Discrete Motion Research Articles

Measurement method of flexible component pose based on discrete motion actuators

Abstract In order to address the limitations of traditional large discrete motion actuator mechanisms in realizing high-precision inter-axis relationship calibration in manufacturing environments, this paper proposes a new convolutional neural network-based attitude mapping estimation method, component pose using convolutional neural network (CPCNN). The method implicitly encodes the inter-axis relation matrix into the weight parameters of the training neural network, which results in a high degree of integration with existing large discrete motion actuator mechanisms. The CPCNN-based method directly obtains the attitude change of the adjustment cabin by reading the change of each axis of the current motion actuator mechanism in its own coordinate system. This method can overcome the limitations of the experimental process in the traditional calibration methods and improve the accuracy of attitude mapping under the influence of self-weight by selecting better motion parameters through redundant degrees of freedom. The application of this new method will provide an effective solution for the high-precision inter-axis relationship calibration of large discrete motion actuator mechanisms in manufacturing environments, offering new possibilities for improving production efficiency and product quality.

Prior coordination solutions shape motor learning and transfer in redundant tasks

Motor learning does not occur on a ‘blank slate’, but in the context of prior coordination solutions. The role of prior coordination solutions is likely critical in redundant tasks where there are multiple solutions to achieve the task goal – yet their influence on subsequent learning is currently not well understood. Here we addressed this issue by having human participants learn a redundant virtual shuffleboard task, where they held a bimanual manipulandum and made a discrete throwing motion to slide a virtual puck towards a target. The task was redundant because the distance traveled by the puck was determined by the sum of the left- and right-hand speeds at the time of release. On the first day, 37 participants in different groups practiced symmetric or asymmetric solutions. On the second day, all participants transferred to a common criterion task, which required an asymmetric solution. Results showed that: (i) the symmetry of the practiced solution affected motor variability during practice, with more asymmetric solutions showing higher exploration of the null space, (ii) when transferring to the common criterion task, participants in the symmetric group showed much higher null space exploration, and (iii) when no constraints were placed on the solution, participants tended to return to the symmetric solution regardless of the solution originally practiced. Overall, these results suggest that the stability of prior coordination solutions plays an important role in shaping learning in redundant motor tasks.

Research on Discrete Clamp Motion Path Control-Based Stretch-Forming Method for Large Surfaces

In this paper, a near-net discrete clamp motion path control (SF-CMPC)-based stretch-forming method is proposed as a solution for the low-cost high-quality machining of highly curved surfaces. In this approach, the clamps are discretized, the motion paths are designed to control deformation distribution and avoid forming defects, the stretch-forming transition zone can be effectively reduced, the material utilization rate can be increased, and the near-net formation of large surfaces can be achieved. To investigate this method’s feasibility, the conventional stretch-forming (SF-C) and SF-CMPC processes are numerically analyzed. The results indicate that, upon increasing the transition zone length via SF-CMPC, the maximum thickness reduction and strain value are reduced by 0.010 mm and 0.0249, respectively, with the dependence of the forming quality on the transition zone length being significantly reduced compared to SF-C. In the formation of surfaces with large curvatures, SF-CMPC’s crack risk is lower than SF-C’s crack risk, with better adaptability. Through controlling the contact process with a die, the sheet metals’ constraint state is improved, the transverse compressive strain can be effectively reduced via friction, and the wrinkling defects can be suppressed. A stretch-forming experiment was carried out on a spherical surface, using self-developed equipment. The feasibility of achieving surfaces’ near-net stretch forming by controlling the clamps’ motion paths was hereby proven.

