Search Results (535)

Advances in sensor technology have significantly improved the efficiency and precision of agricultural spraying. Unmanned aerial vehicles (UAVs) are widely utilized for applying plant protection products (PPPs) and fertilizers, offering enhanced spatial control and operational flexibility. This study evaluated the performance of an autonomous UAV-based precision spraying system that applies variable rates based on zone levels defined in a prescription map. The system integrates real-time kinematic global navigation satellite system positioning with a proximity-triggered spray algorithm. Field experiments on a rice field were conducted to assess spray accuracy and fertilization efficacy with liquid fertilizer. Spray deposition patterns on water-sensitive paper showed that the graded strategy distinguished among zone levels, with the highest deposition in high-spray zones, moderate in medium zones, and minimal in no-spray zones. However, entry and exit deviations—used to measure system response delays—averaged 0.878 m and 0.955 m, respectively, indicating slight lags in spray activation and deactivation. Fertilization results showed that higher application levels significantly increased the grain-filling rate and thousand-grain weight (both p < 0.001), but had no significant effect on panicle number or grain count per panicle (p > 0.05). This suggests that increased fertilization primarily enhances grain development rather than overall plant structure. Overall, the system shows strong potential to optimize inputs and yields, though UAV path tracking errors and system response delays require further refinement to enhance spray uniformity and accuracy under real-world applications. Full article

The economic and strategic value of high-latitude maritime regions is increasingly significant, yet traditional Global Navigation Satellite Systems remain constrained by unfavorable geometric configurations and slow convergence speeds at high latitudes, failing to meet the growing demand for real-time centimeter-level high-precision positioning in these areas. Benefiting from their rapid motion and superior coverage over high-latitude zones, Low Earth Orbit (LEO) satellites offer an effective means to enhance positioning performance in such regions. This paper uses the real BDS data collected by an unmanned surface vessel in the high-latitude waters of the Southern Hemisphere, jointly simulates polar and medium-inclination LEO constellations, and systematically assess the enhancement effects of LEO augmentation on Precise Point Positioning (PPP) and Real-Time Kinematic (RTK) techniques. The results demonstrate that the polar-orbiting constellation markedly improves the observation environment, increasing the number of visible satellites by 70.2% and reducing the Position Dilution of Precision from 2.4 to 1.7, whereas the medium-inclination orbit constellation offered negligible improvement due to insufficient visibility. The rapid geometric change brought by LEO constellations is the core key to achieving fast convergence. Incorporating LEO observations drastically shortened the BDS PPP convergence time from 45.3 min to under 1 min, achieving a reduction of over 97%. Simultaneously, it improved the three-dimensional Root Mean Square accuracy by 54.7%, from 0.086 m to 0.039 m. Convergence within one minute was consistently achieved when at least 5.4 LEO satellites were included in the solution. Moreover, the addition of LEO signals increased the fixed solution rate of short-baseline RTK from 96.5% to 100%, while improving horizontal and vertical accuracy by 31.5% and 12.3%, respectively. This study confirms that LEO constellations, especially those in polar orbits, can substantially enhance BDS precise positioning performance in high-latitude maritime environments, thereby providing critical technical support for related navigation applications. Full article

Automated Valet Parking (AVP) systems require high-precision positioning, especially in indoor environments where Global Positioning System (GPS) is unavailable. Existing methods, which use markers installed on parking lot walls or ceilings, often encounter difficulties due to marker detection failures caused by complex parking behaviors, such as infrastructure constraints or perpendicular parking. This study proposes an optimized indoor positioning system for AVP using fiducial markers recognized by front and rear vehicle cameras. To enhance accuracy and robustness, an Extended Kalman Filter (EKF) fuses vehicle kinematic data with marker pose information. Critically, to address the issue of marker occlusion by the front camera during reverse parking, a novel camera switching algorithm employing a hysteresis pattern based on vehicle position, heading, and motion direction is introduced. This ensures continuous marker visibility and stable positioning during parking maneuvers. The system’s effectiveness was validated through simulations and extensive real-vehicle experiments in a real parking space. Results demonstrate that the EKF significantly reduces positioning errors compared to kinematic prediction alone, particularly during curved driving. Furthermore, the proposed camera switching algorithm successfully overcomes the limitations of a front-only camera system, significantly improving positioning accuracy (e.g., reducing RMS error by up to 25.0% in X and 17.6% in Y during parking) and eliminating instability observed with simpler switching logic. This research contributes a cost-effective and reliable positioning solution, advancing the feasibility of AVP systems in challenging indoor environments. Full article

