Motion Trajectory Research Articles

Research on the multitarget 3D trajectory tracking method of Thalassodes immissaria in the thermal infrared region based on YOLOX-GMM and SORT-Pest.

Studying the behavioral responses and movement trajectories of insects under different stimuli is crucial for developing more effective biological control measures. Therefore, accurately obtaining the movement trajectories and behavioral parameters of insects in three-dimensional space is essential. This study used the litchi pest Thalassodes immissaria as the research object. A special binocular vision observation system was designed for nighttime movement. A thermal infrared camera was used for video recording of T. immissaria in a lightless environment. Moreover, a multi-object tracking method based on the YOLOX-GMM and SORT-Pest algorithms was proposed for tracking T. immissaria in thermal infrared images. By obtaining the central coordinates of the two T. immissaria in the video, target matching and 3D trajectory reconstruction in the parallel binocular system were achieved. Error analysis of the T. immissaria detection and tracking model, as well as the 3D reconstruction model, showed that the average accuracy of T. immissaria detection reached 89.6%, tracking accuracy was 96.9%, and the average error of the reconstructed 3D spatial coordinates was 15 mm. These results indicate that the method can accurately obtain the 3D trajectory and motion parameters of T. immissaria. Such data can greatly contribute to researchers' comprehensive understanding of insect behavioral patterns and habits, providing important support for more targeted control strategies.

Phase-modulation-induced reconfigurable rotating photonic lattices in atomic vapors.

We propose a method to prepare optically induced rotating hexagonal and honey-comb photonic lattices by employing the phase modulated three-beam interference in atomic vapors with electromagnetically induced transparency. The phase differences among the three beams are dynamically elaborated to synthesize the circular motion (in transverse dimensions) of waveguides in the photonic lattices. Further, we verify this model experimentally in the case of low-speed modulation. A weak Gaussian probe field is sent into the constructed helical photonic lattices to image their structures under electromagnetically induced transparency (EIT). The motion trajectories of the sites on the discretized output patterns exhibit repeated circles, advocating the formation of rotating lattices. By introducing phase modulations to involved beams, we provide a continent way for producing transverse motions in waveguide arrays with reconfigurability in rotational direction, radius, and speed. This work looks forward to promising applications in topological photonics with great popularity.

Design and Analysis of a Planar Six-Bar Crank-Driven Leg Mechanism for Walking Robots

This study presents the design and a thorough analysis of a six-bar crank-driven leg mechanism integrated with a skew pantograph, developed for walking robots. The mechanism’s dimensions were optimized using a rigorous dimensional synthesis process in GIM software (version 2024). Subsequently, a detailed kinematic analysis was performed in GIM to simulate the leg’s motion trajectory, velocity, and acceleration. In parallel, kinematic equations were formulated using the vector loop method, implemented in MATLAB (version R2013-b), and compared with the GIM results for validation, demonstrating the strong agreement between both tools. These results confirm the mechanism’s ability to generate a compact, high-lift foot trajectory while maintaining system stability and energy efficiency. An inverse dynamic analysis was carried out to determine the actuator’s driving torque, ensuring efficient operation under expected load conditions. Furthermore, topology optimization conducted in SOLIDWORKS (version 2021) significantly reduced the weight of the ground-contacting link while preserving its structural integrity. A subsequent stress analysis validated the mechanical viability of the optimized design, supporting its feasibility for real-world implementation. This research provides a robust foundation for the development of a functional prototype. Its potential applications include mobile robots for sectors such as agriculture and all-terrain vehicles, where efficient, reliable, and adaptive locomotion is crucial. The proposed mechanism strikes an optimal balance between mechanical simplicity, cost-effectiveness, and high performance, making it well-suited for challenging operational environments.

