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Leveraging Rubik Tables, the authors designed a constant-factor optimal algorithm for stack rearrangement and multi-robot motion planning problems under extreme robot density. A general algorithm and several specialized algorithms are then introduced to solve the problem, constructing a product between the event model and a specification automaton. Toward that, the authors analyze closed-loop dynamics to identify conditions that lead to deadlock and, using duality theory and KKT conditions, derive geometric properties of robot configurations in deadlock. [Extracted from the article]

This special issue is organized by selected papers presented at International Symposium on Robotics Research (ISRR 2019), held on October 6-10, 2019, at Hanoi, Vietnam. The guest editors would like to express their sincere gratitude to the authors for their patience by the review process that was slowed down due to the difficulties caused by the worldwide pandemic and are very delighted this special issue is finally complete. The robotics fundamental contributions are about algorithmic foundation for manipulation and innovative robotics design and two papers are included for each topic. [Extracted from the article]

The fracture phenomenon of fastener clips occurs frequently in the sections with serious rail corrugation of high-speed railway, which brings huge potential safety hazards to track service and driving safety. In order to prevent the vibration fatigue fracture of fastener clips in high-speed railway, a dynamic vibration absorber (DVA) for clips is proposed in this paper to suppress the primary sensitive frequency of the corresponding resonant of clips. Firstly, based on the optimal design theory of DVA, a parameters design method of ring-shaped DVA suitable for clips is proposed. Secondly, the vibration characteristics of the high-speed railway fastener system are investigated, and the main reason for the fastener clips fracture is analyzed by simulation analysis combined with filed test. Then, focusing on the primary resonant frequency of clips, the vibration reduction effect of the ring-shaped DVA is analyzed. The results show that the ring-shaped DVA can change the natural frequency of the clip system, reduce the vibration response in the range of the original resonant frequency, and avoid the resonance phenomenon. Finally, the influences of mass ratio, frequency ratio, and damping ratio on the vibration reduction effect are analyzed, and the effectiveness of the parameters design method of ring-shaped DVA proposed in this paper is further proved. The research results can provide methodological support for the anti-breakage design of high-speed railway fastener clips. [ABSTRACT FROM AUTHOR]

Transparent objects are encountered frequently in our daily lives, yet recognizing them poses challenges for conventional vision sensors due to their unique material properties, not being well perceived from RGB or depth cameras. Overcoming this limitation, thermal infrared cameras have emerged as a solution, offering improved visibility and shape information for transparent objects. In this paper, we present TRansPose, the first large-scale multispectral dataset that combines stereo RGB-D, thermal infrared (TIR) images, and object poses to promote transparent object research. The dataset includes 99 transparent objects, encompassing 43 household items, 27 recyclable trashes, 29 chemical laboratory equivalents, and 12 non-transparent objects. It comprises a vast collection of 333,819 images and 4,000,056 annotations, providing instance-level segmentation masks, ground-truth poses, and completed depth information. The data was acquired using an FLIR A65 thermal infrared camera, two Intel RealSense L515 RGB-D cameras, and a Franka Emika Panda robot manipulator. Spanning 87 sequences, TRansPose covers various challenging real-life scenarios, including objects filled with water, diverse lighting conditions, heavy clutter, non-transparent or translucent containers, objects in plastic bags, and multi-stacked objects. Supplementary material can be accessed from the following link: https://sites.google.com/view/transpose-dataset. [ABSTRACT FROM AUTHOR]

Since the transmission path of inter-shaft bearing-fault signal is complex, a fault feature extraction method based on hierarchical permutation entropy (HPE) and locally linear embedding (LLE) algorithm is proposed in this paper. In this method, HPE is utilized to extract fault information of signals, and LLE is utilized to reduce and fuse high-dimensional fault features of multi-sensors to construct fault samples. Then, the random forest (RF) model is established to diagnose the faults of the inter-shaft bearings. The fault simulation test rig with the inter-shaft bearing is built to simulate the normal bearing, inner ring fault, outer ring fault, and rolling ball fault, and the data are collected to verify the HPE-LLE-RF fault diagnosis algorithm of inter-shaft bearings established in this paper. The experimental results show that the proposed algorithm can extract the fault features of inter-shaft bearings effectively with a fault diagnosis accuracy of 93.3% without overfit phenomenon. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of decentralized, collaborative state estimation in robotic teams. In particular, this paper considers problems where individual robots estimate similar physical quantities, such as each other's position relative to themselves. The use of pseudomeasurements is introduced as a means of modeling such relationships between robots' state estimates and is shown to be a tractable way to approach the decentralized state estimation problem. Moreover, this formulation easily leads to a general-purpose observability test that simultaneously accounts for measurements that robots collect from their own sensors, as well as the communication structure within the team. Finally, input preintegration is proposed as a communication-efficient way of sharing odometry information between robots, and the entire theory is appropriate for both vector-space and Lie-group state definitions. To overcome the need for communicating preintegrated covariance information, a deep autoencoder is proposed that reconstructs the covariance information from the inputs, hence further reducing the communication requirements. The proposed framework is evaluated on three different simulated problems, and one experiment involving three quadcopters. [ABSTRACT FROM AUTHOR]

Socially aware robot navigation is gaining popularity with the increase in delivery and assistive robots. The research is further fueled by a need for socially aware navigation skills in autonomous vehicles to move safely and appropriately in spaces shared with humans. Although most of these are ground robots, drones are also entering the field. In this paper, we present a literature survey of the works on socially aware robot navigation in the past 10 years. We propose four different faceted taxonomies to navigate the literature and examine the field from four different perspectives. Through the taxonomic review, we discuss the current research directions and the extending scope of applications in various domains. Further, we put forward a list of current research opportunities and present a discussion on possible future challenges that are likely to emerge in the field. [ABSTRACT FROM AUTHOR]