This paper proposes a calibration method for redundant robot arms with highly elastic joints. The method uses the second-order Chebyshev polynomial to characterize the variation in the error with the poses of all joints. This error model is consistent with the variation in the gravitational torque on each joint and demonstrates good generalization. Based on this, the calibration model includes both kinematic errors and non-kinematic errors. For this high-dimensional model, an adaptive iterative identification algorithm is proposed for a large number of small error parameters of various types. The algorithm sets specific iteration rules for different types of error parameters and adjusts the convergence amplitude in each iteration, ensuring that the iterative algorithm converges to the global optimum. The simulation results show that for a redundant robot arm with 12 highly elastic joints, even with large linearization modeling errors, the new identification algorithm can gradually eliminate them during iteration, achieving an identification accuracy higher than 99.975% for all of the error parameters. The experimental results indicate that on a redundant robot arm with eight cable-driven elastic joints, the new model and identification algorithm reduce the 96.6% absolute positioning errors of the robot arm, enabling it to perform precise and flexible operations. It takes 40.534 s and 29.077 s to run the identification algorithm on MATLAB (R2023b, 2.10 GHz CPU) in the simulation and experiment, respectively. Full article

Background/Objectives: this study describes the development of a novel three-dimensional electrogoniometer for the quantitative assessment of knee mobility and stability during gait. The primary objective is to determine whether real-time measurements obtained during dynamic activity provide more clinically relevant information than traditional static assessments. Methods: the device employs angular position encoders to capture knee joint kinematics—specifically flexion, extension, rotation, and tibial translation—during locomotion. Data are transmitted in real time to an Android-based application, enabling immediate graphical visualization. A descriptive observational study was conducted involving healthy participants and individuals with anterior cruciate ligament (ACL) injuries to evaluate the device’s performance. Results: results showed that the electrogoniometer captured knee flexion-extension with a range of up to 90°, compared to 45° typically recorded using conventional systems. The device also demonstrated enhanced sensitivity in detecting variations in tibial translation during gait cycles. Conclusions: this electrogoniometer provides a practical tool for clinical assessment of knee function, enabling real-time monitoring of joint behavior during gait. By capturing functional mobility and stability more accurately than static methods, it may enhance diagnostic precision and support more effective rehabilitation planning in orthopedic settings. Full article

The strong coupling of the six-degree-of-freedom (6-DoF) electro-hydraulic Stewart parallel mechanism manifests as adjusting the elongation of one actuator potentially inducing motion in multiple degrees of freedom of the platform, i.e., a change in pose; this pose change leads to time-varying and unbalanced load forces (disturbance inputs) on the six hydraulic actuators; unbalanced load forces exacerbate the time-varying nature of the acceleration and velocity of the six hydraulic actuators, causing instantaneous changes in the pressure and flow rate of the electro-hydraulic system, thereby enhancing the pressure–flow nonlinearity of the hydraulic actuators. Considering the advantage of artificial intelligence in learning hidden patterns within complex environments (strong coupling and strong nonlinearity), this paper proposes a reinforcement learning motion control algorithm based on deep deterministic policy gradient (DDPG). Firstly, the static/dynamic coordinate system transformation matrix of the electro-hydraulic Stewart parallel mechanism is established, and the inverse kinematic model and inverse dynamic model are derived. Secondly, a DDPG algorithm framework incorporating an Actor–Critic network structure is constructed, designing the agent’s state observation space, action space, and a position-error-based reward function, while employing experience replay and target network mechanisms to optimize the training process. Finally, a simulation model is built on the MATLAB 2024b platform, applying variable-amplitude variable-frequency sinusoidal input signals to all 6 degrees of freedom for dynamic characteristic analysis and performance evaluation under the strong coupling and strong nonlinear operating conditions of the electro-hydraulic Stewart parallel mechanism; the DDPG agent dynamically adjusts the proportional, integral, and derivative gains of six PID controllers through interactive trial-and-error learning. Simulation results indicate that compared to the traditional PID control algorithm, the DDPG-PID control algorithm significantly improves the tracking accuracy of all six hydraulic cylinders, with the maximum position error reduced by over 40.00%, achieving high-precision tracking control of variable-amplitude variable-frequency trajectories in all 6 degrees of freedom for the electro-hydraulic Stewart parallel mechanism. Full article

