Search Results (1,430)

One of the primary applications of industrial robots is in various manufacturing processes, such as milling, grinding, and additive manufacturing. To achieve the desired precision in tool trajectory performance when machining specific parts, it is necessary to calibrate the tool centre point (TCP) of the robot for each manufacturing process. The development of industrial robot tool trajectories is a multipurpose task. It encompasses issues related to robot geometry, path interpolation type, and trajectory waypoints approximation. The primary objective of this study is to establish a camera-based methodology for evaluating trajectory-following accuracy in industrial robots. The present paper proposes the use of a high-speed motion camera system for non-contact tracking of TCP trajectories. By capturing the robot’s end-effector motion in real-time and under actual trajectory tracking conditions, this technique enables a clearer understanding of how trajectory execution accuracy varies with velocity, trajectory geometry, trajectory interpolation, and robot kinematics. Provided analysis of two industrial robot types opened interesting findings related to the dependencies between the implementation of first- and second-degree interpolations. To illustrate this point, the implementation of second-degree interpolation ensures a more consistent velocity in the trajectory. This contrasts with first-degree interpolation, which is more challenging to achieve and is susceptible to variations in curvature. Conversely, the utilization of first-degree interpolation facilitates enhanced performance accuracy for smaller curvatures. The results of the experimental research confirm the initial hypothesis regarding the influence of interpolation mode and pave the way for future uses of this information for machine learning algorithms. Full article

This paper presents a control framework for multi-unmanned aerial vehicle systems that achieves safe and cooperative navigation in complex environments through a unified collision avoidance and trajectory guidance strategy. The principal innovation lies in the incorporation of velocity information into the design of a switching function, enabling more accurate assessment of collision risk and effectively reducing system conservativeness. Building upon this, an adaptive trajectory guidance mechanism is developed using collision avoidance information to ensure safe motion coordination among the vehicles. In addition, a closed-form solution for the dynamic system is derived, and its safety and stability are rigorously established through Lyapunov-based analysis. The effectiveness of the proposed framework is validated through simulation studies conducted on the MATLAB/Simulink platform (version R2020b), confirming reliable cooperative navigation in densely cluttered environments and guaranteeing dynamic safety. Full article

Ship maneuvering prediction relies on hydrodynamic derivatives, traditionally obtained through empirical formulations based on hulls built decades ago. A comparison with experimental data revealed a notable discrepancy, particularly for the linear sway velocity derivative ($Y_V^{\prime }$), where these regression models inadequately capture the behavior of modern hulls. To overcome this limitation, a novel approach is proposed, in which 690 virtual static drift tests were conducted across a systematic series of 115 modern hull forms, parametrically generated in the Grasshopper platform and thus benchmarked against seven vessels. This extensive numerical dataset enabled the development of an updated regression formulation for $Y_V^{\prime }$, which was grounded in key geometric parameters and incorporated specific terms related to the bow and stern shapes. The results obtained by the CFD-based method were compared with those obtained experimentally, confirming the high fidelity of this approach, yielding a maximum relative error of only $\mathbf{4.7}\%$ for the sway linear velocity derivative. Crucially, when this proposed empirical formula was integrated into a mathematical model (MM-TPN) to predict a ship’s trajectory, it demonstrated substantial improvement by reducing the absolute relative error in standard maneuvers from $\mathbf{23}\%$ to $\mathbf{10}\%$ compared with traditional methods used to describe the $Y_V^{\prime }$. Furthermore, an extensive discussion regarding the regression model was conducted, leading to the establishment of the drift angle threshold that invalidates the linear theory (set at ${\mathbf{10}}^{\circ }$ for blunt hulls and ${\mathbf{8}}^{\circ }$ for slender hulls). A comprehensive three-step validation process, encompassing the $V\&V$ of the virtual static drift tests, validation of the derived maneuvering coefficient, and validation through standard maneuvers employing the novel approach proposed here, was fully executed. Full article