Envelope vector solitons in nonlinear flexible mechanical metamaterials

In this paper, we employ a combination of analytical and numerical techniques to investigate the dynamics of lattice envelope vector soliton solutions propagating within a one-dimensional chain of flexible mechanical metamaterial. To model the system, we formulate discrete equations that describe the longitudinal and rotational displacements of each individual rigid unit mass using a lump element approach. By applying the multiple-scales method in the context of a semi-discrete approximation, we derive an effective nonlinear Schrödinger equation that characterizes the evolution of rotational and slowly varying envelope waves from the aforementioned discrete motion equations. We thus show that this flexible mechanical metamaterial chain supports envelope vector solitons where the rotational component has the form of either a bright or a dark soliton. In addition, due to nonlinear coupling, the longitudinal displacement displays kink-like profiles thus forming the 2-components vector soliton. These findings, which include specific vector envelope solutions, enrich our knowledge on the nonlinear wave solutions supported by flexible mechanical metamaterials and open new possibilities for the control of nonlinear waves and vibrations.

Finite–Discrete Element Method Simulation Study on Development of Water-Conducting Fractures in Fault-Bearing Roof under Repeated Mining of Extra-Thick Coal Seams

The formation of water-conducting fractures in overlying strata caused by underground coal mining not only leads to roof water inrush disasters, but also water-conducting fractures penetrate the aquifer, resulting in the occurrence of a mine-water-inrush disaster and the loss of water resources. It destroys the sustainability of surface water and underground aquifers. This phenomenon is particularly significant in extra-thick coal seams and fault-bearing areas. Numerical simulation is an effective method to predict the failure range of mining overburden rock with low cost and high efficiency. The key to its accuracy lies in a reasonable constitutive model and simulation program. In this study, considering that the three parts of penetrating cracks, non-penetrating cracks, and intact rock blocks are often formed after rock failure, the contact state criterion and shear friction relationship of discrete rock blocks and the mixed fracture displacement–damage–load relationship are established, respectively. Combined with the Mohr–Coulomb criterion, the constitutive model of mining rock mass deformation–discrete block motion and interaction is formed. On this basis, according to the engineering geological conditions of Yushupo Coal Mine, a numerical model for the development of water-conducting cracks in the roof with faults under repeated mining of extra-thick coal seams is established. The results show the following: The constitutive relation of the continuous deformation–discrete block interaction of overlying strata and the corresponding finite element–discrete element FDEM numerical program and VUSDFLD multi-coal seam continuous mining subroutine can numerically realize the formation process of faults and water flowing fractures in overlying strata under continuous mining of extra-thick multi-coal seams. The toughness of sand mudstone is low, and the fracture will be further developed under the repeated disturbance of multi-thick coal seam mining. Finally, it is stabilized at 216–226 m, and the ratio of fracture height to mining thickness is 14.1. When the working face advances to the fault, the stress concentration occurs in the fault and its overlying rock, which leads to the local fracture of the roof rock mass and the formation of cracks. The fault group makes this phenomenon more obvious. The results have been preliminarily applied and tested in Ningwu mining area, which provides theoretical support for further development of roof water disaster control under the condition of an extra-thick coal seam and avoids the loss of water resources in surface water and underground aquifers.

A cellular automaton model for mixed traffic flow considering the size of CAV platoon

In the environment of intelligent connected vehicles, the mixed traffic flow consisting of human-driven vehicles (HDVs), connected and automated vehicles (CAVs), and their platoons will coexist for a long time. Exploring and discovering the operational rules of mixed traffic flow is the foundation for its management and control. This study delves into the distinct characteristics of various vehicles within a mixed traffic environment, particularly focusing on HDVs, CAVs, and CAV platoons. Drawing on the psychological predisposition of HDVs to maintain a prudent following distance, we introduce a discrete motion safety distance model to establish the longitudinal movement rule for HDVs. Furthermore, a longitudinal movement rule for CAVs is established based on their ability to keep a constant headway using Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC), alongside meeting specific speed and spacing criteria for platoon movement. On this basis, the paper proposes a cellular automaton model for single-lane mixed traffic flow that accounts for CAV platoon length and the decision-time disparity between HDVs and CAVs. The simulation results show that an increased proportion of CAVs can enhance the traffic capacity, elevate average speed, extend average platoon length, and reduce the congestion ratio and CACC degradation rate. Notably, in scenarios where the CAV proportion p > 0.7, the flow-density graph shows a trapezoidal shape, with the majority of CAVs in the traffic flow synchronizing their movements in CACC mode. Even when vehicular density surpasses the optimum threshold, the traffic flow maintains high-speed operation until reaching a critical congestion point, whereupon velocities precipitously decline. Through sensitivity analysis aimed at understanding the impact of maximum platoon length, it has been observed that, for CAV proportions below 0.7, variations in maximum platoon length negligibly impact traffic capacity, velocity, CACC degradation, and congestion levels. Only in scenarios dominated by CAVs does extending platoon length restrictions significantly enhance performance. However, as the maximum platoon length increases, the gains in traffic capacity, speed, CACC degradation rate, and congestion ratio diminish. When the platoon length of CACC exceeds a certain threshold, there will not be a significant improvement in traffic capacity or speed.