The inchworm piezoelectric motor, with the advantages of long stroke and high resolution, is ideally suited for precise positioning in wafer-level electron beam inspection systems. However, the large number of piezoelectric actuators and the complex excitation signal sequences significantly increase the complexity of system assembly and temporal control. A flexure-based actuation stator structure, along with simplified excitation signal sequences of a high-precision inchworm piezoelectric motor, is proposed. The alternating actuation of upper/lower clamping mechanisms and the driving mechanism fundamentally mitigates backstep effects while generating stepping linear displacement. The inchworm piezoelectric motor achieves precision linear motion operation using only two piezoelectric actuators. The actuation stator is analyzed via the compliance matrix method to derive its output compliance, input stiffness, and displacement amplification ratio. Furthermore, a kinematic model and natural frequency expression incorporating the pseudo-rigid-body method and Lagrange’s equations are established. The actuation stator and inchworm piezoelectric motor are analyzed through both simulations and experiments. The results show that the maximum step displacement of the motor is 16.3 μm, and the maximum speed is 9.78 mm/s, at a 600 Hz operation frequency with a combined alternating piezoelectric voltage of 135 V and 65 V. These findings validate the designed piezoelectric motor’s superior motion resolution, operational stability, and acceptable load capacity. Full article

Accurate parameter identification in dynamic models of robotic arms is essential for performing high-performance control and energy-efficient procedures. However, classic methods often encounter difficulties when modeling nonlinear, high-dimensional systems, particularly in the presence of real-world uncertainties. To address these challenges, this study focuses on identifying mass center positions and inertia matrix elements in a six-jointed industrial robotic arm and comparing the influence of optimized algorithms: the classical Particle Swarm Optimization (PSO) and the Quantum-behaved Particle Swarm Optimization (QPSO). The robot’s kinematic model was validated by comparing it with actual motion data, utilizing a high-precision neural network to ensure accuracy before conducting a dynamic analysis. A comprehensive dynamic model was created using Computer-Aided Optimization (CAO) in SolidWorks Premium 2023 to simulate realistic mass parameters, thereby validating the model’s reliability in a practical setting. The real (Referenced) and optimized dynamic models of the robot arm were validated using trajectory tracking simulations under sliding mode control (SMC) to assess the impact of the optimized model on the robot’s performance metrics. Results indicate that QPSO estimates inertia and mass center parameters with Mean Absolute Percentage Errors (MAPE) of 0.76% and 0.43%, outperforming PSO significantly and delivering smoother torque profiles and greater resilience to external disturbances. Full article

In recent years, the rapid development of moving platforms, especially unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), has promoted their widespread applications in various fields such as precision agriculture and formation flight. In these applications, for accurate real-time kinematic positioning between two moving platforms, receiver autonomous integrity monitoring (RAIM) is necessary to assure the reliability of the obtained relative positioning. However, the existing carrier phase-based RAIM (CRAIM) algorithms are mainly a direct extension of pseudorange-based RAIM (PRAIM), whose availability is also a major challenge in signal-harsh environments. Learning from the integrated system between Global Navigation Satellite System (GNSS) and INS and based on a multiple hypothesis solution separation (MHSS) algorithm, we have developed an improved CRAIM algorithm, which combines Beidou Navigation Satellite System (BDS) and INS to offer integrity information for real-time kinematic relative positioning between two moving platforms in challenging environments. To achieve more robust and efficient fault detection and exclusion (FDE) results, an algorithm of observation-domain outlier detection combined with MHSS (OOD-MHSS) is also proposed. In this algorithm, the kinematic relative positioning method with INS addition is performed first, then, based on double-difference (DD) phase observations with known integer ambiguities and the OOD-MHSS method, the integrity monitoring information can be provided for the kinematic relative positioning between two moving platforms. To assess the performance of the OOD-MHSS and the improved CRAIM algorithm, a series of kinematic experiments between different platforms was analyzed and discussed. The results show that the improved CRAIM algorithm can perform effective FDE and provide reliable integrity information, which offers centimeter-level relative position solutions with decimeter-level protection levels (PLs) (integrity budget: ${1\times 10}^{-5}$/h). Both observation outlier detection and INS improve the continuity and availability of kinematic relative positioning and the PLs in horizontal and vertical directions. The PL values have been improved by up to 24.3%, and availability has reached 96.67% in harsh urban areas. This is of great significance for applications requiring higher precision and integrity in kinematic relative positioning. Full article