To address trajectory tracking of underactuated unmanned surface vessels (USVs) under disturbances and model uncertainty, we propose a hierarchical control framework that combines model predictive control (MPC) with proximal policy optimization (PPO). The outer loop runs in the inertial reference frame, where an MPC planner based on a kinematic model enforces velocity and safety constraints and generates feasible body–fixed velocity references. The inner loop runs in the body–fixed reference frame, where a PPO policy learns the nonlinear inverse mapping from velocity to multi–thruster thrust, compensating hydrodynamic modeling errors and external disturbances. On top of this framework, we design a Proactive–Reactive Adaptive Reward (PRAR) that uses the MPC prediction sequence and real–time pose errors to adaptively reweight the reward across surge, sway and yaw, improving robustness and cross–model generalization. Simulation studies on circular and curvilinear trajectories compare the proposed PRAR–driven dual–loop controller (PRAR–DLC) with MPC–PID, PPO–Only, MPC–PPO and PPO variants. On the curvilinear trajectory, PRAR–DLC reduces surge MAE and maximum tracking error from 0.269 m and 0.963 m (MPC–PID) to 0.138 m and 0.337 m, respectively; on the circular trajectory it achieves about an 8.5% reduction in surge MAE while maintaining comparable sway and yaw accuracy to the baseline controllers. Real–time profiling further shows that the average MPC and PPO evaluation times remain below the control sampling period, indicating that the proposed architecture is compatible with real–time onboard implementation and physical deployment. Full article

Accurate multi-leaf collimator (MLC) motion prediction is a prerequisite for precise dose delivery in advanced techniques such as IMRT and VMAT. Traditional patient-specific quality assurance (QA) methods remain resource-intensive and prone to physical measurement uncertainties. This study aimed to develop machine learning (ML) models to predict delivered MLC positions using kinematic parameters extracted from DICOM-RT plans for the Elekta Versa HD system. A dataset comprising 200 patient plans was constructed by pairing planned MLC positions, velocities, and accelerations with corresponding delivered values parsed from unstructured trajectory logs. Four regression models, including linear regression (LR), were trained to evaluate the deterministic nature of the Elekta servo-mechanism. LR demonstrated superior prediction accuracy, achieving the lowest mean absolute error (MAE) of 0.145 mm, empirically confirming the fundamentally linear relationship between planned and delivered trajectories. Subsequent dosimetric validation using ArcCHECK measurements on 17 clinical plans revealed that LR-corrected plans achieved statistically significant improvements in gamma passing rates, with a mean increase of 2.24% under the stringent 1%/1 mm criterion (p < 0.001). These results indicate that the LR model successfully captures systematic mechanical signatures, such as inertial effects. This study demonstrates that a computationally efficient LR model can accurately predict Elekta MLC performance, providing a robust foundation for implementing ML-based virtual QA. This approach is particularly valuable for time-sensitive workflows like adaptive radiotherapy (ART), as it significantly reduces reliance on physical QA resources. Full article

This study examines the distance that an airplane is able to fly on inclined trajectories at various angles when it starts at different altitudes. It clearly exhibits the dependence of the airplane’s performance, when it climbs or descends, on the angle of inclination of the path and on the variations in initial weight, altitude, and power available. The results are obtained by solving the airplane’s equations of motion with the airplane’s dynamic constraints. Different specific dynamic behaviors of the airplanes are shown to occur within particular ranges of the inclination angle. Two remarkable behaviors, which have not been discussed before, are exhibited, namely the existence of a “relative ceiling” in ascending trajectories and that of a “maximum gliding altitude” in descending trajectories. These are specific altitudes that delimit the ranges of altitudes from which the airplane always attains the same terminal speed and reaches the same final altitude, whatever its initial altitude. Finally, it is shown how a feasibility matrix can be constructed, with which one can rapidly determine if a considered trajectory is flyable or not. This matrix requires only a small amount of memory storage and could reside on board essentially any airplane. The results of the study are illustrated with two very different airplanes: a Cessna 182 Skylane and a Silver Fox-like small UAV. Full article