3D spatial perception is the problem of building and maintaining an actionable and persistent representation of the environment in real-time using sensor data and prior knowledge. Despite the fast-paced progress in robot perception, most existing methods either build purely geometric maps (as in traditional SLAM) or "flat" metric-semantic maps that do not scale to large environments or large dictionaries of semantic labels. The first part of this paper is concerned with representations: we show that scalable representations for spatial perception need to be hierarchical in nature. Hierarchical representations are efficient to store, and lead to layered graphs with small treewidth, which enable provably efficient inference. We then introduce an example of hierarchical representation for indoor environments, namely a 3D scene graph, and discuss its structure and properties. The second part of the paper focuses on algorithms to incrementally construct a 3D scene graph as the robot explores the environment. Our algorithms combine 3D geometry (e.g., to cluster the free space into a graph of places), topology (to cluster the places into rooms), and geometric deep learning (e.g., to classify the type of rooms the robot is moving across). The third part of the paper focuses on algorithms to maintain and correct 3D scene graphs during long-term operation. We propose hierarchical descriptors for loop closure detection and describe how to correct a scene graph in response to loop closures, by solving a 3D scene graph optimization problem. We conclude the paper by combining the proposed perception algorithms into Hydra, a real-time spatial perception system that builds a 3D scene graph from visual-inertial data in real-time. We showcase Hydra's performance in photo-realistic simulations and real data collected by a Clearpath Jackal robots and a Unitree A1 robot. We release an open-source implementation of Hydra at https://github.com/MIT-SPARK/Hydra. [ABSTRACT FROM AUTHOR]

In this paper, a new dynamic photogrammetry method using an unmanned aerial vehicle (UAV) is proposed and the UAV-based operational modal analysis method is established. The presented dynamic photogrammetry method can measure the dynamic displacements of structure using one or two cameras. However, it is difficult to use fixed cameras or handheld cameras to measure the dynamic responses for some outdoor structures, because it is hard to reach for measurement or maintenance sometimes. Therefore, UAV is an effective alternative and the homography-based perspective rectification method can be used to correct the UAV images. However, the presented methods use only four calibration points. Additionally, there is still obvious signal drift in the extracted dynamic responses. In this paper, a new time varying homography matrix, calculated using n calibration points, is proposed here for the first time to correct the perspective distortion for image at different time and the relative displacements are used to eliminate the influence of UAV in-plane motion. The influence analysis of UAV vibration and the method to eliminate the UAV vibration are the novelty of this paper. And then, the UAV-based operational modal analysis method is proposed. The proposed method is verified using a plane frame structure in laboratory. The results show that the proposed method can effectively eliminate the vibration of UAV and identify the structural modal parameters. Under the impulse excitation and white noise excitation, when the calibration points n is larger than 60, the relative errors of natural frequency and damping ratio are less than 0.4% and 5.45%, respectively. And the MAC values are all greater than 0.99. Meanwhile, the influence of the number of adopted calibration points on modal parameters identification is considered in the experiment and the advised value is given. [ABSTRACT FROM AUTHOR]

Kalman filter has emerged as a powerful tool for unbalance identification in rotating machinery. Recently, the augmented Kalman filter combined with the finite element model has grown up and projects its potential for complex rotor systems. This paper investigates the application of the augmented Kalman filter (AKF) to a practical turbofan engine. The current study reveals that using steady-state responses as measurements can cause fluctuation in the estimated results, even divergence for some cases, while the available signals in practice are steady-state responses generally. To the authors' knowledge, this practical problem is revealed for the first time. To address the problem, the convergence criterion is employed to improve the AKF and formulates the adaptive fading augmented Kalman filter (AFAKF) proposed in this paper. Results indicate that the increase of the amplification factor, the insufficient measurement points, and the complexity of the dynamic model can all lead to the deterioration of the estimated unbalance. The proposed AFAKF method shows favorable convergence and can achieve accurate estimation with less than 5% relative errors, and the superiority over AKF in computation cost is also observed. [ABSTRACT FROM AUTHOR]

Bearing is the most vulnerable key part in rotating machine and bears important influence on the safety of equipment. Weakness and complexity are the two features of fault characteristic information carried by signals in the early stage of fault. For that, a fault is difficult to be recognized correctly. To identify a compound failure of bearing, the paper has brought forward a new self-adaptive option method for component signals that are sensitive to failure feature information of bearing. The sensitivity of kurtosis to bearing failure is exploited and the influence of signal complexity on the extraction of failure feature information is taken seriously, the paper has proposed the self-adaptive option method for component signals that are sensitive to failure feature by combined kurtosis with Complexity parameter included in Hjorth parameters. Furthermore, as the mid-value represents the general level of signal and is not affected by larger or smaller data, with the mid-values of kurtosis and Complexity parameter as the boundary, the paper has chosen the component signals which can more comprehensively show the failure features of bearing. Additionally, by principal component analysis (PCA), component signals selected are blended and reconstructed. Finally, by the Hilbert envelope spectrum of signals reconstructed, failure types of bearing are identified. To verify the effectiveness of presented method, the presented method is compared with conventional method on the basis of the exactly consistent data. The result indicates that the proposed method is superior to the traditional one in extracting fault information and identifying the multiple failure types of bearing. [ABSTRACT FROM AUTHOR]

Honeycomb paperboard has been widely used in transportation and packaging of electronic instruments and furniture owing to the advantages of light weight, good compression resistance, environmental friendliness, and easy degradation. As a common cushioning material in transportation, it is worth studying the damping characteristic and vibration reduction mechanism of honeycomb paperboard. Based on the basic principles of vibration mechanics, the damping of honeycomb paperboard was proposed. Then the influences of cell length, paperboard thickness, grammage of core paper, and the honeycomb structures with face sheet or without face sheet on the damping characteristic of honeycomb paperboard were analyzed. Finally, the vibration reduction mechanism of packaging system with honeycomb paperboard and block was discussed briefly. The results show that the damping value of honeycomb paperboard decreases with the increase of cell length and paperboard thickness. The honeycomb paperboard with large grammage of core paper has high damping value, and yet paper honeycomb core structure without face sheet has poor damping capacity. What's more, the vibration reduction capacity of packaging system is influenced by the deformation and damping characteristic of honeycomb paperboard, among which material damping and structural damping contribute more, honeycomb deformation and air damping contribute less. The weight of block does not affect the damping characteristic of honeycomb paperboard, but affects the vibration reduction capacity of the whole system. [ABSTRACT FROM AUTHOR]