Research on the Performance and Control Strategy of Electro-Hydraulic Servo System for Selective Hole Digging Tree Planter

Compared to agricultural environments, afforestation sites are more complex, often presenting issues such as undulating and uneven terrain. These conditions lead to instability in hole digging depth and plant spacing during continuous movement, and the hole shape may not meet expectations. Additionally, the hydraulic system exhibits slow response speed and long steady-state time, affecting the quality of sapling planting. To address these issues, this paper designs an intelligent planting control system for intermittent hole digging under continuous dynamic movement, based on a large tree planter. The focus is on studying the dynamic accuracy of the hole digging cylinder to resolve the instability of plant spacing and planting depth in actual planting processes. Firstly, a motion trajectory model of the intermittent hole digging mechanism is established to obtain the relationship between the displacement trajectory of the rotating cutter and the displacements of the floating cylinder and the hole digging cylinder. Secondly, a mathematical model of the electro-hydraulic servo system is established to control the dynamic accuracy of the hole digging operation. Finally, a Simulink simulation model of the system is established to analyze the performance indicators of the hydraulic system during operation using step and sinusoidal excitation signals. The test results show that the displacement of the hydraulic piston rod can ensure a linear extension trend within the range of 0 to 0.4 m, and the extension distance of the hole digging cylinder in the planting system is 0 to 0.35 m, ensuring linear change within this stroke. When the system’s extension command is 1 V, the actual output is 0.6 m, with a relative error of less than 10% compared to the simulation value, indicating that the control strategy can effectively improve the dynamic performance of the system. When the hydraulic system is in a steady-state extension state at 50 to 58.6 s, the relative error with the simulation value is 7.3%, meeting the “double ten indicators” requirement. The research results clearly verify the superior performance of the proposed intelligent control system, and the proposed control strategy has great potential in practical applications, promising to improve afforestation quality by stabilizing planting spacing and planting depth.

Deep learning neural network-assisted badminton movement recognition and physical fitness training optimization

This work aims to solve the problem of low accuracy in recognizing the trajectory of badminton movement. This work focuses on the visual system in badminton robots and conducts side detection and tracking of flying badminton in two-dimensional image plane video streams. Then, the cropped video images are input into a convolutional neural network frame by frame. By adding an attention mechanism, it helps identify the badminton movement trajectory. Finally, to address the detection challenge of flying badminton as a small target in video streams, the deep learning one-stage detection network, Tiny YOLOv2, is improved from both the loss function and network structure perspectives. Moreover, it is combined with the Unscented Kalman Filter algorithm to predict the trajectory of badminton movement. Simulation results show that the improved algorithm performs excellently in tracking and predicting badminton trajectories compared with the existing algorithms. The average accuracy of the proposed method for tracking badminton trajectories is 91.40 %, and the recall rate is 84.60 %. The average precision, recall, and frame rate of the measured trajectories in four simple and complex scenarios of badminton flight video streams are 96.7 %, 95.7 %, and 29.2 frames/second, respectively. They are all superior to other classic algorithms. It is evident that the proposed method can provide powerful support for badminton trajectory recognition and help improve the accuracy of badminton movement recognition.

Theoretical Analysis of Self-Shrinkage Spheroidization of Irregular Degradable Polymer Powder under Thermodynamic Nonequilibrium State

Laser three-dimensional (3D) printing offers significant advantages in integrating the shape and function of regenerative tissues through biomimetic manufacturing. However, its effectiveness is limited by the lack of specialized biopolymer powders—while solvent methods that use residual solvents produce powders with poor biocompatibility, mechanical methods result in irregularly shaped crystals. In this study, a biopolymer powder spheroidization and shaping technology, which utilizes the evolution of irregular powders into spheres with minimal surface free energy in the molten state, is proposed based on the thermodynamic principle of minimum energy. Initially, the motion trajectory and temperature field of the poly(L-lactic acid) (PLLA) powder during spheroidization were quantitatively assessed and optimized using Stokes’ law and Fourier's principle. Subsequently, the cohesive forces and aggregation kinetics of the polymer chains were calculated using molecular dynamics. Finally, based on these calculations, a phase-field model was constructed to simulate the evolution of the spheroidization rate and deduce the optimal parameters for the process. This precise approach enhances PLLA spheroidization control for laser 3D printing, improves part densification and surface quality, and offers a clean and efficient path for preparing high-quality PLLA spheroidized powder for laser 3D printing.