Pseudorange reconstruction for high‐dynamic eLoran signal simulators using B‐spline interpolation

AbstractThe High‐Dynamic enhanced Loran (eLoran) signal simulator must accurately model the motion parameters and dynamic transmission delays of the carriers in the channel. A method was proposed based on B‐Spline interpolation, capable of arbitrary ratio interpolation and multi‐channel simulation. This method can regenerate carrier‐to‐transmitter distances from available discrete motion asynchronous data, it can effectively reproduce more motion details for carrier. The algorithm can be implemented using digital circuits, allowing for real‐time simulation on a hardware platform. The circuit structure is divided into integer and fractional calculations, which can satisfy the interpolation of asynchronous data with arbitrary ratios. Compared with existing methods, the approach simulates the motion parameters with satisfactory accuracy.

Articulated Motion-Aware NeRF for 3D Dynamic Appearance and Geometry Reconstruction by Implicit Motion States.

We propose a self-supervised approach for 3D dynamic reconstruction of articulated motions based on Generative Adversarial Networks and Neural Radiance Fields. Our method reconstructs articulated objects and recover their continuous motions and attributes from an unordered, discontinuous image set. Notably, we treat motion states as time-independent, recognizing that articulated objects can exhibit identical motions at different times. The key insight of our approach utilizes generative adversarial networks to create a continuous implicit motion state space. Initially, we employ a motion network extracts discrete motion states from images as anchors. These anchors are then expanded across the latent space using generative adversarial networks. Subsequently, motion state latent codes are input into motion-aware neural radiance fields for dynamic appearance and geometry reconstruction. To deduce motion attributes from the continuously generated motions, we adopt a cluster-based strategy. We thoroughly evaluate and validate our method on both synthesized and real data, demonstrating superior fidelity in appearances, geometries, and motion attributes of articulated objects compared to state-of-the-art methods.

Insights into post-emplacement lava flow dynamics at Mt. Etna volcano from 2016 to 2021 by synthetic aperture radar and multispectral satellite data

Insights into post-emplacement lava flow dynamics at Mt. Etna volcano from 2016 to 2021 by synthetic aperture radar and multispectral satellite data

Hyper-fidelity depletion with discrete motion for pebble bed reactors

Hyper-fidelity (HxF) depletion of pebble bed reactors (PBRs) is the capability to model depletion for every pebble while accounting for motion through the core. Previous HxF work demonstrated feasibility to deplete hundreds of thousands of stationary pebbles concurrently within reasonable timeframes. This work illustrates the second step towards HxF, coupling depletion with a discrete motion scheme. The model assumes an ordered bed with pebbles occupying fixed positions. Motion is simplified as discrete since pebbles move in straight lines from one set position to another. The methodology was implemented in Serpent 2, combined with its transport and depletion capabilities. Ad-hoc routines were developed ensuring compatibility with domain decomposition and pebble recirculation after each pass based on discharge criteria and fresh pebble insertion. Capabilities of HxF with discrete motion are demonstrated using a full-scale high-temperature gas-cooled reactor model. Specifically, an approach to equilibrium is performed, and example results are shown for in-core and discarded pebbles. The data illustrates how HxF provides unique insights into PBR fuel, producing information on statistical distributions rather than average values only, as obtained by traditional methods that rely on spectral zoning for depletion. Knowledge of these distributions can greatly improve analysis and assessment of PBRs.