This paper proposes a new disturbance observer concept based on the information of the estimated and measured signals for a micro-electro-mechanical system. First, the sliding-mode observer based on the linear matrix inequality was designed to estimate the states of a micro-electro-mechanical system gyroscope. Second, a new disturbance observer was proposed for estimating perturbations of the T-S fuzzy micro-electro-mechanical system gyroscope. Third, the double phase's fixedtime sliding-mode control was designed to control the positions and velocities of the MEMS system. The proposed disturbance observer input signals were taken into account from the measured and estimated signals. Two cases of high magnitudes and high frequencies disturbances were used to test the power of the proposed disturbance observer. Fourth, the Lyapunov condition was used to verify the corrections of the proposed controller and observer. Finally, the simulation by using MATLAB software was used to show the power of the proposed methods. The achievements of the small reachingtime, small tracking error, and stable steady-states under the vibration form outside of the system. [ABSTRACT FROM AUTHOR]

Periodic structures are a type of metamaterial in which the physical properties depend not only on the details of the unit cell but also on how unit cells are arranged and interact with each other. In conventional engineering structures, each unit cell interacts with adjacent cells. Methods developed for vibrational and wave propagation analysis in periodic engineering structures consider only nearest-neighbor interactions. The dispersion curves of such systems, in which only adjacent cells interact, have been extensively studied. Metamaterial properties depend on the interactions of a unit cell with other cells. Further interactions, and specifically, interactions beyond the closest neighbors, imply a more complex band structure and wave behavior. In this paper, an example class of such structures, in which electrostatic forces are the driving force, has been investigated. In this paper, properties affecting these periodic structures, such as elastic forces, have been investigated. An attractive property of such structures is that the band structures of such metamaterials can be tuned by changing electric voltages. [ABSTRACT FROM AUTHOR]

Control effects of leading-edge grooves on deep cavity noise are investigated numerically. The length-to-depth ratio of the grooves are 0.5, 1, and 4, respectively. The freestream Mach number is equal to 0.16, corresponding to the Re based on the cavity length of 7.7 × 105. The Detached Eddy Simulations (DES) combined with Ffowcs Williams–Hawkings (FW-H) acoustic analogy are adopted to simulate the characteristic information of the flow field and the acoustic field. The analysis results show that all grooves investigated in this paper have a certain noise control effect, and the groove with the most obvious noise reduction effect is the groove with a length-to-depth ratio of 0.5, namely, the deep groove. Narrowband noise generated by the flow-acoustic feedback (100–700 Hz) and the acoustic resonance mechanism (above 300 Hz) and broadband noise caused by the turbulent disturbance in the shear layer of the cavity mouth significantly reduce when the deep groove is used for the noise control. The reason for the noise reduction is that the leading-edge grooves can effectively change the flow characteristics near the mouth of the downstream cavity. When the flow passes the grooves, the groove flow reduces the energy in the fluid, resulting in the significant decrease of flow velocity of the boundary layer of the incoming flow of the cavity. In addition, the use of the deep groove also promotes the vortex concentration position to move towards the bottom of the cavity, pushes the energy in the shear layer of the downstream cavity towards the front-edge as well as the bottom of the cavity, and stabilizes the development of the shear layer near the cavity mouth. [ABSTRACT FROM AUTHOR]

In the active magnetic bearing (AMB)-rotor system, a displacement sensor is one of the indispensable components. It detects the displacement of the rotor in real time and feeds back to the control system to realize the stable suspension of the rotor. However, due to electromagnetic interference, unstable power supply, poor grounding, and uneven rotor surface, the signal runout of the displacement sensor can often occur, resulting in rotor vibration. Given the above problems, this paper establishes the control model of the AMB-rotor system, lists three typical sensor runout signals, then analyzes the influence of different types of sensor runout on the system, and discusses the control methods under different sensor runout conditions. The research results have a specific guiding significance for vibration control of the maglev rotor under sensor runout influence. [ABSTRACT FROM AUTHOR]

Free vibration characteristics in uniform beams with several lumped attachments are an important problem in engineering applications that have to deal with mounting different equipment (e.g. motors, oscillators or engines) on a structural beam. In order to solve the lack of a generalized automatic procedure, this investigation presents a simple solving approach based on analytical means applied to a secular frequency equation for obtaining the natural frequencies of an arbitrarily supported single-span, or multi-span Euler–Bernoulli beam carrying any combination of miscellaneous attachments. The approach is obtained by solving a characteristic polynomial equation using a classical method for computing the roots of a polynomial. Interestingly, if the number of elements is greater than one, a pole-zero cancellation is needed, but it does not require manual interventions such as initial values and iteration. The mathematical approach is validated with bibliographic references and evaluated for accuracy and computational effectiveness. A good agreement is observed with relative error values practically negligible mostly ranging between 10−3 and 10−9 in the first five natural frequencies, which confirms the validity of the presented approach in this paper. The MatLab code that has been developed with the solving approach is freely available as a supplementary material to this paper. Additionally, a MatLab graphical user interface has also been developed in this work which allows to obtain the eigenvalues of a simply supported Euler–Bernoulli beam carrying an undetermined number of lumped elements. The graphical user interface is also available for download, along with help facilities to be run in a Windows operating system and detailed instructions to reproduce the case studies presented here. The proposed scheme (and also the MatLab graphical user interface) is very easy to code, and can be slightly modified to accommodate beams with arbitrary supports. [ABSTRACT FROM AUTHOR]