Enhancing cathode design by considering complex motion and variations of electric field distribution in counter-rotating electrochemical machining

The counter-rotating electrochemical machining (CRECM) shows unique potential in the machining of thin-walled rotating parts with complex convex structures. CREM realizes the shaping of complex convex structures through the relative rotation of the cathode and anode. The complex motion pattern and electric field distribution make it difficult to apply the existing cathode design methods to CRECM. To solve this problem, the matrix equations of cathode motion based on the kinematics and the electric field simulation model are established. The motion trajectories and edge contours at different angles are analyzed. The rotational overlap theory of motion trajectories under the windows at different angles is proved. Besides, the relationship between electric field distribution and the convex structure forming under different angle windows is studied, and the fundamental reason for deviations occurs when the convex profile is rotated to coincide is revealed. Therefore, a prediction model of the sidewall dissolution is established to correct this deviation, thereby deriving a high-precision design formula for the cathode windows of the high convex structures. By designing a cathode with oval-like windows to curry out CRECM experiments, the array-arranged (30×5) circular high convex structure with a maximum roundness error of 0.065 mm is successfully fabricated.

Effect of side ratio on the two-degrees-of-freedom vortex-induced vibrations of the rectangular cross section model

The effect of the mutative side ratio (D/B) on the vortex-induced vibration (VIV) characteristic, aerodynamic characteristics, and the surrounding time-averaged and transient flow field of a rectangular cross section model were simulated numerically. Based on Fluent 19.0 platform, overset grid technology and the fourth-order Runge–Kutta method were used at Re 22 000. First, a rectangular cross section model with D/B = 0.25 was selected, and the simulation method and parameter settings were validated against previous literatures. The subsequent analysis compared and evaluated the effect of side ratio on the VIV response by focusing on statistical values of aerodynamic force coefficients, self-spectra, amplitude ratio, motion trajectory, and phase transition changes for stream-wise and cross-flow directions. Moreover, the study examined the influence of different models at different reduced velocities (Ur) on wake vortex-shedding. The findings suggest that, within a fixed cross-sectional area, a smaller side ratio leads to a weaker VIV characteristic and notably lower aerodynamic performance compared to a larger side ratio. The vortex-shedding mode of the rectangular cross section, particularly with a large side ratio, is less sensitive to changes in Ur compared to the standard square cylinder. An examination of the Reynolds number (Re) effect on the minimum and maximum side ratio models reveals that it has a more pronounced impact on the aerodynamic performance and VIV of the cross-flow when compared to in-line flow. In general, it is noted that larger side ratio model exhibits a stronger sensitivity to the variation of Re.

3D Numerical Investigation of Bubble Upflow Condensation Behaviors During Subcooled Flow Boiling in Mini-Channel with VOSET

In the subcooled flow boiling process, bubble condensation is an inevitable basic phenomenon. This paper studies the condensation phenomenon of the single, double vertical/horizontal saturated bubbles rising in a three-dimensional mini-rectangular channel based on the interface capture method VOSET (coupled volume-of-fluid and level set method) and the phase transition model. Bubble condensation behaviors are investigated at different initial diameters, inlet velocity distributions, subcooling temperatures, bubble gaps, and arrangement for the two-bubble condensing system especially. The effects of these parametric on bubble motion trajectory, shape evolution, volume variation, and condensation rate are presented. The numerical results indicated that the initial bubble size and liquid subcooling play an important role in influencing the shape and volume variation of condensing bubble behaviors significantly, while the inlet velocity distribution only affects bubble motion trajectory. Furthermore, the interaction and coalescence between the bubbles will affect the bubble behaviors and the condensation rate. Finally, the condensation heat transfer coefficients at the bubble surfaces for different cases simulated in this paper are presented, seemingly first in the literature.