Sport horse feeding robots face significant challenges in achieving precise navigation within complex stable environments. Uneven terrain and frequently moist ground often cause drive wheel slippage, resulting in path deviation and cumulative pose errors that compromise feeding accuracy and operational efficiency. To address this challenge, an enhanced Dynamic Window Approach (DWA) path planning framework, which integrates an automatic drift correction module based on an Inertial Measurement Unit (IMU) and a two-stage cascade proportional–integral–derivative (PID) controller, is proposed in this paper. This enhanced DWA enables precise yaw adjustment while preserving the native velocity sampling and trajectory evaluation framework of conventional DWA. Field validations were conducted through ten independent trials along a fixed 28 m feeding route in an actual sport horse feeding environment to quantitatively evaluate the robot’s path deviation and yaw angle stability. The results demonstrated that the enhanced algorithm reduced the standard deviation of path deviation from 0.161 m to 0.144 m (10.56% improvement) and decreased yaw angle standard deviation from 2.19° to 1.74° (20.55% reduction in angular oscillation). These improvements validated the effectiveness of the proposed algorithm in mitigating slippage-induced pose drift and significantly improving the locomotion capability of robots for sport horse feeding within stable environments. Full article

The growing adoption of Unmanned Surface Vessels (USVs) in commercial and defense domains raises challenges for safe navigation and strict adherence to the International Regulations for Preventing Collisions at Sea (COLREGs). This paper presents the implementation and field validation of three collision-avoidance approaches on a real USV: (i) behavior-based, (ii) a modified Velocity Obstacles (VO) algorithm, and (iii) a modified A* path-planning algorithm. Field trials in Guanabara Bay (Brazil) show that the behavior-based algorithm achieved the best balance between safety and efficiency, maintaining a safe mean Closest Point of Approach (30.0 m) while minimizing operational penalties: shortest total distance (179.4 m average), lowest mission completion time (174.7 s average), and smallest trajectory deviation (27.2% average increase). The VO algorithm operated with reduced safety margins (13.0 m average CPA) at the expense of larger detours (37.6% average distance increase), while the modified A* maintained equivalent safety (30.0 m average CPA) but produced the largest deviations (46.5% average increase). The trade-off analysis reveals that algorithm selection depends on operational priorities between safety margins and route efficiency. Full article

Accurate and real-time sperm tracking is essential for automation in Intracytoplasmic Sperm Injection (ICSI) and fertility diagnostics, where maintaining correct identities across frames improves the reliability of sperm selection. However, identity fragmentation, overcounting, and tracking instability remain persistent challenges in crowded and low-contrast microscopy conditions. This study presents a robust two-layer tracking framework that integrates BoT-SORT with an Extended Kalman Filter (EKF) to enhance identity continuity. The EKF models sperm trajectories using a nonlinear state that includes position, velocity, and heading, allowing it to predict motion across occlusions and correct fragmented or duplicate IDs. We evaluated the framework on microscopy videos from the VISEM dataset using standard multi-object tracking (MOT) metrics and trajectory statistics. Compared to BoT-SORT, the proposed EKF-BoT-SORT achieved notable improvements: IDF1 increased from 80.30% to 84.84%, ID switches reduced from 176 to 132, average track duration extended from 74.4 to 91.3 frames, and ID overcount decreased from 68.75% to 37.5%. These results confirm that the EKF layer significantly improves identity preservation without compromising real-time feasibility. The method may offer a practical foundation for integrating computer vision into ICSI workflows and sperm motility analysis systems. Full article

