Motion In Space Research Articles

A pristine view of galactic globular clusters and their peripheries: Omega centauri

Abstract The central regions of the globular cluster Omega Centauri (ω Cen) have been extensively studied, but its outer regions and tidal structure have been less so. Gaia’s astrometry uncovered substantial tidal substructure associated with ω Cen, yet the lack of chemical tagging makes these associations tenuous. In this paper, we utilise the Gaia-Synthetic CaHK-band photometry, metallicities from the Pristine survey and Gaia’s astrometry to explore up to a clustercentric radius of 5 degrees from ω Cen. We identify ω Cen-like stars based on proper motion, colour-magnitude and colour-colour space, exploring the morphology, and stellar populations of the outer regions. Our probabilistic approach recovers the tidal tails of ω Cen, and we investigate the metallicity distribution of ω Cen ranging from a radius of 15 arcmin to the tidal radius, and beyond into the tidal tails. We present (1) two components between 15 arcmin and tidal radius at -1.83 and 1.45 dex which are also the dominant populations within 15 arcmin, and (2) the first evidence that the same two populations in the outer regions of the cluster are present outside the tidal radius and into the tidal tails. These populations are mixed about the stream, and are typically amongst the faintest stars in our sample; all indicating that the tidal tails are made of tidally stripped ω Cen stars.

A framework for demonstrating, forecasting, and explaining topic evolution by analyzing geometrical motion of topic embeddings

Abstract Topic analysis aims to study topic evolution and trends in order to help researchers understand the process of knowledge evolution and creation. This paper develops a novel topic evolution analysis framework, which we use to demonstrate, forecast, and explain topic evolution from the perspective of the geometrical motion of topic embeddings generated by pretrained language models. Our data set comprises approximately 15 million papers in the computer science field, with 7,000 “fields of study” to represent the topics. First, we demonstrate that over 80% of topics have undergone obvious motion in the semantic vector space, based on the hyperplane and its normal vector generated by a support vector machine. Subsequently, we verified the predictability of the motion based on three vector regression models by predicting topic embeddings. Finally, we employed a decoder to explain the predicted motion, whose forecast embeddings can capture about 50% of unseen topics. Our research framework shows that topic evolution can be analyzed via the geometrical motion of topic embeddings, and the semantic motion of old topics nurtures new topics. The current study opens new research pathways in topic analysis and sheds light on the topic evolution mechanism from a novel geometric perspective.

BPT‐Planner: Continuous Behavior Perception and Robot Trajectory Planner in Pathological Collaboration Experiments

Current research in human‐robot collaboration has yielded promising results, enabling us to predict human motion intentions and facilitate safe robot movement in shared spaces, avoiding collisions. However, achieving truly efficient human‐robot collaboration remains a complex and challenging endeavor. It entails not only imbuing robots with an understanding of human behavior but also empowering them to actively cooperate with human subjects and accomplish specific tasks. Against this backdrop, this paper introduces a comprehensive behavior perception and robot trajectory planner (BPT‐Planner). Leveraging continuous image data as input, the planner utilizes a CNN‐RNN network to predict human subjects' behavioral intentions, thereby gaining deeper insights. Based on these predictive outcomes and human‐robot action pairings, the planner generates a motion space for the robot. Subsequently, through the application of the proximal policy optimization algorithm, the planner dynamically optimizes the robot's action strategies to generate optimal motion trajectories. This optimization process takes into full consideration the behavior of human subjects, ensuring seamless integration of robot movement with human actions. To validate the performance of the BPT‐Planner, we conducted offline training and real‐time online validation in a pathological experiment scenario. Additionally, we conducted comparative experiments using datasets from other scenarios to further corroborate the planner's efficacy. Through the endeavors of this study, we not only showcase the exceptional performance of the BPT‐Planner across diverse scenarios but also affirm its applicability and effectiveness in various application environments. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

A singular theory of sensorimotor coordination: On targeted motions in space.