Upper Limb Rehabilitation Robots Training Analysis based on Multi-sensor Trajectory Data and Human-computer Interaction

Upper limb rehabilitation robots have important practical significance in helping patients recover their motor function, but traditional training methods often lack real-time and accurate evaluation of patient movement status and intention and have low interactive participation. Therefore, the study proposes a training system for upper limb rehabilitation robots based on multi-sensor trajectory data and human-computer interaction. The system is designed from three aspects: inertial sensor trajectory tracking, Kinect sensor image restoration, and interaction system design. A polynomial joint zero value constraint algorithm is introduced to correct errors, and variable parameter pixel filtering combined with a weighted average moving algorithm is used to improve image quality. The training results showed that the robot trajectory tracking accuracy significantly improved, and the improvement in motion trajectory error was greater than 20%. Compared with traditional training methods, interactive training had a recognition accuracy of over 85% in rehabilitation actions, and the system had better stability. This rehabilitation robot can effectively meet the feasibility and effectiveness of improving the interaction system, providing new technological means for intelligent medical services.

Isolated humeral distalization in reverse total shoulder arthroplasty: a biorobotic shoulder simulator study

BackgroundHumeral distalization is inherent to reverse total shoulder arthroplasty (rTSA) and is often produced with concomitant humeral lateralization via the level of the humeral head cut, implant positioning, implant neck shaft angle, and polymer insert thickness. Biomechanical data on the isolated effects of humeral distalization remain limited but could be important to consider when optimizing postoperative rTSA shoulder function. This study investigated the effects of isolated humeral distalization on shoulder biomechanics using a biorobotic shoulder simulator. MethodsEight fresh-frozen cadaveric shoulders were tested using custom polymer inserts that translated the bearing surface 0, +5, +10, and +15 mm along the humeral stem axis, producing isolated distalization without lateralization. Specimens underwent passive elevation in the scapular plane with a static scapula to assess glenohumeral range of motion. Scapular plane abduction motion trajectories were then performed, driven by previously collected scapulothoracic and glenohumeral kinematics from rTSA patients. The effect of isolated distalization on passive elevation was tested using mixed-effects linear regression and the effect on muscle force, joint reaction force, and muscle excursion during active scapular-plane abduction was tested using statistical parametric mapping random effects analysis. ResultsMaximum passive scapular plane elevation increased with humeral distalization (4° per 5 mm distalization). During active elevation, deltoid and rotator cuff muscle forces, and joint reaction forces, increased up to 37% per 5 mm of distalization. Simulated deltoid muscle excursion was altered with increasing distalization but amounted to no more than 0.8 mm change from baseline per 5 mm of distalization. Rotator cuff muscles were consistently lengthened throughout abduction, up to 1.6 mm per 5 mm of distalization. These trends were observed across various patient motions. ConclusionsIsolated humeral distalization caused dramatic increases in the muscle forces required to perform scapular-plane abduction. Joint reaction forces increased correspondingly. These results suggest that implant and surgical strategies to generate deltoid muscle tension without humeral distalization may promote better active range of motion and more durable long-term outcomes over approaches that rely on distalization.

Intention inference for space targets using deep convolutional neural network

Intention inference for space targets is crucial for space situational awareness. This paper introduces a rapid and precise method for recognizing the intentions of non-cooperative space targets using a deep convolutional neural network (CNN). By employing a relative orbital dynamics model, an analysis of relative motion was performed, resulting in the identification of 11 distinct motion intentions for space targets. This study also describes how to generate relative motion trajectory images to create a training set for intention inference, effectively converting the problem into one of image recognition and classification. Extensive simulations were carried out to fine-tune the network hyperparameters, and the results highlight the exceptional performance of the proposed CNN-based method, which achieved an accuracy of 99.682%. This method significantly enhances recognition accuracy over other neural network-based methods for space objects and offers considerable potential for applications like spacecraft collision avoidance and strategic maneuvers among space targets.

Hybrid Orientation and Position Collaborative Motion Generation Scheme for a Multiple Mobile Redundant Manipulator System Synthesized by a Recurrent Neural Network.

To enable distributed multiple mobile manipulator systems to complete collaborative tasks safely and stably, this article investigates and presents a motion generation scheme that considers both orientation and position coordination based on a distributed recurrent neural network. Moreover, physical limits are also considered. Specifically, the orientation and position coordination constraints and physical limits are modeled separately as equality and inequality constraints with coupled variables. Subsequently, a motion generation scheme for multiple mobile manipulators based on quadratic programming is established. Finally, a distributed linear variational inequality-based primal-dual neural network is constructed to solve the motion generation scheme and obtain the motion trajectories of all the mobile manipulators. The simulation results demonstrate that the hybrid orientation and position collaboration motion generation scheme effectively addresses the position and orientation coordination problem for multiple mobile manipulator systems. Compared to other schemes, the proposed scheme based on a distributed computing structure greatly enhances the stability of the system. Additionally, the proposed approach introduces orientation coordination and physical limits, which increases the practicality of the system.