Given the undoubtable similarities between the dynamic behavior of the vehicular traffic flow in terms of its response to boundary condition alterations dictated in the form of obstacles, and the specific case of supersonic compressible fluid flow fields, the current manuscript addresses developing a target trajectory for the overtaking maneuver of autonomous vehicles. The path-planning is pursued in analogy to the governing principles of the supersonic compressible fluid flow fields, with the specific definition of a physically meaningful dimensionless group, namely the Traffic Mach number (M_T), which grants the initial access point to the said set of fundamental equations. This practical application is a follow-up to the primarily established proof-of-concept level introduction and analysis of the more general case of collision avoidance for autonomously driven vehicles in accordance with the supersonic compressible fluid flow field, where the Traffic Mach number was first introduced. The proposed trajectory is then taken to the next block of the investigation, namely the tracking and control aspects of the maneuvering vehicle’s dynamics. The path tracking controller is designed based on sliding mode control technique and the algorithm is applied on a 7-DOF simulation model, used for validation and discussion of results. The proposed method is shown to be suitable for overtaking maneuvers of autonomous vehicles, whilst meeting the criteria for a relative velocity from the constant-velocity vehicle ahead of the road in the supersonic regime based on the defined Traffic Mach number. The results are then presented, first, in the scope of the aerodynamics field configuration and their verifications, followed by the vehicle dynamics remarks showing the practicality of the proposed method in terms of vehicle motion. It is observed that the distance corresponding to the delayed maneuver maximizes at highest velocities of the ego vehicle, consistent with the highest M_T values, yet in all simulated cases, the control system of the vehicle model was capable of performing the maneuver based on the assigned trajectories through the present model. Full article

Variations in flight trajectory and velocity during vertical takeoff, transition, and level flight cause substantial changes in the relative inflow vector of multi-rotor unmanned aerial vehicles (UAVs). In urban environments, disturbances from complex wind fields further increase the uncertainty of inflow conditions. This study investigates the aerodynamic characteristics of a fixed-pitch small-sized UAV rotor under varying inflow angles, velocities, and rotational speeds using a subsonic return-flow wind tunnel. The experimental setup enables inflow angle control from −90° to +90° via a turntable. Results indicate that, without incoming flow, the axial thrust and torque coefficients remain nearly constant. With inflow, both coefficients become highly sensitive to velocity in the 2000–5000 rpm range, with deviations up to four times those under static conditions. The in-plane lateral force along the X-axis increases significantly with inflow velocity, reaching about half the axial force, while the Y-axis component is minor and negligible under symmetric configurations. Pitching and rolling moments increase rapidly once inflow velocity exceeds 8 m/s, surpassing the axial torque and exhibiting strong directional asymmetry around ±15° inflow angles. The results demonstrate coupled aerodynamic force and moment behavior of small rotors under complex inflow, providing experimental evidence for improved dynamic modeling, control design, and the energy optimization of UAVs operating in turbulent wind environments. Full article

Intrusive phase-detection probes remain a standard tool for local characterization of gas–liquid bubbly flows, but their accuracy is strongly affected by probe geometry and bubble–probe interaction kinematics. This work presents a Monte Carlo-based framework to evaluate four-sensor intrusive probes in bubbly flow, relaxing the classical assumptions of spherical bubbles and purely axial trajectories. Bubbles are represented as spheres or ellipsoids, a broad range of non-dimensional probe geometries are explored, and local quantities such as interfacial area concentration, bubble and flux velocities, and chord lengths are recovered from synthetic four-sensor signals. The purpose of the framework is threefold: (i) it treats four-sensor probes in a unified way for interfacial area, velocity, and chord length estimation; (ii) it includes ellipsoidal bubbles and statistically distributed incidence angles; and (iii) it yields compact correction laws and design maps expressed in terms of the spacing-to-diameter ratio $a_p/D$, the dimensionless probe radius $r_p/D$, and the missing ratio $m_r$ (defined as the fraction of bubbles that cross the probe footprint without being detected), which can be applied to different intrusive four-sensor probes. The numerical results show that, within a recommended geometric range $0.5\le a_p/D\le 2$ and $r_p/D\le 0.25$ and for missing ratios $m_r\le 0.7$, the axial velocity $V_z$ estimates the bubble centroid velocity and its projection with typical errors within $\pm 10\%$, while a chord length correction $C{L}_{corr}\left(m_r\right)$ recovers the underlying chord length distribution with a residual bias of only a few percent. The proposed interfacial area correction, written solely in terms of $m_r$, remains accurate in polydisperse bubbly flows. Outside the recommended $(a_p/D,r_p/D)$ range, large probe radius or extreme tip spacing lead to velocity and chord length errors that can exceed 20–30%. Overall, the framework provides quantitative guidelines for designing and using four-sensor intrusive probes in bubbly flows and for interpreting their measurements through geometry-aware correction factors. Full article