Dynamic Multiaction Recognition and Expert Movement Mapping for Closed Pelvic Reduction

Pelvic fractures are one of the most serious traumas in orthopedic care, and reduction during routine surgery is a significant challenge. Because there are so many vital organs, blood vessels, and nerves around the pelvis, and the reduction force is large, the operational requirements for the surgeon are extremely strict and require extensive experience and surgical skills. This paper proposes a method for collecting and digitizing doctors' reduction movements, which aims to help intelligent devices recognize surgeons' reduction actions and provides a means to learn from expert experience to improve the accuracy of surgery. First, the Conv-BiLSTM algorithm with multilayer cross-fused features is proposed. It extracts time and spatial correlations between multimodal data in a hierarchical manner. Second, discrete dynamic motion primitives (DMPs) are adopted for mapping the surgeon's palm movement trajectory. Finally, this paper constructs a data acquisition platform and collects data from surgeons with varying proficiency in closed reduction. Experiment results show that the closed reduction action recognition accuracy is 99% and posture recognition accuracy is 95.5%. The recognition algorithm proposed by this paper is significantly higher than the commonly used algorithms in terms of Accuracy, Precision, Recall, and F1-Score. This work provides methods and means for the digitization of surgical expertise and transfers learning for robot-assisted surgery.

A Review of Myoelectric Control for Prosthetic Hand Manipulation.

Myoelectric control for prosthetic hands is an important topic in the field of rehabilitation. Intuitive and intelligent myoelectric control can help amputees to regain upper limb function. However, current research efforts are primarily focused on developing rich myoelectric classifiers and biomimetic control methods, limiting prosthetic hand manipulation to simple grasping and releasing tasks, while rarely exploring complex daily tasks. In this article, we conduct a systematic review of recent achievements in two areas, namely, intention recognition research and control strategy research. Specifically, we focus on advanced methods for motion intention types, discrete motion classification, continuous motion estimation, unidirectional control, feedback control, and shared control. In addition, based on the above review, we analyze the challenges and opportunities for research directions of functionality-augmented prosthetic hands and user burden reduction, which can help overcome the limitations of current myoelectric control research and provide development prospects for future research.

Restitution coefficient of various particles based on acoustic technology

The restitution coefficient is used to characterize the energy loss in particle impact, which is an important parameter for discrete element simulation and mechanical system motion analysis. However, the restitution coefficient of many materials needs to be determined, and the key material properties affecting the restitution coefficient are still unclear. In this paper, the acoustic frequency sampling method was used to study the restitution coefficients of 10 representative particles. The effects of material density and specific strength on the restitution coefficient and the relationship between the restitution coefficient and the damping coefficient were investigated. The results show that the particle restitution coefficient is inversely proportional to the density except for aluminium. The relationship between particle restitution coefficient and specific strength is not significant.

Taking Locomotion Mode as Prior: One Algorithm-Enabled Gait Events and Kinematics Prediction on Various Terrains

Both the discrete motion states and continuous joint kinematics are essential for controlling assistive robots under changeable environmental conditions. However, few studies investigate both the discrete and continuous motion intents. This paper is the first work to propose an end-to-end motion intent decoding method that integrates recognition of discrete locomotion modes and prediction of continuous joint kinematics and gait events. First, we propose a data-driven approach to segment the transitional periods of the adjacent locomotion modes and determine their boundaries. Second, we build a CNN-LSTM network that utilizes locomotion mode classification as the prior of joint kinematics and gait events prediction and thus decodes discrete and continuous motion intents. Finally, we evaluate our method through extensive experiments and comparisons. The proposed method for locomotion mode recognition achieves an accuracy of 98.73% for steady-state and 97.53% for transitional periods. For knee angle prediction, the method achieves the ahead-of-time prediction with NRMSE lower than 8.41%, R-value higher than 0.93, and R-square higher than 0.88 for various prediction time. For gait event prediction, it predicts the future timing of events with an error of 8.46 ms for initial contact, 32.76 ms for heel off, and 8.91 ms for toe off. This study reveals the feasibility of predicting locomotion-based kinematics and gait events on various terrains. The rich motion intents studied in this work highlight the potential of a more detailed lower-limb rehabilitation system.

