Reliable Motion Research Articles

The development of AUV control system with accommodation to thruster faults

Abstract A new method for the synthesis of AUV control system for high-precision and highly reliable spatial motion is proposed. The feature of these control systems is that they include an additional accommodation circuit that provides timely detection, determination of the magnitude and compensation for the consequences of the appearance of minor faults that occur in the AUV thrusters. The simulation results confirmed the efficiency and high quality of the synthesized systems. Key words Dynamics modeling of system of solids, unmanned underwater vehicle, UUV, AUUV, mobile robot, hyper-redundant robot, computer design of machines and mechanisms, SolidWorks Motion, MCS.ADAMS. Acknowledgements This work was supported by the Russian Foundation for Basic Research (projects 20-38-70161 and 19-08-00347).

Design and path tracking control of a continuum robot for maxillary sinus surgery.

Continuum robots (CRs) have been developed for maxillary sinus surgery (MSS) in recent years. However, due to the anatomically curved and narrow pathway of the maxillary sinus and the deformable characteristics of the CR, it is still a challenge to accurately approach the target in the sinus. Thus, the CR-assisted MSS demands further research, whether in robotic system design or in reliable motion control. A continuum robotic system integrated with essential instruments and sensors for MSS is developed, and the path tracking control of the designed CR is studied. The differential kinematic model of the CR is constructed. By analyzing the potential problem of the traditional Jacobian-based control, an iterative Jacobian transpose-based closed-loop control method is proposed to improve the path tracking performance. To validate the design of the CR and the effectiveness of the proposed control scheme, different groups of experiments are performed. With the proposed method, the path tracking performance of the CR is improved. Compared with the open-loop Jacobian transpose-based control method, the path tracking error of the proposed method is much less. The maxillary sinus phantom tests are conducted to verify the reachability of the designed CR. Given the reference path from the nostril to the target in the maxillary sinus phantom, experiments show a mean error of 0.96mm. The designed CR is slender, flexible, and able to smoothly approach the target in a tortuous and constrained environment without colliding with or damaging the surrounding tissue. The designed continuum robotic system and the proposed iterative Jacobian transpose-based closed-loop control strategy have great potential for MSS. The limitations of the proposed method are also discussed.

SmartCrawler: A Size-Adaptable In-Pipe Wireless Robotic System with Two-Phase Motion Control Algorithm in Water Distribution Systems.

Incidents to pipes cause damage in water distribution systems (WDS) and access to all parts of the WDS is a challenging task. In this paper, we propose an integrated wireless robotic system for in-pipe missions that includes an agile, maneuverable, and size-adaptable (9-in to 22-in) in-pipe robot, "SmartCrawler", with 1.56 m/s maximum speed. We develop a two-phase motion control algorithm that enables reliable motion in straight and rotation in non-straight configurations of in-service WDS. We also propose a bi-directional wireless sensor module based on active radio frequency identification (RFID) working in 434 MHz carrier frequency and 120 kbps for up to 5 sensor measurements to enable wireless underground communication with the burial depth of 1.5 m. The integration of the proposed wireless sensor module and the two-phase motion controller demonstrates promising results for wireless control of the in-pipe robot and multi-parameter sensor transmission for in-pipe missions.

Short-term motion prediction of a semi-submersible by combining LSTM neural network and different signal decomposition methods

Reliable short-term motion prediction can improve the safety of marine operations. In order to improve the prediction accuracy, the original motion signal is often decomposed into several uncoupled sub-series by using the empirical mode decomposition (EMD) technology. Based on the traditional EMD method, several new signal decomposition methods have been proposed, such as ensemble empirical mode decomposition (EEMD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and empirical wavelet transform (EWT). However, there is still a lack of detailed comparative study research on the influence of different signal decomposition methods on the prediction accuracy of short-term motion. In this study, several hybrid prediction models for short-term motion of semi-submersible are established by combining different signal decomposition methods and the LSTM model, namely EMD-LSTM, EEMD-LSTM, CEEMDAN-LSTM and EWT-LSTM models. Through the comparative analysis of the prediction results of different models, the influence of different signal decomposition methods on the prediction accuracy of short-term motion is investigated. It is found that the EWT-LSTM model consumes the least computing resource, but presents the best performance among different hybrid models.