A static world assumption is often used when considering the simultaneous localization and mapping (SLAM) problem. In reality, especially when long-term autonomy is the objective, this is not a valid assumption. This paper studies a scenario where landmarks can occupy multiple discrete positions at different points in time, where each possible position is added to a multi-hypothesis map representation. A selector-mixture distribution is introduced and used in the observation model. Each landmark position hypothesis is associated with one component in the mixture. The landmark movements are modeled by a discrete Markov chain and the Monte Carlo tree search algorithm is suggested to be used as component selector. The non-static environment model is further incorporated into the factor graph formulation of the SLAM problem and is solved by iterating between estimating discrete variables with a component selector and optimizing continuous variables with an efficient state-of-the-art nonlinear least squares SLAM solver. The proposed non-static SLAM system is validated in numerical simulation and with a publicly available dataset by showing that a non-static environment can successfully be navigated. [ABSTRACT FROM AUTHOR]

This paper studies the continuous-time behavior of sampled-data control systems. In these systems, a discrete-time control law is applied to a continuous-time system model using sampling and zero-order hold. Traditionally, sampled-data systems are modeled in discrete-time, and thus, the intersample dynamics cannot be examined. This paper shows that the classical methods (such as the d2c function of MATLAB) for obtaining continuous-time models can be inaccurate between the sampling instants, even when Shannon's sampling theorem is satisfied. In this paper, a new method is also presented which can be used to characterize the continuous-time dynamics of sampled-data control systems. The new continuous-time system model including the intersample behavior is derived analytically from the discrete-time model, and the results are verified by simulations. [ABSTRACT FROM AUTHOR]

The study is an attempt to understand the trends in LIS research by analyzing published literature on the topic. The study identifies and analyses 39 research papers on LIS research trends in various countries, three papers on LIS research trends in regional countries and 13 papers on LIS research trends with an international perspective. The findings of the study reveal that there is a similarity among various countries as far as the LIS research topics are concerned but with a different focus at different periods. While understanding international research trends in LIS, it was interesting to note that the research trend in China was similar to the worldwide research trend while the pattern in other countries differed. [ABSTRACT FROM AUTHOR]

The hunting motion of the vehicle increases the wear of the wheel tracks and affects the lateral stability of the vehicle system and safety, so the hunting motion of the vehicle needs to be recognized. However, the existing recognition methods ignore the small-amplitude hunting motion that precedes the onset of the running motion. In addition, due to the scarcity of real hunting data of high-speed trains, there is a problem of underfitting when training with traditional deep learning methods. In this paper, first, the dynamics model of a high-speed train is established, and the normal, small-amplitude hunting and hunting motions of high-speed trains in the process are simulated. Second, this paper proposes a transfer learning-based method for high-speed train hunting motion recognition. The method uses easily collected normal data samples in the training process, does not use real data samples of small-amplitude hunting and hunting motions, and completes the high-speed train running motions recognition task by transferring from simulation data (source domain) to real data (target domain). Finally, the validation is carried out using the real data, which proves that the method-related approach has some engineering application value in the intelligent monitoring of high-speed trains. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of determining the exact value of the maximum allowable upper bound (MAUB) of delay of an LTI system in the presence of the delay parameters. The delay parameter is supposed to be time-invariant, and this paper proposes a systematic method to exactly solve the mentioned problem. For this purpose, a modified system matrix is defined based on the delay system's model that involves an extra complex variable on the boundary of the unit circle. It will be shown that the characteristic polynomial of the modified system can equivalently assess the system's stability. The characteristic polynomial of the modified system is a quadratic function of the imaginary part of the extra complex variable which enables us to rewrite the polynomial based on only the real part of the extra variable. This property is exploited to develop an explicit formula to find the MAUB based on the critical numbers (complex numbers that force the modified characteristic polynomial to have pure imaginary roots) and their related pure imaginary roots. The method is tested for some sample delay systems and the results demonstrate a tighter bound for the MAUB of the systems. [ABSTRACT FROM AUTHOR]

Improving the control performance of dynamic vibration absorbers has recently been effective by introducing a grounded negative stiffness device. However, the negative stiffness structure is unstable and difficult to achieve in engineering practice , and its major drawback is that it amplif ies the vibration response of the primary system at low frequency region. Meanwhile, some mechanical devices can be combined to make the DVA work even better with a grounded positive stiffness. For this purpose, this paper combines for the first time the control effect of the inerter device and grounded positive stiffness into a three-element DVA model in order to better improve vibration reduction of an undamped primary system under excitation. First, the dynamic equation of motion of the system is written according to Newton 's second law. Then, the steady-state displacement response of the primary system under harmonic excitation is calculated. In order to minimize the resonant response of the primary system around its natural frequency, the extended fixed point theory is applied. Thus, the optimized parameters such as the tuning frequency ratio, the stiffness ratio , and the approximate damping ratio are determined as a function of mass ratio and inerter – mass ratio. From the results analysis, it was found that the inerter – mass ratio has a better working range to guarantee the stability of the coupled system. Then , study on the effect of inerter – mass ratio on the primary system response is carried out. It can be seen that increasing the inerter – mass ratio in the optimal working range can reduce the response of the primary system beyond its uncontrolled static response. However, it is necessary to avoid the situation where the inerter – mass ratio is very large because it can lead to unrealistic optimal parameters. Finally, comparison with other DVA models is show n under harmonic and random excitation of the primary system. It is found that the proposed DVA model in this paper has high control performance and can be used in many engineering practice s. [ABSTRACT FROM AUTHOR]

In this paper, a new adaptive method is proposed for online frequency estimation of a periodic disturbance by the output measurement based on the fast Fourier transform algorithm. The performance of the proposed algorithm is evaluated in the face of time varying frequency, time varying amplitude, non-sinusoidal periodic disturbance, and different plants. Also, the convergence rate of the method is compared by other estimation methods. Then, the online estimation is used in the repetitive controller structure for eliminating periodic disturbances with unknown frequencies. A new analysis is performed on RC and the stability conditions are determined in a theorem. These are illustrated in simulation results. The effectiveness of this method is shown on the application of ϕ -shaped PZT active suspension and it is compared by existing results. [ABSTRACT FROM AUTHOR]