Теоретическое исследование кривизны обработанной поверхности при косоугольном фрезеровании сборными фрезами

Introduction. The paper discusses the methods of processing large parts having curved convex surfaces with a rectilinear guide on multi-coordinate CNC machining centers using the touch method with a discrete motion of the tool feed along the profile of the part. It is shown that the main disadvantages of this method are lower productivity, which is due to the presence of discrete tool motions between cycles of its translation mode, where the value of discrete tool motion for a given processing accuracy depends on the curvature of the surface being processed. To improve processing performance, it is proposed to use prefabricated disc cutters equipped with replaceable polyhedral inserts (RPI) with rectilinear cutting edges. Its installation in the cutter body with non-zero angles of inclination of the main cutting edge, in combination with an additional rotation of the cutter, during processing, along the direction of the translational feed movement, allows you to obtain a concave surface and ensure a tighter fit of the producing surface of the tool and the machined surface of the part. The aim of the work is to reduce the error of approximation of the profile when it is processed using the touch method with discrete motion of prefabricated disc cutters along the profile and, consequently, to ensure workpiece the possibility of increasing the step of tool movement along the profile being formed to improve processing performance. Research methods: geometrical theory of designing metal-cutting tools. Results and discussion. The regularities established in the work made it possible to create a method for determining the angle of inclination of the main cutting edge of the RPI milling cutter and the angles of rotation of the milling cutter along the direction of translational feed movement during line-by-line processing of extended sections of parts with a curved profile on multi-coordinate CNC machines by turning the milling cutter to ensure the best fit of its producing surface to the surface being processed at the point of its contact, to reduce the approximation error processed profile and improve processing performance, due to the possibility of increasing the tool movement step.

Investigation of Inlet Conditions in The Mixing Process of Nanoparticles and Blood in a T-Shaped Microfluidic Reactor with Small Rectangular Cavities.

During the metastasis of cancer cells, circulating tumor cells (CTCs) are released from the primary tumor, reach the bloodstream, and colonize new organs. A potential reduction of metastasis may be accomplished through the use of nanoparticles in micromixers in order to capture the CTCs that circulates in blood. In the present study, the effective mixing of nanoparticles and the blood that carries the CTCs are investigated. The mixing procedure was studied under various inlet velocity ratios and several T-shaped micromixer geometries with rectangular cavities by using computational fluid dynamics techniques. The Navier-Stokes equations were solved for the blood flow; the discrete motion of particles is evaluated by a Lagrangian method while the diffusion of blood substances is studied by using a scalar transport equation. Results showed that as the velocity ratio between the inlet streams increases, the mixing rate of nanoparticles with the blood flow is increased. Moreover, nanoparticles are uniformly distributed across the mixing channel while their concentration is decreased along the channel. Furthermore, the evolution in time of the blood substances in the mixing channel increases with the increase of the velocity ratio between the two streams. On the other hand, the concentration of both the blood substances and the nanoparticles is decreased in the mixing channel as the velocity ratio increases. Finally, the differences in the dimensions of the rectangular cavities seems to have an insignificant effect both in the evolution in time of the blood substances and the concentration of nanoparticles in the mixing channel.

Magnetically Driven Modular Mechanical Metamaterials with High Programmability, Reconfigurability, and Multiple Applications

Shape transformation and motion guidance are emerging research hotspots of mechanical metamaterials. In this case, the key issue is how to improve the programmability and reconfigurability of metamaterials. The magnetically driven method enables materials to accomplish remote, fast, and reversible deformation, so it is desired for improving the programmability and reconfigurability of metamaterials. However, conventional magnetically driven materials are often pure elastomer materials. Their magnetic programming method is single, and their overall shape is unchangeable after fabrication, which limits their programmability and reconfigurability. Herein, this article proposes a kind of magnetically driven, programmable, and reconfigurable modular mechanical metamaterial based on origami and kirigami design mechanisms. The motion and deformation were designed to follow the predefined creases and incisions that could be transformed into each other. This metamaterial enabled more discrete motion and force transmission and integrated the fold of origami, the rotation of kirigami, and the fold guided by cuts. Such designs laid the foundation for complex, three-dimensional structures which could be quickly reassembled and constructed to deal with complex situations. This paper also demonstrated applications of this metamaterial in information storage and manifestation, mechanical logic computing, reconfigurable robotics, deployable mechanisms, and so on. The results indicated that the high programmability and reconfigurability expanded the application potential of the metamaterial for broader needs.

