sinusoidal inputs to demonstrate the feasibility of motion control and tracking performance in spite of uncertainties. Finally, the SMP is operated using two representative flight maneuvers as a flight simulator. The results show that the SMP with the control system can be utilized to provide motion cue for a human on board for virtual reality.