This paper introduces the inertial amplifier viscoelastic tuned mass dampers (IAVTMD). The viscoelastic materials are implanted inside the core material of the inertial amplifier tuned mass dampers. The standard linear solid models are applied mathematically to formulate the viscoelastic material. H 2 and H ∞ optimization mechanisms apply to derive the exact mathematical closed-form formulations for optimal design parameters for novel inertial amplifier viscoelastic tuned mass dampers. The optimum IAVTMD installs at the top of the structures to mitigate the dynamic responses, determining the dynamic responses analytically through transfer function formation. At first, IAVTMD's dynamic response reduction capacity compares with the conventional tuned mass damper's (CTMD) dynamic response reduction capacity. As a result, H 2 optimized IAVTMD's dynamic response reduction capacity is significantly 20.87% and 26.47% superior to two well-established H 2 optimized conventional tuned mass damper's dynamic response reduction capacity. In addition, H ∞ optimized IAVTMD has 15.48% more dynamic response reduction capacity than H ∞ conventional tuned mass damper. H 2 and H ∞ optimized tuned mass damper inerters with optimal closed-form solutions are introduced in this paper. A higher damper mass ratio and a lower inerter mass ratio are recommended to produce H 2 and H ∞ optimized tuned mass damper inerter (TMDI) with a lower frequency and damping ratio in an affordable range. Accordingly, H 2 and H ∞ optimized IAVTMD's dynamic response reduction capacities are significantly 6.94% and 23.29% superior to H 2 and H ∞ optimized TMDI's dynamic response reduction capacity. The closed-form expressions for optimal design parameters of inertial amplifier viscoelastic tuned mass dampers and tuned mass damper inerters are mathematically correct and effective for practical applications. [ABSTRACT FROM AUTHOR]

Intelligent fault diagnosis utilizing deep learning algorithms is currently a topic of great interest. When developing a new Convolutional Neural Network (CNN) architecture to address machine diagnosis problem, it is common to use a deep model, with many layers, many feature maps, and large kernels. These models are capable of learning complex relationships and can potentially achieve superior performance on test data. However, not only does a large network potentially impose undue computational complexity for training and eventual deployment, it may also lead to more brittleness—where data outside of the curated dataset used in CNN training and evaluation is poorly handled. Accordingly, this paper will investigate a methodical approach for identifying a quasi-optimal CNN architecture to maximize robustness when a model is trained under one set of operating conditions, and deployed under a different set of conditions. Optuna software will be used to optimize a baseline CNN model for robustness to different rotational speeds and bearing Model #'s. To further improve the network generalization capabilities, this paper proposes the addition of white Gaussian noise to the raw vibration training data. Results indicate that the number of trainable weights and associated multiplications in the optimized model were reduced by almost 95% without jeopardizing the network classification accuracy. Additionally, moderate Additive White Gaussian Noise (AWGN) improved the model adaptation capabilities. [ABSTRACT FROM AUTHOR]

Automation is critically important for sustainability in meat production, where heavy reliance on human labour is a growing challenge. In this work, a novel robotic Meat Factory Cell (MFC) platform presents the opportunity for unconventional automation in pork meat processing, particularly abattoirs. Instead of following line-based approaches, which are the main option today, it uses robotics and Artificial Intelligence (AI) to perform complex cutting and manipulation operations on entire unchilled pork carcasses, with awareness of biological variation and deformation. The long-term goal of the MFC is to take a pork carcass as an input and produce seven primal outputs: hams, shoulders, saddle, belly and entire organ set. However, the MFC platform is under continuous development – therefore, this paper aims to demonstrate it through a specific use-case: shoulder removal. The system is evaluated based on data from testing and development sessions (June–November 2022), with a total of 34 attempted shoulder removals. Data regarding the MFCs' ability to handle variation, in addition to success rate and process timing models are presented. Qualitative feedback from skilled butchers is also discussed. The authors propose that, as well as technical development of the platform, it is important to consider new ways of comparing unconventional systems with their conventional counterparts. Innovative manufacturing systems have more to offer than raw speed and volume; traits such as flexibility, robustness and scalability – particularly economic scalability – should play a prominent role. Future legislation and standards must also encourage innovation rather than hinder innovative robotics solutions. [ABSTRACT FROM AUTHOR]

Motion planning can be cast as a trajectory optimisation problem where a cost is minimised as a function of the trajectory being generated. In complex environments with several obstacles and complicated geometry, this optimisation problem is usually difficult to solve and prone to local minima. However, recent advancements in computing hardware allow for parallel trajectory optimisation where multiple solutions are obtained simultaneously, each initialised from a different starting point. Unfortunately, without a strategy preventing two solutions to collapse on each other, naive parallel optimisation can suffer from mode collapse diminishing the efficiency of the approach and the likelihood of finding a global solution. In this paper, we leverage on recent advances in the theory of rough paths to devise an algorithm for parallel trajectory optimisation that promotes diversity over the range of solutions, therefore avoiding mode collapses and achieving better global properties. Our approach builds on path signatures and Hilbert space representations of trajectories and connects parallel variational inference for trajectory estimation with diversity-promoting kernels. We empirically demonstrate that this strategy achieves lower average costs than competing alternatives on a range of problems, from 2D navigation to robotic manipulators operating in cluttered environments. [ABSTRACT FROM AUTHOR]