This study systematically investigates the evolution of the flow field and cavitation behavior during the underwater launch of a rotating sphere. By comparing surface pressure distribution, cavitation evolution, flow separation locations, and re-entrant jet formation under various rotational conditions, this study reveals the significant influence of rotation on both the cavitation processes and sphere’s motion trajectory. It is found that under rotational conditions, cavity detachment tends to occur earlier on the front side, and the re-entrant jet develops more fully, reaching maximum length and intensity at a moderate angular velocity. In additionally, rotation alters the cavity interface and overall flow structure, resulting in noticeable differences in surface wetting, pressure distribution, and separation behavior between the front and rear sides. As the rotational speed increases, flow separation points become less distinct, and pressure fluctuations on the rear side intensify, indicating that rotation plays a critical role in modulating underwater cavitation dynamics. The findings provide theoretical insights into flow control and cavitation risk assessment for underwater launches of rotating bodies. Full article

The Twin Hybrid Autonomous Underwater Vehicle (THAUV) is an underwater monitoring system consisting of a twin buoyant body and a fixed wing mounted between them. It is equipped with two propeller thrusters and a pair of elevators at the aft end. As a new type of underwater vehicle, it combines the long endurance of an underwater glider (UG), the high-speed maneuverability of an autonomous underwater vehicle (AUV), and the ability to carry larger payloads. In this paper, the motion equations of the THAUV are established, and its simulation model is developed using SIMULINK. Computational fluid dynamics (CFD) is further employed to identify hydrodynamic parameters under different elevator size conditions. A case study is conducted to analyze the effects of three different widths of elevators on glide performance, including gliding speed, pitching angle, and gliding trajectory. CFD results show that when the elevator deflection angle is zero, the hydrodynamic forces acting on the THAUV increase as the elevator width increases under identical angle of attack and velocity conditions. Under CFD conditions with fixed angle of attack and flow velocity, the sensitivity of the hydrodynamic characteristics to elevator deflection became significantly more pronounced. Increasing the elevator deflection angle led to substantial growth in the generated hydrodynamic forces. Motion simulations further show that increasing the elevator deflection angle enhances the THAUV’s gliding performance. Comparative results also reveal that glide performance improves with larger elevator width. Full article

This paper presents a physically constrained grey-box identification framework for electromechanical systems, illustrated through the dynamics of brushed DC motors. The method estimates all electromechanical parameters by minimizing a normalized residual that combines current, velocity, and steady-state algebraic constraints under a current-limit condition. Classical approaches such as least-squares and black-box identification often lack physical interpretability and do not explicitly enforce steady-state consistency, making their estimates susceptible to nonphysical parameter drift. The proposed formulation incorporates these physical constraints within a Levenberg–Marquardt scheme with signal normalization, enabling the joint minimization of current and velocity errors. Validation was performed using step-response data from two DC motors under both synthetic and experimental conditions. When applied to unfiltered measurements, the method maintained steady-state relative errors below 1% and achieved low trajectory discrepancies, with NRMSE in velocity between 2.6 and 3.2% and NRMSE in current between 0.9 and 1.2% across both motors. Embedding physical and steady-state constraints directly into the cost function improves robustness and ensures physically consistent parameter estimates, even under high measurement noise and without filtering. The approach provides a general strategy for dynamic system identification under physical consistency requirements and is suitable for rapid calibration, diagnostic monitoring, and controller tuning in robotic and mechatronic applications. Full article