Gravity has long been purported to serve a unique role in sensorimotor coordination, but the specific mechanisms underlying gravity-based visuomotor realignment remain elusive. In this study, astronauts (9 males, 2 females) performed targeted hand movements with eyes open or closed, both on the ground and in weightlessness. Measurements revealed systematic drift in hand-path orientation seen only when eyes were closed and only in very specific conditions with respect to gravity. In weightlessness, drift in path orientation was observed in two postures (seated, supine) for two different movement axes (longitudinal, sagittal); on Earth, such drift was only observed during longitudinal (horizontal) movements performed in the supine posture. In addition to providing clear evidence that gravitational cues play a fundamental role in sensorimotor coordination, these unique observations lead us to propose an "inverted pendulum" hypothesis to explain the saliency of the gravity vector for eye-hand coordination - and why eye-hand coordination is altered during body tilt or in weightlessness.Significance statement In an experiment performed with astronauts, we made an unexpected observation that bears upon the fundamental question of gravity's role in aligning visuomotor reference frames. Measurements of targeted motions performed on the ground and in weightlessness revealed systematic drift in path orientation seen only in very specific conditions. These unique observations lead us to propose an "inverted pendulum" hypothesis to explain the saliency of the gravity vector for sensorimotor coordination.

Entropy analysis of convective nanofluid flow with Brownian motion in an annular space between confocal elliptic cylinders

Purpose This study aims to provide an in-depth analysis of entropy generation (EG) during natural convection within the annular space between confocal elliptic cylinders, with a specific focus on the influence of Brownian motion on nanofluid behavior. Design/methodology/approach A finite volume control method was used to conduct a detailed numerical analysis, examining the behavior of various nanofluids across a range of volume concentrations (2%–6%) and Rayleigh numbers. The study explores heat transfer (HT) and fluid flow mechanisms, particularly highlighting the role of nanoparticle Brownian motion in enhancing thermal conductivity. Findings The findings reveal that increased Rayleigh numbers significantly improve HT rates, while at lower Rayleigh values, EG is primarily governed by thermodynamic irreversibility. At higher Rayleigh numbers, this irreversibility plays a less dominant role in overall entropy production. Originality/value This study offers a novel perspective on the interplay between Rayleigh numbers, Brownian motion and EG, providing valuable insights for optimizing HT processes in engineering applications involving nanofluids.

Doppler Effects of Nonlinear Sensor Motion in 3D Space: Curvature, Torsion, Jolts, and Directional Wave Propagation

Doppler Effects of Nonlinear Sensor Motion in 3D Space: Curvature, Torsion, Jolts, and Directional Wave Propagation

Exploring the basic concepts of Calculus through a case study on motion in gravitational space

In universities, the Calculus course presents significant challenges year after year. In this article, we will demonstrate how to use methods of Realistic Mathematics Education (RME) to introduce the concepts of limits, differentiation, and integration based on high school kinematics and dynamics knowledge. All mathematical concepts are coherently built upon experiences, experiments, and fundamental dynamics knowledge related to motion in a gravitational field. With the help of worksheets created using GeoGebra or Microsoft Excel, students can conduct digital experiments and later independently visualize and relate abstract concepts to practical applications, thereby facilitating their understanding. Subject Classification: 97D40, 97I40, 97M50

New Metrics and Dataset for Biological Development Video Generation

Image generative models have advanced in many areas to produce synthetic images of high resolution and detail. This success has enabled its use in the biomedical field, paving the way for the generation of videos showing the biological evolution of its content. Despite the power of generative video models, their use has not yet extended to time-based development, focusing almost exclusively on generating motion in space. This situation is largely due to the lack of specific datasets and metrics to measure the individual quality of videos, particularly when there is no ground truth available for comparison. We propose a new dataset, called GoldenDOT , which tracks the evolution of apples cut in parallel over 10 days, allowing to observe their progress over time while remaining static. In addition, four new metrics are proposed that provide different analyses of the generated videos as a whole and individually. In this article, the proposed dataset and measures are used to study three state-of-the-art video generative models and their feasibility for video generation with biological development: Temporal GAN version 2 (TGANv2), Low-Dimensional Video Discriminator Generative Adversarial Network (LDVDGAN), and Video Diffusion Model (VDM). Among them, the TGANv2 model has managed to obtain the best results in most metrics, including those already known in the state of the art, demonstrating the viability of the new proposed metrics and their congruence with these standard measures.