High-quality observations of the real world are crucial for a variety of applications, including producing 3D printed replicas of small-scale scenes and conducting inspections of large-scale infrastructure. These 3D observations are commonly obtained by combining multiple sensor measurements from different views. Guiding the selection of suitable views is known as the Next Best View (NBV) planning problem. Most NBV approaches reason about measurements using rigid data structures (e.g., surface meshes or voxel grids). This simplifies next best view selection but can be computationally expensive, reduces real-world fidelity and couples the selection of a next best view with the final data processing. This paper presents the Surface Edge Explorer (SEE), a NBV approach that selects new observations directly from previous sensor measurements without requiring rigid data structures. SEE uses measurement density to propose next best views that increase coverage of insufficiently observed surfaces while avoiding potential occlusions. Statistical results from simulated experiments show that SEE can attain similar or better surface coverage with less observation time and travel distance than evaluated volumetric approaches on both small- and large-scale scenes. Real-world experiments demonstrate SEE autonomously observing a deer statue using a 3D sensor affixed to a robotic arm. [ABSTRACT FROM AUTHOR]

We present an algorithm that, given a representation of a road network in lane-level detail, computes a route that minimizes the expected cost to reach a given destination. In doing so, our algorithm allows us to solve for the complex trade-offs encountered when trying to decide not just which roads to follow, but also when to change between the lanes making up these roads, in order to—for example—reduce the likelihood of missing a left exit while not unnecessarily driving in the leftmost lane. This routing problem can naturally be formulated as a Markov Decision Process (MDP), in which lane change actions have stochastic outcomes. However, MDPs are known to be time-consuming to solve in general. In this paper, we show that—under reasonable assumptions—we can use a Dijkstra-like approach to solve this stochastic problem, and benefit from its efficient O (n log n) running time. This enables an autonomous vehicle to exhibit lane-selection behavior as it efficiently plans an optimal route to its destination. [ABSTRACT FROM AUTHOR]

The ability to accurately predict others' behavior is central to the safety and efficiency of robotic systems in interactive settings, such as human–robot interaction and multi-robot teaming tasks. Unfortunately, robots often lack access to key information on which these predictions may hinge, such as other agents' goals, attention, and willingness to cooperate. Dual control theory addresses this challenge by treating unknown parameters of a predictive model as stochastic hidden states and inferring their values at runtime using information gathered during system operation. While able to optimally and automatically trade off exploration and exploitation, dual control is computationally intractable for general interactive motion planning, mainly due to the fundamental coupling between the robot's trajectory plan and its prediction of other agents' intent. In this paper, we present a novel algorithmic approach to enable active uncertainty reduction for interactive motion planning based on the implicit dual control paradigm. Our approach relies on sampling-based approximation of stochastic dynamic programming, leading to a model predictive control problem that can be readily solved by real-time gradient-based optimization methods. The resulting policy is shown to preserve the dual control effect for a broad class of predictive models with both continuous and categorical uncertainty. To ensure the safe operation of the interacting agents, we use a runtime safety filter (also referred to as a "shielding" scheme), which overrides the robot's dual control policy with a safety fallback strategy when a safety-critical event is imminent. We then augment the dual control framework with an improved variant of the recently proposed shielding-aware robust planning scheme, which proactively balances the nominal planning performance with the risk of high-cost emergency maneuvers triggered by low-probability agent behaviors. We demonstrate the efficacy of our approach with both simulated driving studies and hardware experiments using 1/10 scale autonomous vehicles. [ABSTRACT FROM AUTHOR]

This paper addresses the lower limits of encoding and processing the information acquired through interactions between an internal system (robot algorithms or software) and an external system (robot body and its environment) in terms of action and observation histories. Both are modeled as transition systems. We want to know the weakest internal system that is sufficient for achieving passive (filtering) and active (planning) tasks. We introduce the notion of an information transition system (ITS) for the internal system which is a transition system over a space of information states that reflect a robot's or other observer's perspective based on limited sensing, memory, computation, and actuation. An ITS is viewed as a filter and a policy or plan is viewed as a function that labels the states of this ITS. Regardless of whether internal systems are obtained by learning algorithms, planning algorithms, or human insight, we want to know the limits of feasibility for given robot hardware and tasks. We establish, in a general setting, that minimal information transition systems (ITSs) exist up to reasonable equivalence assumptions, and are unique under some general conditions. We then apply the theory to generate new insights into several problems, including optimal sensor fusion/filtering, solving basic planning tasks, and finding minimal representations for modeling a system given input-output relations. [ABSTRACT FROM AUTHOR]

To tackle the active control problem of the heavy platform-struts structure, this paper designs a kind of hydraulic servo actuator that can withstand large static load and generate high-precision, small-amplitude dynamic forces as well, and develops a multi-channel cross-coupling suppression control algorithm that can consider the strong coupling effect in structure. Subsequently, combining the designed actuators with the control algorithm, this paper puts forward an active heavy platform-struts structure vibration control method. By embedding the designed actuators into the support struts, a platform-struts structure experimental system is constructed to assess the vibration suppression effect of the proposed method. Excited by the amplitude-varying disturbing force, four different kinds of experiments are conducted. The experimental results show that the distributed multi-channel adaptive active control method without considering the coupling effect will lead to the instability, and the vibration attenuation of the proposed method in this paper exceeds 90%. The proposed method therefore has an important potential in addressing the problem of vibration suppression in the practical engineering. In addition, it has strong convergence and robustness. [ABSTRACT FROM AUTHOR]

A call for papers on the special issue on human-robot interaction is presented.