Simulation of strong earthquake characteristics of a scenario earthquake (MS7.5) based on the enlightenment of 2022 MS6.9 earthquake in Menyuan

The Menyuan area is an important transportation hub in the Hexi Corridor. The Menyuan MS6.9 earthquake that occurred on January 8, 2022 had a major impact on the local infrastructure and transportation of this region. Due to the high possibility of similar strong earthquakes occurring in this area in the future, preliminary assessment of the seismic intensity characteristics of destructive earthquakes in this region is essential for effective disaster control. This paper uses the empirical Green′s function (EGF) method as a numerical simulation tool to predict the ground motion intensity of Datong Autonomous County under the action of the scenario earthquake (MS7.5). Seismic records of aftershocks of the 2016 Menyuan MS6.4 earthquake were used as Green’s functions for this simulation. The uncertainties associated with various source parameters were considered, and 36 possible earthquake scenarios were simulated to obtain 72 sets of horizontal ground motions in Datong County. The obtained peak ground acceleration (PGA) vs. time histories of the horizontal ground motion were screened using the attenuation relationships provided by the fifth-edition of China's Seismic Ground Motion Parameter Zoning Map and the NGA-West2 dataset. Ultimately, 32 possible acceleration-time histories were selected for further analysis. The screened PGA values ranged from 78.8 to 153 cm/s2. The uncertainty associated with the initial rupture point was found to greatly affect the results of the earthquake simulation. The average acceleration spectrum of the selected acceleration-time history exceeded the expected spectrum of a intermediate earthquake, which means that buildings in Datong County might sustain some damage should the scenario earthquake occur. This research can provide reliable ground motion input for urban earthquake damage simulation and seismic design in Datong County. Growing the dataset of small earthquakes recorded in this region will facilitate the large-scale simulation of ground motions under different earthquake scenarios.

Free-breathing myocardial T1 mapping using inversion-recovery radial FLASH and motion-resolved model-based reconstruction.

To develop a free-breathing myocardial mapping technique using inversion-recovery (IR) radial fast low-angle shot (FLASH) and calibrationless motion-resolved model-based reconstruction. Free-running (free-breathing, retrospective cardiac gating) IR radial FLASH is used for data acquisition at 3T. First, to reduce the waiting time between inversions, an analytical formula is derived that takes the incomplete recovery into account for an accurate calculation. Second, the respiratory motion signal is estimated from the k-space center of the contrast varying acquisition using an adapted singular spectrum analysis (SSA-FARY) technique. Third, a motion-resolved model-based reconstruction is used to estimate both parameter and coil sensitivity maps directly from the sorted k-space data. Thus, spatiotemporal total variation, in addition to the spatial sparsity constraints, can be directly applied to the parameter maps. Validations are performed on an experimental phantom, 11 human subjects, and a young landrace pig with myocardial infarction. In comparison to an IR spin-echo reference, phantom results confirm good accuracy, when reducing the waiting time from 5 s to 1 s using the new correction. The motion-resolved model-based reconstruction further improves precision compared to the spatial regularization-only reconstruction. Aside from showing that a reliable respiratory motion signal can be estimated using modified SSA-FARY, in vivo studies demonstrate that dynamic myocardial maps can be obtained within 2 min with good precision and repeatability. Motion-resolved myocardial mapping during free-breathing with good accuracy, precision and repeatability can be achieved by combining inversion-recovery radial FLASH, self-gating and a calibrationless motion-resolved model-based reconstruction.