STRAIN-RATE WEBER NUMBER AS A LOCAL ATOMIZATION CONDITION IN COMPUTATIONAL PROTOCOL FOR SPRAY FLOW SIMULATIONS

Computational simulations of spray flows typically start with bulk liquid flow, bulk-to-droplet conversion algorithm for primary atomization, then tracking of discrete particle motion. The key step is the atomization criterion and subsequent drop size conversion. To facilitate this process, we consider the Weber number, based on strain rate (We<sub><i>st</i></sub>), as the local atomization condition in computational simulations of spray flows. This atomization criterion is tested within the computational protocol developed in this laboratory, which uses the integral theory as the primary atomization algorithm. Based on this definition, We<sub><i>st</i></sub> ~ 10<sup>7</sup> appears to work quite well in specifying the location of primary atomization, across different spray geometries. Therefore, the conservation equations of mass and energy in integral forms can be effectively coupled with the CFD-based momentum solver to simulate spray flows, by using the current atomization criterion.

CONCEPTUAL DESIGN AND BIO-INSPIRED CONTROL OF A NEW SURGICAL SOFT ROBOTIC GRIPPER

This paper describes the design and development of a novel soft robotic gripper for minimally invasive surgery (MIS) intended to remove foreign bodies from the patient's body by imitating human esophageal swallowing motions. The robotic gripper operates as follows: after locating and contacting the foreign body, the last segment of the gripper is expanding or contracting to match the size and catch the targeted object, then pushes forward, or bends it with its legs and starts to remove the object by a rhythmic peristaltic (periodically repeated) motion. This mode of the gripper’s operation allows removing bodies of different sizes from the human body without damaging the surrounding tissues, especially the blood vessels. Both the segments and legs of the gripper are made of dielectric elastomer actuators (DEAs) capable of large deformations under the influence of an external electric field (EEF). A central pattern generator (CPG)- based controller and a Rowat-Selverston type oscillator are selected to control both discrete and rhythmic motions based on electrical voltage – equivalent elastic strain relations of the orthotropic silicone elastomer obtained by the finite element analysis (FEA). The robotic gripper is modelled and studied by the ANSYS Workbench and Matlab/Simulink software. The performed computer modeling shows that due to the simple modular structure and CPG controller, the proposed robotic gripper is much faster, more adaptive and shows better response compared with its pneumatic actuated analogues.

SEMG-Based Continuous Hand Action Prediction by Using Key State Transition and Model Pruning.

Conventional classification of hand motions and continuous joint angle estimation based on sEMG have been widely studied in recent years. The classification task focuses on discrete motion recognition and shows poor real-time performance, while continuous joint angle estimation evaluates the real-time joint angles by the continuity of the limb. Few researchers have investigated continuous hand action prediction based on hand motion continuity. In our study, we propose the key state transition as a condition for continuous hand action prediction and simulate the prediction process using a sliding window with long-term memory. Firstly, the key state modeled by GMM-HMMs is set as the condition. Then, the sliding window is used to dynamically look for the key state transition. The prediction results are given while finding the key state transition. To extend continuous multigesture action prediction, we use model pruning to improve reusability. Eight subjects participated in the experiment, and the results show that the average accuracy of continuous two-hand actions is 97% with a 70 ms time delay, which is better than LSTM (94.15%, 308 ms) and GRU (93.83%, 300 ms). In supplementary experiments with continuous four-hand actions, over 85% prediction accuracy is achieved with an average time delay of 90 ms.