Unsteady separation mechanism of ground horizontal-sliding takeoff system

Complex aerodynamic interference has long posed challenges to the safe separation of multi-body systems. Based on a new conceptual ground horizontal-sliding takeoff approach, the rocket sled system, computational fluid dynamics (CFD) simulations are conducted to study the separation between the payload and the rocket booster, with the Advisory Group for Aerospace Research and Development Model B representing the payload. The motion trajectory of the payload and the unsteady interaction flow mechanisms during separation are analyzed by altering the payload's layout, center of gravity (CoG), and sliding Mach number. The results show that positioning the payload closer to the front along the axis increases the likelihood of collisions after separation. At a sliding velocity of Ma=3, the payload tends to exhibit a nose-down attitude when lc/lp<0.64. Conversely, when lc/lp>0.64, the pitching angle becomes excessively large, which increases the risk of stalling or somersaulting. Furthermore, the CoG needs to move forward to reduce the growth rate of the pitching angle when the separation Mach number is increased. These studies can provide valuable references for the development of separation technology for the rocket sled system.

Decoding Silent Speech Cues From Muscular Biopotential Signals for Efficient Human‐Robot Collaborations

AbstractSilent speech interfaces offer an alternative and efficient communication modality for individuals with voice disorders and when the vocalized speech communication is compromised by noisy environments. Despite the recent progress in developing silent speech interfaces, these systems face several challenges that prevent their wide acceptance, such as bulkiness, obtrusiveness, and immobility. Herein, the material optimization, structural design, deep learning algorithm, and system integration of mechanically and visually unobtrusive silent speech interfaces are presented that can realize both speaker identification and speech content identification. Conformal, transparent, and self‐adhesive electromyography electrode arrays are designed for capturing speech‐relevant muscle activities. Temporal convolutional networks are employed for recognizing speakers and converting sensing signals into spoken content. The resulting silent speech interfaces achieve a 97.5% speaker classification accuracy and 91.5% keyword classification accuracy using four electrodes. The speech interface is further integrated with an optical hand‐tracking system and a robotic manipulator for human‐robot collaboration in both assembly and disassembly processes. The integrated system achieves the control of the robot manipulator by silent speech and facilitates the hand‐over process by hand motion trajectory detection. The developed framework enables natural robot control in noisy environments and lays the ground for collaborative human‐robot tasks involving multiple human operators.

RS-SLAM: real time semantic slam with driverless car using LiDAR-camera-IMU sensing

Abstract Accurate and robust Simultaneous Localization and Mapping (SLAM) technology is a critical component of driverless cars, and semantic information plays a vital role in their analysis and understanding of the scene. In the actual scene, the moving object will produce the shadow phenomenon in the mapping process. The positioning accuracy and mapping effect will be affected. Therefore, we propose a semantic SLAM framework combining LiDAR, IMU, and camera, which includes a semantic fusion front-end odometry module and a closed-loop back-end optimization module based on semantic information. An improved image semantic segmentation algorithm based on Deeplabv3+ is designed to enhance the performance of the image semantic segmentation model by replacing the backbone network and introducing an attention mechanism to ensure the accuracy of point cloud segmentation. Dynamic objects are detected and eliminated by calculating the similarity score of semantic labels. A loop closure detection method based on semantic information is proposed to detect key semantic features and use threshold range detection and point cloud re-matching to establish the correct loop closure detection, and finally reduce the global cumulative error and improve the global trajectory accuracy using graph optimization to ultimately obtain the global motion trajectory and realize the construction of 3D semantic maps. We evaluated it on the KITTI dataset and collected a dataset for evaluation by ourselves, which includes four different sequences. The results show that the proposed framework has good positioning accuracy and mapping effect in large-scale urban road environments.