In this paper, the scattering and vibration isolation of elastic waves from the open trench are analyzed and discussed in the time domain using the scaled boundary finite element method (SBFEM). Based on the theory of soil-structure interaction, the site with an open trench is decomposed into a near-field system and an infinite far-field system. The quadtree domain decomposition technique is employed for SBFEM mesh discretization of the near-field. The far-field is rigorously modeled by the displacement unit impulse response matrix. The obliquely plane elastic wave from the far-field is converted into a traction force acting on the near/far-field boundary. The time-space domain numerical model is established for elastic wave propagation in half-space with an open trench. Finally, through numerical examples to verify the effectiveness of the method of this paper, and parametric analyze the effect law of the incidence angle and the trench depth, etc., on the vibration isolation performance. [ABSTRACT FROM AUTHOR]

The paper investigates a nonlinear vibration mitigation strategy of a variable length pendulum subjected to a harmonic external excitation. A nonlinear absorber in a form of a tri-pendulum system is used to reduce the response of the primary pendulum. Thus, the paper investigates a non-stationary problem of nonlinear vibration mitigation of the primary pendulum using another nonlinear passive pendulum absorber. Due to genuine interest in capturing the nonlinear dynamic interaction, the paper numerically studies the performance of the primary mass and absorber, first, by constructing 2D maps in the unrestrained parametric space, which demonstrate the qualitative behavior of the system. Then, the surrogate optimization technique is used to tune the absorber's parameters within a given bounded set of parameters' values. The optimization is conducted based on a priori known reeling speed or acceleration/deceleration of the primary pendulum, thereby completely removing the need for acquiring a current system states essential for active feedback control. The obtained numerical results validate the proposed strategy and demonstrate high performance of the nonlinear passive absorber when it is properly tuned. [ABSTRACT FROM AUTHOR]

Detailed simulations of car and train platoons are critical steps towards successful implementations of the platooning technologies. This paper developed a simulation method that can be used for both car and train platoon simulations. The method includes three main parts: (1) a scalable adaptive longitudinal space controller, (2) 2D multibody dynamics simulations that consider wheel-rail contact (Polach model), tyre-road contact (Magic Formula), suspensions and in-train forces, and (3) a scalable parallel computing method that processes the dynamics simulation of each individual vehicle (one car or one carriage) by using one independent computer core. Two case studies were conducted to simulate a 6-car platoon and a 3-train platoon on real-world road and track. The maximum space errors for the car platoon and the train platoon results were about 0.32 and 0.23 m, respectively. When the platoons were running at a steady speed, space errors were well controlled within ±0.05 m for both car and train platoons. Example dynamics results were presented including wheel-rail contact forces, tyre-road contact forces, and carbody vibrations. This information can be used to conduct a wide range of related studies such as passenger ride comfort and system damage assessments. [ABSTRACT FROM AUTHOR]

In order to realize the wide frequency excitation and high accuracy control of a new type of large load-capacity seismic simulation shaking table (named large compound shaking table, LCST), it is necessary to study the in-depth mechanism, influence factors, and compensation algorithm of the LCST. In this paper, the mechanism model of LCST was established by transfer function matrix and the coupling characteristics were analyzed by Bristol method. Then, the three-variable control simulation analysis was carried out by Simulink. The specimen influence on the LCST was tested by setting different mass, frequencies, and damping ratios of the specimen. A series of simulation studies suggest that the control accuracy of LCST is most sensitive to the frequency of the specimen, and high natural frequency could even lead to system instability. For the sake of high-precision control, this paper proposed a feedforward decoupling compensation control (FDCC) strategy. Through numerical studies, the improved tracking of the FDCC strategy was demonstrated. In addition, the mature compensation methods in the conventional shaking table can be introduced reasonably into the control loop by FDCC strategy so that the LCST can more accurately replicate the characteristics of synthetic and historic earthquakes. [ABSTRACT FROM AUTHOR]

This paper focuses on acoustic modeling for active noise control applications. The desired model must be finite dimensional and efficient numerically to meet implementation requirements. It must also be parameterized by the position considered inside the cavity. This additional characteristic is desirable for addressing specific active noise control applications such as optimizing microphone placement, estimating acoustic pressure at different positions of the considered cavity, and finally attenuating noise level in a subarea of the cavity. The main contribution of this paper is to propose a finite-dimensional, low-order, and parameterized acoustic model of a cavity, suitable for active noise control applications. The resulting model is defined as a gray box that combines a one-dimensional analytical model of acoustic propagation, which handles the physical parameterization, and a black-box model that copes with actuator and sensor dynamics as well as modeling errors. The parameters of the proposed model are optimized in order to reproduce the frequency behavior of the real system (LS2N active noise control platform) in a frequency range. This model allows one to accurately reproduce the dynamics at any position in the considered cavity. The prediction performance of the proposed model was compared to a classical black-box model (usually used for active noise control applications) and validated experimentally using the LS2N active noise control platform. The analysis highlighted that the proposed gray-box model can predict the acoustic behavior in a great range of positions. [ABSTRACT FROM AUTHOR]

The particle damper has been widely used as an efficient passive vibration control device in recent years. The highly non-linear characteristic of the energy dissipation mechanism is essential for our increased understanding of non-obstructive particle damper (NOPD). To connect motion modes of the granular system and energy dissipation, we developed a neural network using simulated annealing backpropagation (SA-BP) to predict the loss factor of NOPD in this paper. The simulations based on the verified discrete element method (DEM) model are carried out, and the data is used to train and test the neural network. Based on the prediction of a well-trained neural network using SA-BP, the relationship between the loss factors and the rheology behaviors of the granular system is discussed. This paper effectively combines intelligent algorithms (SA-BP) and particle damping characteristics. The algorithm is expected to be further used as an auxiliary method for the experimental study of the granular system. [ABSTRACT FROM AUTHOR]

Biological systems, including human beings, have the innate ability to perform complex tasks in a versatile and agile manner. Researchers in sensorimotor control have aimed to comprehend and formally define this innate characteristic. The idea, supported by several experimental findings, that biological systems are able to combine and adapt basic units of motion into complex tasks finally leads to the formulation of the motor primitives' theory. In this respect, Dynamic Movement Primitives (DMPs) represent an elegant mathematical formulation of the motor primitives as stable dynamical systems and are well suited to generate motor commands for artificial systems like robots. In the last decades, DMPs have inspired researchers in different robotic fields including imitation and reinforcement learning, optimal control, physical interaction, and human–robot co-working, resulting in a considerable amount of published papers. The goal of this tutorial survey is two-fold. On one side, we present the existing DMP formulations in rigorous mathematical terms and discuss the advantages and limitations of each approach as well as practical implementation details. In the tutorial vein, we also search for existing implementations of presented approaches and release several others. On the other side, we provide a systematic and comprehensive review of existing literature and categorize state-of-the-art work on DMP. The paper concludes with a discussion on the limitations of DMPs and an outline of possible research directions. [ABSTRACT FROM AUTHOR]