Behaviour recognition based on the integration of multigranular motion features in the Internet of Things

With the adoption of cutting-edge communication technologies such as 5G/6G systems and the extensive development of devices, crowdsensing systems in the Internet of Things (IoT) are now conducting complicated video analysis tasks such as behaviour recognition. These applications have dramatically increased the diversity of IoT systems. Specifically, behaviour recognition in videos usually requires a combinatorial analysis of the spatial information about objects and information about their dynamic actions in the temporal dimension. Behaviour recognition may even rely more on the modeling of temporal information containing short-range and long-range motions, in contrast to computer vision tasks involving images that focus on understanding spatial information. However, current solutions fail to jointly and comprehensively analyse short-range motions between adjacent frames and long-range temporal aggregations at large scales in videos. In this paper, we propose a novel behaviour recognition method based on the integration of multigranular (IMG) motion features, which can provide support for deploying video analysis in multimedia IoT crowdsensing systems. In particular, we achieve reliable motion information modeling by integrating a channel attention-based short-term motion feature enhancement module (CSEM) and a cascaded long-term motion feature integration module (CLIM). We evaluate our model on several action recognition benchmarks, such as HMDB51, Something-Something and UCF101. The experimental results demonstrate that our approach outperforms the previous state-of-the-art methods, which confirms its effectiveness and efficiency.

Wearable, ultrathin and breathable tactile sensors with an integrated all-nanofiber network structure for highly sensitive and reliable motion monitoring

Wearable tactile sensors are under high pursuit in healthcare monitoring. However, most of previous sensors were assembled by non-binding multilayers and built on airtight substrates, hindering the wearability with comfort. Herein, we develop an ultrathin and breathable tactile sensor based on an integrated all-nanofiber network via a facile electrospinning and spray-drying technique. The sensor is constructed by directly ink-patterning of conductive Co3O4/PEDOT:PSS/AgNW electrodes on both sides of one single ionic IL/TPU fabric electrolyte mat, the overall thickness of which is only 40 µm for a conformal attachment on any curved surface. The highly porous nanofiber network endows the sensor with both high air and moisture permeabilities, ensuring an excellent comfortability for a long-time wearing. Due to the pseudocapacitive sensing mechanism and nanofiber-based microstrucutre, the sensor exhibits ultrahigh sensitivity of 79.5 kPa−1, fast response time of 40 ms, and a wide detection range of 38 kPa. Reliability tests show that the sesnor is capable of outputting stable and repeatable sensing signals for over 10,000 loading/unloading cycles of 5 kPa on testing machine and for over 5,000 s of continuous motion detection of foot stamping and elbow bending. Monitoring of arm swinging and elbow bending by sensor units and recognition of hand gesture by sensor array are also demonstrated towards practical wearable sensing applications.

Markerless motion evaluation via OpenPose and fuzzy activity evaluator

ABSTRACT Human motion evaluation techniques can be categorized as marker-based and markerless-based. The former can be time-consuming and expensive, while the latter has the advantage of being online, fast, and noninvasive. This study presents the development of a markerless-based system by means of OpenPose, Python codes, and fuzzy inference for push-up motion evaluation. The markerless technique generally produced good accuracy in locating human joint centers and formed the skeleton. A fuzzy activity evaluator was introduced to help resolve possible uncertainty occurring at the elbow joint due to possible abduction or adduction. The fuzzy evaluator used degree of truth for each of all three joint angles to consider the overall quality of motion. With the aid of fuzzy inference, the evaluation system produced satisfactory and reliable results by taking both qualitative and quantitative measures into account. The presented technique demonstrated how a combination of hard computing and soft computing can be a powerful tool for developing a reliable motion evaluation. Notably, the developed system used low-cost hardware and open-source software. The technique can also be applied to evaluation of rehabilitation exercise, or industrial safety, such as instantly identifying improper or dangerous motions in production.

Mathematical study of neural feedback roles in small target motion detection.