In deformation monitoring, the severe GNSS multipath caused by reflective surfaces can significantly degrade positioning accuracy. However, traditional multipath mitigation methods often assume strong day-to-day repeatability of residual errors, which is not always valid in complex monitoring environments. We propose a novel GNSS multipath correction approach that leverages multi-day post-fit residual data and principal component analysis to extract stable multipath signals, integrating them into an enhanced spatial repeatability multipath correction model. This method can effectively isolate true multipath errors, even under conditions of weak inter-day repeatability. Experimental results from a dam monitoring network demonstrate that the proposed method reduces the root mean square (RMS) error of single-day kinematic positioning by about 1.8 mm, 2.4 mm, and 6.7 mm in the East, North, and Up components, respectively. For static positioning solutions over 1 h, 2 h, and 4 h sessions, the RMS in East, North, and Up is reduced by approximately 40% on average. After correction, 2 h sessions achieve ~1.1 mm horizontal and ~3.0 mm vertical accuracy, while 4 h sessions reach ~0.9 mm horizontal and ~2.5 mm vertical accuracy. These improvements confirm that the proposed method effectively mitigates multipath effects and meets the high-precision requirements of deformation monitoring. Full article

This paper introduces a robust and efficient Localized Spline-based Path-Planning (LSPP) algorithm designed to enhance autonomous vehicle navigation on highways. The LSPP approach prioritizes smooth maneuvering, obstacle avoidance, passenger comfort, and adherence to road constraints, including lane boundaries, through optimized trajectory generation using quintic spline functions and a dynamic speed profile. Leveraging real-time data from the vehicle’s sensor fusion module, the LSPP algorithm accurately interprets the positions of surrounding vehicles and obstacles, creating a safe, dynamically feasible path that is relayed to the Model Predictive Control (MPC) track-following module for precise execution. The theoretical distinction of LSPP lies in its modular integration of: (1) a finite state machine (FSM)-based decision-making layer that selects maneuver-specific goal states (e.g., keep lane, change lane left/right); (2) quintic spline optimization to generate smooth, jerk-minimized, and kinematically consistent trajectories; (3) a multi-objective cost evaluation framework that ranks competing paths according to safety, comfort, and efficiency; and (4) a closed-loop MPC controller to ensure real-time trajectory execution with robustness. Extensive simulations conducted in diverse highway scenarios and traffic conditions demonstrate LSPP’s effectiveness in delivering smooth, safe, and computationally efficient trajectories. Results show consistent improvements in lane-keeping accuracy, collision avoidance, enhanced materials wear performance, and planning responsiveness compared to traditional path-planning methods. These findings confirm LSPP’s potential as a practical and high-performance solution for autonomous highway driving. Full article

Inspection, maintenance, and repair (IMR) operations on underwater infrastructure remain costly and time-intensive because fully teleoperated remote operated vehicle s(ROVs) lack the range and dexterity necessary for precise cooperative underwater manipulation, and the alternative of using professional divers is ruled out due to the risk involved. This work presents and experimentally validates an autonomous, dual-I-AUV (Intervention–Autonomous Underwater Vehicle) system capable of assembling rigid pipeline segments through coordinated actions in a confined underwater workspace. The first I-AUV is a Girona 500 (4-DoF vehicle motion, pitch and roll stable) fitted with multiple payload cameras and a 6-DoF Reach Bravo 7 arm, giving the vehicle 10 total DoF. The second I-AUV is a BlueROV2 Heavy equipped with a Reach Alpha 5 arm, likewise yielding 10 DoF. The workflow comprises (i) detection and grasping of a coupler pipe section, (ii) synchronized teleoperation to an assembly start pose, and (iii) assembly using a kinematic controller that exploits the Girona 500’s full 10 DoF, while the BlueROV2 holds position and orientation to stabilize the workspace. Validation took place in a 12 m × 8 m × 5 m water tank. Results show that the paired I-AUVs can autonomously perform precision pipeline assembly in real water conditions, representing a significant step toward fully automated subsea construction and maintenance. Full article