Faster Sampling in Molecular Dynamics Simulations with TIP3P-F Water.

The need for short time steps currently limits routine atomistic molecular dynamics (MD) simulations to the microsecond time scale. For long time steps, the numerical integration of the equations of motion becomes unstable, resulting in catastrophic crashes. Here, we combine mass repartitioning and rescaling to construct a water model that increases the sampling efficiency in biomolecular simulations without compromising integration stability and with preserved structural and thermodynamic properties. The resulting "fast water" is then used with a time step as before in combination with standard force fields. The reduced water viscosity and faster diffusion result in proportionally faster sampling of the larger-scale motions in the conformation space of both solute and solvent. We illustrate this approach by developing TIP3P-F based on the popular TIP3P model of water. A roughly 2-fold boost in the sampling efficiency at minimal cost in accuracy is substantial and helps lower the energy impact of large-scale MD simulations. The approach is general and can readily be applied to other water models and different types of solvents.

Structural Design and Kinematic Analysis of Cable-Driven Soft Robot

Continuous robots have attracted more and more attention from the robotics community due to their high degree of flexibility and pliability, and have shown great potential for application in a variety of fields. With the continuous progress of material science, control technology, and artificial intelligence, the performance and application range of soft robotics have been further expanded, in which the cable drive has the advantages of large workspace, high flexibility, etc. The cable-driven soft robotic arm serves as an ultra-redundant robot that can operate in cramped and confined environments. In this paper, a cable-driven soft robot based on soft continuums and a cross gimbal is presented. The kinematics of the cable-driven soft robot is modeled and the mapping relations of the kinematics are solved by the D–H method and piecewise constant curvature, and the relations between the cable length, joint angle, and pose are further derived. Finally, the motion space of the cable-driven soft robot in the three-dimensional coordinate system is obtained by MATLAB2021b, and the single-segment soft body is simulated and analyzed using ADAMS to compare the theoretical data with the actual data and verify the reliability of this structure and method.

HiSC4D: Human-Centered Interaction and 4D Scene Capture in Large-Scale Space Using Wearable IMUs and LiDAR.

We introduce HiSC4D, a novel Human-centered interaction and 4D Scene Capture method, aimed at accurately and efficiently creating a dynamic digital world, containing large-scale indoor-outdoor scenes, diverse human motions, rich human-human interactions, and human-environment interactions. By utilizing body-mounted IMUs and a head-mounted LiDAR, HiSC4D can capture egocentric human motions in unconstrained space without the need for external devices and pre-built maps. This affords great flexibility and accessibility for human-centered interaction and 4D scene capturing in various environments. Taking into account that IMUs can capture human spatially unrestricted poses but are prone to drifting for long-period using, and while LiDAR is stable for global localization but rough for local positions and orientations, HiSC4D employs a joint optimization method, harmonizing all sensors and utilizing environment cues, yielding promising results for long-term capture in large scenes. To promote research of egocentric human interaction in large scenes and facilitate downstream tasks, we also present a dataset, containing 8 sequences in 4 large scenes (200 to 5,000 [Formula: see text]), providing 36 k frames of accurate 4D human motions with SMPL annotations and dynamic scenes, 31k frames of cropped human point clouds, and scene mesh of the environment. A variety of scenarios, such as the basketball gym and commercial street, alongside challenging human motions, such as daily greeting, one-on-one basketball playing, and tour guiding, demonstrate the effectiveness and the generalization ability of HiSC4D. The dataset and code will be publicly available for research purposes.