Building an efficient and reliable small target motion detection visual system is challenging for artificial intelligence robotics because a small target only occupies few pixels and hardly displays visual features in images. Biological visual systems that have evolved over millions of years could be ideal templates for designing artificial visual systems. Insects benefit from a class of specialized neurons, called small target motion detectors (STMDs), which endow them with an excellent ability to detect small moving targets against a cluttered dynamic environment. Some bio-inspired models featured in feed-forward information processing architectures have been proposed to imitate the functions of the STMD neurons. However, feedback, a crucial mechanism for visual system regulation, has not been investigated deeply in the STMD-based neural circuits and its roles in small target motion detection remain unclear. In this paper, we propose a time-delay feedback STMD model for small target motion detection in complex backgrounds. The main contributions of this study are as follows. First, a feedback pathway is designed by transmitting information from output-layer neurons to lower-layer interneurons in the STMD pathway and the role of the feedback is analyzed from the view of mathematical analysis. Second, to estimate the feedback constant, the existence and uniqueness of solutions for nonlinear dynamical systems formed by feedback loop are analyzed via Schauder's fixed point theorem and contraction mapping theorem. Finally, an iterative algorithm is designed to solve the nonlinear problem and the performance of the proposed model is tested by experiments. Experimental results demonstrate that the feedback is able to weaken background false positives while maintaining a minor effect on small targets. It outperforms existing STMD-based models regarding the accuracy of fast-moving small target detection in visual clutter. The proposed feedback approach could inspire the relevant modeling of robust motion perception robotics visual systems.

Using a single inertial sensor to control exergames for children with cerebral palsy

Abstract Introduction: Children with cerebral palsy (CCP) benefit from intensive arm training. Exergames that can be played at home offer the possibility to increase the frequency of therapy but require reliable and accurate real-time motion tracking via easy-to-use sensors in unsupervised settings and magnetically disturbed environments. Method: We propose an inertial-sensor-based method with a single sensor on the wrist for real-time tracking of the inclination of the forearm. The control parameter of the game was validated with an optical marker-based ground truth system. Results: First experiments with a therapist performing training movements in a healthy and simulated spastic manner show that the forearm inclination well captures the motion dynamics. The accuracy of the inertial-sensor-based measurement is validated with respect to the reference system in three healthy subjects. Orientation offsets between the inertial sensor and the forearm marker set in the range of 2° ̶ 6° and dynamic measurement errors about 3.1° were obtained. Conclusion: This work demonstrates that the proposed method is suitable for real-time control of exergames of CCP. The validation with an optical reference system showed that the forearm inclination can be used as for feedback and therapeutic progress monitoring.

Toward Safer Navigation of Heterogeneous Mobile Robots in Distributed Scheme: A Novel Time-to-Collision-Based Method.

For safe and efficient navigation of heterogeneous multiple mobile robots (HMRs), it is essential to incorporate dynamics (mass and inertia) in motion control algorithms. Many methods rely only on kinematics or point-mass models, resulting in conservative results or occasionally failure. This is especially true for robots with different masses. In this article, we develop a novel navigation methodology for a distributed scheme by incorporating the robots' dynamics through calculating the time to collision (TTC) and designing a new controller accordingly that avoids collisions. We first propose a new predictive collision term by TTC that will be used to quantify imminent collisions among HMRs. Subsequently, using this term, we develop a novel nonlinear controller that explicitly incorporates TTC in the design and guarantees collision-free motion. Simulations and experiments were performed to demonstrate the effectiveness of the developed methods. We first compared the results of our proposed approach with controllers that only consider the robots' kinematics. It was shown that the proposed control strategy (a TTC-based controller) proves to be less conservative when determining safe motions. Specifically, for environments with limited space, it was demonstrated that using robots' kinematics may result in a collision, while our strategy results in safe motion. We also performed experiments that proved collision-free navigation of HMRs with this approach. The outcomes of this work provide more reliable motion control for HMRs, especially when the robots' masses or inertias are significantly different, for example, warehouses. The developments in this work are also applicable to vehicles and can therefore be beneficial in automated collision avoidance in autonomous driving and intelligent transportation.