Resonance and surfactant effects on the interaction of a rising bubble and a sedimenting particle in an oscillating fluid

This paper considers the interaction of a rising bubble and a sedimenting fine particle in a liquid with a soluble surfactant subjected to the gravity field, forced flow, and vibrations. The frequency of vibrations is close to the resonance value. The particle motion trajectories are calculated, taking into account its inertia, fluid viscosity, Basset force, buoyancy force, and the mean force acting on the particle due to the inhomogeneity of the pulsating field. The collision efficiency is evaluated, and the cross sections of the sedimenting particles captured by the rising bubble are found. It is demonstrated that even at low vibration intensity but in the presence of a weak surfactant effect, a significant increase in the probability of particle–bubble collision and in the capture cross section can be achieved. The obtained results can be used to intensify the flotation processes by ultrasonic treatment.

Study on the diffusion and blocking behavior of bubble plumes in Cross-Flow

Abstract With the deepening of the concept of environmental protection, environmental problems in dredging engineering have attracted more and more attention. The diffusion of suspended particulate matter is inevitable in the process of dredging. A bubble plume, as a multiphase linear plume, has been widely studied because it can block the diffusion of suspended particulate matter in water. To explore the formation characteristics of the bubble plume, we evaluate the formation quality of the bubble plume qualitatively and quantitatively, analyze the effect of the bubble plume on blocking the diffusion of suspended particulates under different operating conditions (the type of bubble plume generation device, the number of bubble plume rows, and the ventilation flow rate) and environmental conditions (water flow velocity), and study the movement trajectory of blocked particles and blocked failed particles. A series of experiments on bubble plume characteristics and bubble plume blocking the diffusion of suspended particles were conducted to analyze the particle distribution characteristics and bubble plume blocking effect under the interaction of transverse flow and bubble plume. The results show that the better the quality of the bubble plume is, the lower the transverse flow intensity is, and the better the barrier effect of the bubble plume is.

EMG and SSVEP-based bimodal estimation of elbow angle trajectory

Detecting intentions and estimating movement trajectories in a human–machine interface (HMI) using electromyogram (EMG) signals is particularly challenging, especially for individuals with movement impairments. Therefore, incorporating additional information from other biological sources, potential discrete information in the movement, and the EMG signal can be practical. This study combined EMG and target information to enhance estimation performance during reaching movements. EMG activity of the shoulder and arm muscles, elbow angle, and the electroencephalogram signals of ten healthy subjects were recorded while they reached blinking targets. The reaching target was recognized by steady-state visual evoked potential (SSVEP). The selected target’s final angle and EMG were then mapped to the elbow angle trajectory. The proposed bimodal structure, which integrates EMG and final elbow angle information, outperformed the EMG-based decoder. Even under conditions of higher fatigue, the proposed structure provided better performance than the EMG decoder. Including additional information about the recognized reaching target in the trajectory model improved the estimation of the reaching profile. Consequently, this study’s findings suggest that bimodal decoders are highly beneficial for enhancing assistive robotic devices and prostheses, especially for real-time upper limb rehabilitation.

Tracking Public Interest Through Google Trends: Comparative Analysis of Global Movements

This paper examined the digital dynamics of three significant social justice movements: Black Lives Matter (2020), South African unrest (2021), and Mahsa Amini protests (2022) through the lens of Google Trends analysis. By tracking search interest patterns during key events, the study explored how each movement gained momentum and sustained visibility online. The analysis revealed distinct public engagement patterns for each movement: The Black Lives Matter movement experienced global peaks in search interest, with sustained attention driven by discussions on police reform and racial justice. The South African unrest saw sharp, localized spikes in search activity, particularly during moments of heightened tension such as the looting incidents and the government’s deployment of the military. The Mahsa Amini protests demonstrated sustained, high-level interest globally, with search trends reflecting the growing international focus on human rights and state repression in Iran. The findings highlighted how search engines like Google played a pivotal role in amplifying these movements, documenting societal reactions, and shaping the global public discourse. This study underscored the power of digital platforms to reflect and influence the trajectory of social movements across different political and cultural contexts.