Multi-Degree-of-Freedom Platforms: Development and Outlook

Background: Multi-degree-of-freedom platforms are frequently employed in space docking devices, motion modeling, and robotics. Complex research is being done on multidegree- of-freedom platforms, including work on spatial route creation, control simulation, and forward and backward platform motion solutions. The development of research platforms in their current state is advantageous for advancing multi-degree-of-freedom motion research, which has the potential to advance numerous sectors through new technology. Objective: In this patent study, the benefits and drawbacks of various platforms are explored to analyze the development trend of platforms for academics and engineers through the most recent development of multi-degree-of-freedom platforms. Methods: By using the research methodology, research substance, and inventive structure of the most recent representative patents of the multi-degree-of-freedom platform, the platform's operating principle and characteristics are demonstrated. Results: By contrasting multi-degree-of-freedom platforms in various application domains, the issues with current multi-degree-of-freedom platforms are enumerated, and the platforms' potential future development path and research topics are suggested. Conclusion: The growth of the aviation, medical, and military industries is aided by the development of multi-degree-of-freedom platforms, and the flexible distribution of high-precision parallel platforms has promising future development.

Research on the Cutting Control Method of a Shield-Type Cutting Robot

With problems of low cutting efficiency, poor forming quality, and low control accuracy in large-section semi-coal rock roadway tunneling, comprehensive coal tunneling seriously lags behind fully mechanized mining. A fuzzy PID control method was proposed, taking the cutting robot in a shield-type tunneling system as the research object. Using mathematical analysis and other methods, a kinematic model of the cutting robot was established, and the pose transformation matrix of the cutting robot during the cutting process was obtained. A limit model for the motion space of the cutting robot cutting drum was established, and the motion relationship between the driving cylinder and the cutting drum was obtained. A fuzzy PID cutting robot control scheme was designed. The feasibility of the model was validated by simulation methods, and the correctness of the model and simulation was verified through on-site experiments. The results indicate that the established cutting robot model is correct, and the proposed fuzzy PID control method for the cutting robot is feasible. The maximum error of the center control of the cutting drum was 5.5 mm, and the average date was 0.89 mm, which was far less than the standard of 50 mm for engineering of the cross-section cutting of the roadway. This study enriches the control theory of shield-type cutting robots by improving control accuracy, enhancing the cutting efficiency of large-section semi-coal rock tunnels, ensuring the quality of section forming, and narrowing the gap between comprehensive coal tunneling and fully mechanized mining.

Hidden-like Attractors in a Class of Discontinuous Dynamical Systems

In continuous dynamical systems, a hidden attractor occurs when its basin of attraction does not connect with small neighborhoods of equilibria. This research aims to investigate the presence of hidden-like attractors in a class of discontinuous systems that lack equilibria. The nature of non-smoothness in Filippov systems is critical for producing a wide variety of interesting dynamical behaviors and abrupt transient responses to dynamic processes. To show the effects of non-smoothness on dynamic behaviors, we provide a simple discontinuous system made of linear subsystems with no equilibria. The explicit closed-form solutions for each subsystem have been derived, and the generalized Poincaré maps have been established. Our results show that the periodic orbit can be completely established within a sliding region. We then carry out a mathematical investigation of hidden-like attractors that exhibit sliding-mode characteristics, particularly those associated with grazing-sliding behaviors. The proposed system evolves by adding a nonlinear function to one of the vector fields while still preserving the condition that equilibrium points do not exist in the whole system. The results of the linear system are useful for investigating the hidden-like attractors of flow behavior across a sliding surface in a nonlinear system using numerical simulation. The discontinuous behaviors are depicted as motion in a phase space governed by various hidden attractors, such as period doubling, period-m segments, and chaotic behavior, with varying interactions with the sliding mode.

Drivable path detection for a mobile robot with differential drive using a deep Learning based segmentation method for indoor navigation.