Skyrmions in synthetic antiferromagnets and their nucleation via electrical current and ultra-fast laser illumination

Magnetic skyrmions are topological spin textures that hold great promise as nanoscale information carriers in non-volatile memory and logic devices. While room-temperature magnetic skyrmions and their current-induced motion were recently demonstrated, the stray field resulting from their finite magnetisation and their topological charge limit their minimum size and reliable motion. Antiferromagnetic skyrmions allow to lift these limitations owing to their vanishing magnetisation and net zero topological charge, promising ultra-small and ultra-fast skyrmions. Here, we report on the observation of isolated skyrmions in compensated synthetic antiferromagnets at zero field and room temperature using X-ray magnetic microscopy. Micromagnetic simulations and an analytical model confirm the chiral antiferromagnetic nature of these skyrmions and allow the identification of the physical mechanisms controlling their size and stability. Finally, we demonstrate the nucleation of synthetic antiferromagnetic skyrmions via local current injection and ultra-fast laser excitation.

Time-evolution of electrical resistance-strain hysteresis curve of embroidered stretch sensors and their application in reliable human motion tracking

Time-evolution of electrical resistance-strain hysteresis curve of embroidered stretch sensors and their application in reliable human motion tracking

Stereotactic Body Radiotherapy for Locally Advanced Pancreatic Cancer Using Optical Surface Management System - AlignRT as an Optical Body Surface Motion Management in Deep Breath Hold Patients: Results from a Single-Arm Retrospective Study.

PurposeTo assess the efficacy and safety of stereotactic body radiotherapy for patients with unresectable, locally advanced pancreatic cancer using Optical Surface Management System – AlignRT (OSMS-AlignRT) as an optical body surface motion management in deep breath hold.Patients and MethodsForty-five patients diagnosed with locally advanced pancreatic cancer were treated with stereotactic body radiotherapy in 3 or 5 fractions, and received varying BED10 (median 79.5 Gy) from April 2017 to December 2020. All patients were treated in deep breath hold with OSMS-AlignRT used as optical body surface motion management. Thirty-three patients received systemic treatment before and/or after stereotactic body radiotherapy, and twelve patients received no systemic treatment. In this retrospective, observational, single-arm study, primary endpoints were overall survival and freedom from local progression (ie, local control). Secondary endpoints were progression-free survival and toxicity. Actuarial survival analysis and univariate analysis were investigated.ResultsData from forty-five patients were analyzed. Median follow-up was 15 months. One-year freedom from local progression and survival were 95.5% and 71.1%, respectively. Median progression-free survival was 14 months. Median overall survival from diagnosis for all patients was 17 months, and 19 months for patients alive at the time of analysis. No patient had >G2 toxicity.ConclusionStereotactic body radiotherapy for locally advanced pancreatic cancer using OSMS-AlignRT as optical body surface motion management in deep breath hold patients is an effective and safe local treatment option, with no >G2 toxicity, and could be a promising therapeutic option with acceptable toxicity, either as a single treatment or in a multimodal regimen. OSMS-AlignRT provided accurate and reliable body surface motion management during stereotactic body radiotherapy.

Waterproof and Breathable Graphene‐Based Electronic Fabric for Wearable Sensors

AbstractElectronic skin (E‐skin) is widely used in bionic prosthesis, soft robotics, and wearable electronic devices. Waterproof and breathable membranes that provide a high level of protection and comfort are promising core materials for meeting the pressing demand for E‐skin. However, creating such materials exhibiting waterproofness, breathability, and ease of fabrication has proven to be a great challenge. In this paper, waterproof, breathable Modal‐based knitted fabric E‐skin is proposed by using a simple, low‐cost three‐step process including the graphene oxide (GO) loading, GO‐reducing, and polydimethylsiloxane loading. The prepared E‐skin is insensitive to common liquids in daily life including water, tea, milk, and coffee, showing superior waterproof capability. The breathability is also tested with a water vapor transmission rate of ≈13.6 mg cm−2 h−1. Besides, this E‐skin can achieve reliable human motion monitoring. The authors believe this E‐skin can provide a reliable principle for further design and development of waterproof and breathable electronic skin.