The study of the bearing fatigue life in high-speed gearboxes is an integral part of high-speed gear transmission research. There is still a lack of research on the analysis and calculating the bearing internal dynamic load and fatigue life considering gear engagement and centrifugal force simultaneously. Therefore, the paper proposes a new approach to acquire the internal load and fatigue life of the deep groove ball bearing in a high-speed gearbox system. Firstly, the modeling method of the gear mesh, flexible shaft, high-speed deep groove ball bearing, and housing is established, constructing the gearbox system dynamic model, which has the ability to calculate the bearing internal load. Then, the calculation method for analyzing the fatigue life of bearings is given based on the linear damage theory. The results show that the gear engagement and centrifugal force have meaningful effects on bearing internal loads and fatigue life, especially at high speeds. Both of them should be considered when evaluating the bearing life. The calculation method and model proposed in this paper provide a new technical approach for service performance evaluation and reliability design of high-speed deep groove ball bearing. [ABSTRACT FROM AUTHOR]

This paper puts forward the robust control problem of rail vehicle virtual coupling platoon that is subject to nonlinear and time-varying uncertainties. The uncertainties are caused by external disturbances and parameter variations. To this end, this paper designs a robust controller based on the Udwadia-Kalaba (UK) equation. First, we introduce the concept of virtual coupling and establish the longitudinal dynamics model of the rail vehicle. In order to ensure the control objective of the virtual coupling process, the spatial constraint on position and bidirectional inequality constraints for the spacing error between the marshalling trains are established. After changing the bounded state into the unbounded state through the state transformation, the UK approach can be used. Then, the constraint-following robust controller design is based on the UK approach and Lyapunov stability theory. The controller can render the unbounded state eventual uniform boundedness and uniformultimate boundedness, which also satisfies the spatial constraint and the bidirectional restriction for the spacing error. Moreover, an optimized design scheme for the tunable parameter of this controller is proposed, which minimizes the control cost and comprehensive index of system performance. Finally, numerical simulations are given to illustrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Interruptions in turning make the process forces non-smooth and nonlinear. Smooth nonlinear cutting forces result in the process of being stable for small perturbations and unstable for larger ones. Re-entry after interruptions acts as perturbations making the process exhibit bistabilities. Stability for such processes is characterized by Hopf bifurcations resulting in lobes and period-doubling bifurcations resulting in narrow unstable lenses. Interrupted turning remains an important technological problem, and since experimentation to investigate and mitigate instabilities are difficult, this paper instead emulates these phenomena on a controlled hardware-in-the-loop simulator. Emulated cutting on the simulator confirms that bistabilities persist with lobes and lenses. Cutting in bistable regimes should be avoided due to conditional stability. Hence, we demonstrate the use of active damping to stabilize cutting with interruptions/perturbations. To stabilize cutting with small/large perturbations, we successfully implement an adaptive gain tuning scheme that adapts the gain to the level of interruption/perturbation. To facilitate real-time detection of instabilities and their control, we characterize the efficacy of the updating scheme for its dependence on the time required to update the gain and for its dependence on the levels of gain increments. We observe that higher gain increments with shorter updating times result in the process being stabilized quicker. Such results are instructive for active damping of real processes exhibiting conditional instabilities prone to perturbations. [ABSTRACT FROM AUTHOR]

This paper contributes to the literature and development of knowledge in the topic of energy harvesting by presenting the modelling and calculations of energy from vibration of railway tracks due to moving trains on floating-slab tracks with continuous slabs, considering both the quasi-static and dynamic effects. The floating-slab track is modelled as a double Euler–Bernoulli beam connected by continuous spring and damper elements. The dynamic excitation is accounted for by considering the un-sprung axles of a passing train with a number of coaches. The dynamic excitation is simulated using randomly generated unevenness from standard functions of power spectral density. The responses of rails' beam and slab are calculated for different unevenness realizations, and then used as inputs for a base-excited single-degree-of-freedom system that models the harvester. The change in the harvested energy is investigated due to the change of natural frequency of the harvester, the change of condition of track and change of train's velocity. The parameters used in this paper correspond to tracks and trains for Doha metro and unevenness information from the literature. The results show that more energy can be harvested by tuning the harvester's natural frequency to the frequency of axle-track resonance. It is found that a maximum mean-energy can be harvested from the rails of 0.35 J/kg for a train moving at 100 km/h for a track with poor condition and this is obtained at the axle-track resonance frequency. For the same track condition, a reduction of about 55% and 61% is observed for train's velocities of 70 km/h and 40 km/h, respectively. Using a track with medium and good conditions resulted in reduction of the mean harvested energy at the axle-track resonance by 73.5% and 99.9%, respectively. [ABSTRACT FROM AUTHOR]

Vision-based estimation of the motion of a moving target is usually formulated as a bearing-only estimation problem where the visual measurement is modeled as a bearing vector. Although the bearing-only approach has been studied for decades, a fundamental limitation of this approach is that it requires extra lateral motion of the observer to enhance the target's observability. Unfortunately, the extra lateral motion conflicts with the desired motion of the observer in many tasks. It is well-known that, once a target has been detected in an image, a bounding box that surrounds the target can be obtained. Surprisingly, this common visual measurement especially its size information has not been well explored up to now. In this paper, we propose a new bearing-angle approach to estimate the motion of a target by modeling its image bounding box as bearing-angle measurements. Both theoretical analysis and experimental results show that this approach can significantly enhance the observability without relying on additional lateral motion of the observer. The benefit of the bearing-angle approach comes with no additional cost because a bounding box is a standard output of object detection algorithms. The approach simply exploits the information that has not been fully exploited in the past. No additional sensing devices or special detection algorithms are required. [ABSTRACT FROM AUTHOR]