The integration of artificial intelligence into the field of robotics enables robots to perform their tasks more meaningfully. In particular, deep-learning methods contribute significantly to robots becoming intelligent cybernetic systems. The effective use of deep-learning mobile cyber-physical systems has enabled mobile robots to become more intelligent. This effective use of deep learning can also help mobile robots determine a safe path. The drivable pathfinding problem involves a mobile robot finding the path to a target in a challenging environment with obstacles. In this paper, a semantic-segmentation-based drivable path detection method is presented for use in the indoor navigation of mobile robots. The proposed method uses a perspective transformation strategy based on transforming high-accuracy segmented images into real-world space. This transformation enables the motion space to be divided into grids, based on the image perceived in a real-world space. A grid-based RRT* navigation strategy was developed that uses images divided into grids to enable the mobile robot to avoid obstacles and meet the optimal path requirements. Smoothing was performed to improve the path planning of the grid-based RRT* and avoid unnecessary turning angles of the mobile robot. Thus, the mobile robot could reach the target in an optimum manner in the drivable area determined by segmentation. Deeplabv3+ and ResNet50 backbone architecture with superior segmentation ability are proposed for accurate determination of drivable path. Gaussian filter was used to reduce the noise caused by segmentation. In addition, multi-otsu thresholding was used to improve the masked images in multiple classes. The segmentation model and backbone architecture were compared in terms of their performance using different methods. DeepLabv3+ and ResNet50 backbone architectures outperformed the other compared methods by 0.21%-4.18% on many metrics. In addition, a mobile robot design is presented to test the proposed drivable path determination method. This design validates the proposed method by using different scenarios in an indoor environment.

MpcMech: Multi-Point Conjugation Mechanisms

A mechanism is an assembly of moving parts interconnected by joints to transfer an input motion to a desired output motion. Traditionally, to generate a complex motion, mechanisms are modeled by selecting and combining a number of mechanical parts with simple shapes such as links, gears, and cams. Combining multiple mechanical parts results in a mechanism with an intricate topology, which not only complicates assembly and maintenance but also deteriorates the functionality of generating motions due to accumulation of manufacturing imprecisions. To get rid of these limitations, we study mechanisms with a single pair of moving parts for generating complex motions. We model the pair of moving parts as a pair of conjugate surfaces with multiple conjugation points, forming a multi-point conjugation mechanism. To study this new mechanism, we establish a connection between conjugate surface pairs and form-closure grasps to formulate a dynamic form closure condition under which one conjugate surface is able to continuously transfer the motion to the other conjugate surface by utilizing multiple conjugation points. Guided by the condition, we propose an optimization-based approach to model the geometry of a multi-point conjugation mechanism for exactly generating a user-specified motion, in 1-, 2-, or 3-DOF motion space. The core of our approach is to model multiple conjugate curve pairs that satisfy various requirements in multi-point conjugation, dynamic form closure, and surface fabricability. We demonstrate the effectiveness of our approach by modeling different classes of multi-point conjugation mechanisms to generate various motions, evaluating the mechanisms' kinematic performance with 3D printed prototypes, and presenting three applications of these mechanisms.

Dynamical phase transitions in the XY model: A Monte Carlo and mean-field-theory study.

We investigate the dynamical phases and phase transitions arising in a classical two-dimensional anisotropic XY model under the influence of a periodically driven temporal external magnetic field in the form of a symmetric square wave. We use a combination of finite temperature classical Monte Carlo simulation, implemented within a CPU+GPU paradigm, utilizing local dynamics provided by the Glauber algorithm and a phenomenological equation-of-motion approach based on relaxational dynamics governed by the time-dependent free energy within a mean-field approximation to study the model. We investigate several parameter regimes of the variables (magnetic field, anisotropy, and the external drive frequency) that influence the anisotropic XY system. We identify four possible dynamical phases: Ising-SBO, Ising-SRO, XY-SBO, and XY-SRO. Both techniques indicate that only three of them (Ising-SRO, Ising-SBO, and XY-SRO) are stable dynamical phases in the thermodynamic sense. Within the Monte Carlo framework, a finite-size scaling analysis, shows that XY-SBO does not survive in the thermodynamic limit giving way to either an Ising-SBO or a XY-SRO regime. The finite-size scaling analysis further shows that the transitions between the three remaining dynamical phases either belong to the two-dimensional Ising universality class or are first-order in nature. Within the mean-field calculations yield three stable dynamical phases, i.e., Ising-SRO, Ising-SBO and XY-SRO, where the final steady state is independent of the initial condition chosen to evolve the equationsof motion, as well as a region of bistability where the system flows to either Ising-SBO or XY-SRO (Ising-SRO) depending on the initial condition. Unlike the stable dynamical phases, the XY-SBO represents a transient feature that is eventually lost to either Ising-SBO or XY-SRO. Our mean-field analysis highlights the importance of the competition between switching of the stationary point(s) of the free energy after each half cycle of the external field and the two-dimensional nature of the phase space for the equationsof motion.