Fuzzy Approximation-Based Task-Space Control of Robot Manipulators With Remote Center of Motion Constraint

The presence of unknown physical interaction between the patients’ body and surgical tool in laparoscopic surgery requires a secure end-effector positioning while assuring a reliable constraint motion. In this work, a task-space control approach based on fuzzy approximation is proposed for a teleoperated surgery scenario utilizing a serial redundant robot manipulator (7 degrees of freedom), the motions of which are constrained with respect to a point known as remote center of motion (RCM). The dynamical uncertainties due to the physical interaction are considered and estimated to maintain operational accuracy by introducing the decoupled adaptive fuzzy approximation technique. Experiments verify the presented approach’s effectiveness. Results indicate that with the proposed control method, the accuracy of the surgical operation is improved while the safety can be ensured for teleoperated surgery with RCM constraint.

Development of a frequency-controlled inertial type piezoelectric locomotion method with nano-scale motion resolution driven by a symmetrical waveform

This study proposes and analyzes a novel inertial type piezoelectric locomotion method that can achieve bi-directional motion with a single piezoelectric element under a symmetrical sawtooth wave. In a stepwise approach, the dynamic responses of the guide at valley/peak points and rising/falling phases are different, and can be transformed into forward or backward stepping motion of a slider via friction by adjusting the driving frequency. A dynamic model is established and a compact prototype is developed to better understand this novel locomotion method. Simulations and a series of experiments verify that the proposed locomotion method can achieve reliable bi-directional motion under various conditions. Maximum velocities of 1.764 mm/s forward and 2.248 mm/s backward were achieved at frequencies of 300 Hz and 400 Hz, respectively. In addition, the stepping resolution was observed to reach 20 nm forward and 13 nm backward at respective frequencies of 175 and 350 Hz. The frequency-controlled locomotion method is outstanding in fast and fine actuation, and greatly simplifies the design and control of existing inertial type piezoelectric actuators.

Improved inter-view correlations for low complexity MV-HEVC

The more advanced multi-view extension, MV-HEVC, effectively exploits visual similarities between multi-view videos and enables high compression efficiency. Each view in the multi-view sequence depends on the captured scene, the distance between cameras and recording angles. Increasing the distance between dependent viewpoints generates an inter-view disparity. This impacts the inter-view similarities, affects the disparity estimation and further increases the computational complexity of the MV-HEVC encoder. In this paper, an efficient earlier disparity estimation is proposed for low complexity MV-HEVC. This algorithm is based on reducing the complexity of disparity estimation by eliminating the inter-view offset. Moreover, the inter-view similarities are controlled by considering the reliability of each coding unit size in the search range. This reliability is estimated by reducing the number of searching points within a new limited window. For reliable motion estimation, we further proposed an earlier decision of coding units splitting in the dependent views according to those in the reference views. Experimental results show that the proposed algorithm can achieve an average encoding time saving of 20.37%–40,61% with marginal performance degradation.

Multi-input adaptive neural network for automatic detection of cervical vertebral landmarks on X-rays

Cervical vertebral landmark detection is a significant pre-task for vertebral relative motion parameter measurement, which is helpful for doctors to diagnose cervical spine diseases. Accurate cervical vertebral landmark detection could provide reliable motion parameter measurement results. However, different cervical spines in X-rays with various poses and angles have imposed quite challenges. It is observed that there are similar appearances of vertebral bones in different cervical spine X-rays. For this, to fully use these similar features, a multi-input adaptive U-Net (MultiIA-UNet) focusing on the similar local features between different cervical spine X-rays is put forward to do cervical vertebral landmark detection accurately and effectively. MultiIA-UNet used an improved U-Net structure as backbone network combining with the novel adaptive convolution module to better extract changing global features. At training, MultiIA-UNet applied a multi-input strategy to extract features from random pairs of training data at the same time, and then learned their similar local features through a subspace alignment module. We collected a dataset including 688 cervical spine X-rays to evaluate MultiIA-UNet. The results exhibited that our method demonstrated the state-of-the-art performance (the minimum average point to point error of 12.988 pixels). In addition, we further evaluated the effect of these landmark detection results on cervical motion angle parameter measurement. It showed that our method was capable to obtain more accurate cervical spine motion angle parameters (the minimum symmetric mean absolute percentage is 26.969%). MultiIA-UNet could be an efficient and accurate landmark detection method for doctors to do cervical vertebral motion analysis.