Achieving high-precision positioning operations in a small space was of great significance in aerospace, biomedical, and other fields. In order to obtain smaller displacements with higher accuracy, this paper focused on the design, optimization, and performance analysis of a two-stage reduction micro-drive mechanism. Using the principle of lever and the principle of balanced additional force, a two-stage reduction micro-motion mechanism without parasitic motion and non-motion directional force was designed, and the structure optimization of the mechanism was completed by employing the particle swarm algorithm. A finite element analysis was conducted to assess the strength, dynamics, and kinematic properties of the mechanism. Experimental methods were also employed to analyze its dynamic and kinematic properties. The analysis results demonstrated that the mechanism met the design requirements in terms of strength and dynamic properties, with a maximum error of 9.02% and a maximum kinematic error of 0.0267 μm. The achieved reduction ratio was 24.73:1. These results indicated that the mechanism possesses excellent strength and dynamic performance, a large reduction ratio, high motion accuracy, and good linearity. This paper contributes significantly to the advancement of research in precision mechanical motion and micro-drive mechanisms. Full article

This study presented an autonomous shield-cutting end-effector for maize surrounding weeding (SEMSW), addressing the challenges of the low weed removal rate (WRR) and high seedling damage rate (SDR) in northern China’s 3–5 leaf stage maize. The SEMSW integrated seedling positioning, robotic arm control, and precision weeding functionalities: a seedling positioning sensor identified maize seedlings and weeds, guiding XYZ translational motions to align the robotic arm. The seedling-shielding anti-cutting mechanism (SAM) enclosed crop stems, while the contour-adaptive weeding mechanism (CWM) activated two-stage retractable blades (TRWBs) for inter/intra-row weeding operations. The following key design parameters were determined: 150 mm inner diameter for the seedling-shielding disc; 30 mm minimum inscribed-circle for retractable clamping units (RCUs); 40 mm ground clearance for SAM; 170 mm shielding height; and 100 mm minimum inscribed-circle diameter for the TRWB. Mathematical optimization defined the shape-following weeding cam (SWC) contour and TRWB dimensional chain. Kinematic/dynamic models were introduced alongside an adaptive sliding mode controller, ensuring lateral translation error convergence. A YOLOv8 model achieved 0.951 precision, 0.95 mAP50, and 0.819 mAP50-95, striking a balance between detection accuracy and localization precision. Field trials of the prototype showed 88.3% WRR and 2.2% SDR, meeting northern China’s agronomic standards. Full article

The precise point positioning (PPP) method in GNSS is based on the processing of undifferenced phase observations. For long static sessions, this method provides results characterized by accuracies better than one centimeter, and has become a standard practice in the processing of geodetic permanent stations data. However, a drawback of the PPP method is its slow convergence, which results from the necessity of jointly estimating the coordinates and the initial phase ambiguities. This poses a challenge for very short sessions or kinematic applications. The introduction of new satellites in Low Earth Orbits (LEO) that provide phase observations for positioning, such as those currently provided by GNSS constellations, has the potential to radically improve this scenario. In this work, a preliminary case study is discussed. For a given day, two configurations are analyzed: the first considers only the GNSS satellites currently in operation, while the second includes a simulated constellation of LEO satellites. For both configurations, the geometric quality of a PPP solution is evaluated over different session lengths throughout the day. The adopted quality index is the trace of the cofactor matrix of the estimated coordinates, commonly referred to as the position dilution of precision (PDOP). The simulated LEO constellation demonstrates the capability to enhance positioning performance, particularly under conditions of good sky visibility, where the time needed to obtain a reliable solution decreases significantly. Furthermore, even in scenarios with limited satellite visibility, the inclusion of LEO satellites helps to reduce PDOP values and overall convergence time. Full article