Design of a Novel Macro-Micro Integrated Brain Surgery Robot Based on Modular Parallel Mechanisms

The advancement and development of medical surgical robots have provided new technological support for brain surgery and neurosurgical procedures, improving the reliability of highly complex and precise surgeries. In turn, this urges the design and development of novel surgical robots to possess higher precision, stability, and enhanced motion capabilities. In response to this practical demand, this paper introduces a macro-micro integrated medical brain surgery robot system based on the concept of modular PMs (parallel mechanisms), which have a total of 13 active DOFs (degrees of freedom). This system divides the motion process of brain surgery into a large-scale macro-motion space and a small-scale high-precision motion space for design and planning control. The introduction of the design concept that combines multiple modular parallel sub-mechanisms has brought a significant level of decoupling characteristics to the mechanism itself. A comprehensive introduction and analysis of the surgical robot are provided, covering aspects such as design, kinematics, motion planning, and performance indicators. To address the pose allocation and coordination of motion between the macro platform and the micro platform, a pose allocation algorithm based on the decoupling and non-decoupling characteristics in various dimensions of the macro-micro platform is proposed. The designed measurement experiments have demonstrated that the repeatability in positioning accuracy of the macro and micro platform reaches the level of micron and submicron respectively. Practical experiments of motion control and simulated brain electrode implantation validate the excellent performance and stability of the entire surgical robot system. This research contributes innovative insights to the development of medical surgical robot systems, particularly in the domain of mechanism design.

A Multi-Posture Grasping Manipulator Actuated by Shape Memory Alloy with Different Functional Modules.

Currently, multi-posture robots have complex grasping robotic manipulators with low power density, making it difficult to miniaturize and integrate. In this paper, a multi-posture grasping manipulator actuated by shape memory alloy with different functional modules is presented. It is composed of deflection, translation, rotation and grasping modules. Based on a D-H parameter method, the end motion trajectory model is established and the end motion space is drawn. Finally, the grasping experiment of a light circular object is carried out to verify the validity of the multi-posture grasping function of the multi-module combination manipulator, which provides a choice for future intelligent robot manipulators.

Achieving view-distance and -angle invariance in motion prediction using a simple network

Recently, human motion prediction has gained significant attention and achieved notable success. However, current methods primarily rely on training and testing with ideal datasets, overlooking the impact of variations in the viewing distance and viewing angle, which are commonly encountered in practical scenarios. In this study, we address the issue of model invariance by ensuring robust performance despite variations in view distances and angles. To achieve this, we employed Riemannian geometry methods to constrain the learning process of neural networks, enabling the prediction of invariances using a simple network. Furthermore, this enhances the application of motion prediction in various scenarios. Our framework uses Riemannian geometry to encode motion into a novel motion space to achieve prediction with an invariant viewing distance and angle using a simple network. Specifically, the specified path transport square-root velocity function is proposed to aid in removing the view-angle equivalence class and encode motion sequences into a flattened space. Motion coding by the geometry method linearizes the optimization problem in a non-flattened space and effectively extracts motion information, allowing the proposed method to achieve competitive performance using a simple network. Experimental results on Human 3.6M and CMU MoCap demonstrate that the proposed framework has competitive performance and invariance to the viewing distance and viewing angle.