Trajectory Path Research Articles

Investigating the influence of various tool path trajectories on the anisotropic behaviour of bulk NiCrMo-3 alloy fabrication by WADED process

This study delves into the Wire Arc directed energy Deposition (WADED) process, exploring the impact of four tool path trajectories designated as bidirectional contour (S1), bidirectional spiral (S2), Fermat's spiral (S3), and Fermat's zigzag (S4) on the deposition of a regular cubic solid structure of NiCrMo-3 alloy. The research focuses on understanding how these trajectories influence both microstructural heterogeneity and anisotropic mechanical response. It has been shown that the WADED processed NiCrMo-3 alloy displays prominent dendritic microstructure, with varying dendritic arm spacing and growth orientation angle across S1 to S4. S2 and S4 favours more columnar to equiaxed transition due to relatively lower cooling rates and G/R values along with higher extent of Laves and carbide precipitation compared to S1 and S3. Texture analysis reveals S1 and S3 exhibit a predominant cube {001} 〈100〉 texture, while S2 transitions to Goss {110} 〈001〉 texture, and S4 displays a more random texture with higher intensity in copper {112} 〈111〉 and brass {110} 〈1−12〉. Microhardness exhibited slight anisotropy (within 3%), with S4 (227.77HV0.5) having the highest microhardness. The YS and UTS were lower in the build direction (〈001〉 S) compared to the horizontal direction (<001 > S) in all samples. S2 showcased maximum YS (334.5 MPa) and UTS (655 MPa) values while minimum anisotropy (≤2%) was observed in S4 along with comparable YS (330 MPa) and UTS (645.5 MPa).

Comparative Analysis of Integrated Filtering Methods Using UWB Localization in Indoor Environment.

This research delves into advancing an ultra-wideband (UWB) localization system through the integration of filtering technologies (moving average (MVG), Kalman filter (KF), extended Kalman filter (EKF)) with a low-pass filter (LPF). We investigated new approaches to enhance the precision and reduce noise of the current filtering methods-MVG, KF, and EKF. Using a TurtleBot robotic platform with a camera, our research thoroughly examines the UWB system in various trajectory situations (square, circular, and free paths with 2 m, 2.2 m, and 5 m distances). Particularly in the square path trajectory with the lowest root mean square error (RMSE) values (40.22 mm on the X axis, and 78.71 mm on the Y axis), the extended Kalman filter with low-pass filter (EKF + LPF) shows notable accuracy. This filter stands out among the others. Furthermore, we find that integrated method using LPF outperforms MVG, KF, and EKF consistently, reducing the mean absolute error (MAE) to 3.39% for square paths, 4.21% for circular paths, and 6.16% for free paths. This study highlights the effectiveness of EKF + LPF for accurate indoor localization for UWB systems.

Optimizing Energy Consumption of Industrial Robots with Model-Based Layout Design

The paper describes the development of an optimization model for the layout of an industrial robot relative to known locations of served machines and operations to be performed. Robotized material handling applications, defined by trajectories (paths, speed profiles) and final points, are considered in this research. An energy-monitoring framework set up by joint velocities provides input data that are fed to the optimization model. The physical placement of the robot base stands for the decisional variables, while the objective function is represented by the total distance covered by individual joints along established task routes transposed into energy consumption. The values of the decisional variables are restricted by trajectory constraints (waypoints on paths), joint operating values and link dimensions. Modelling technique and practical results using the Microsoft Solver optimization tool from Excel for Microsoft 365, Version 2312 are reported for SCARA-type robots. The performance of the optimization model is compared with actual measurements of consumed energy on an Adept Cobra S600 SCARA robot.

Qualitative analysis and chaotic behavior of respiratory syncytial virus infection in human with fractional operator

Respiratory syncytial virus (RSV) is the cause of lung infection, nose, throat, and breathing issues in a population of constant humans with super-spreading infected dynamics transmission in society. This research emphasizes on examining a sustainable fractional derivative-based approach to the dynamics of this infectious disease. We proposed a fractional order to establish a set of fractional differential equations (FDEs) for the time-fractional order RSV model. The equilibrium analysis confirmed the existence and uniqueness of our proposed model solution. Both sensitivity and qualitative analysis were employed to study the fractional order. We explored the Ulam–Hyres stability of the model through functional analysis theory. To study the influence of the fractional operator and illustrate the societal implications of RSV, we employed a two-step Lagrange polynomial represented in the generalized form of the Power–Law kernel. Also, the fractional order RSV model is demonstrated with chaotic behaviors which shows the trajectory path in a stable region of the compartments. Such a study will aid in the understanding of RSV behavior and the development of prevention strategies for those who are affected. Our numerical simulations show that fractional order dynamic modeling is an excellent and suitable mathematical modeling technique for creating and researching infectious disease models.

Quantifying effects of long-range transport of NO2 over Delhi using back trajectories and satellite data

Abstract. Exposure to air pollution is a leading public health risk factor in India, especially over densely populated Delhi and the surrounding Indo-Gangetic Plain. During the post-monsoon seasons, the prevailing north-westerly winds are known to influence aerosol pollution events in Delhi by advecting pollutants from agricultural fires as well as from local sources. Here we investigate the year-round impact of meteorology on gaseous nitrogen oxides (NOx=NO+NO2). We use bottom-up NOx emission inventories (anthropogenic and fire) and high-resolution satellite measurement based tropospheric column NO2 (TCNO2) data, from S5P aboard TROPOMI, alongside a back-trajectory model (ROTRAJ) to investigate the balance of local and external sources influencing air pollution changes in Delhi, with a focus on different emissions sectors. Our analysis shows that accumulated emissions (i.e. integrated along the trajectory path, allowing for chemical loss) are highest under westerly, north-westerly and northerly flow during pre-monsoon (February–May) and post-monsoon (October–February) seasons. According to this analysis, during the pre-monsoon season, the highest accumulated satellite TCNO2 trajectories come from the east and north-west of Delhi. TCNO2 is elevated within Delhi and the Indo-Gangetic Plain (IGP) to the east of city. The accumulated NOx emission trajectories indicate that the transport and industry sectors together account for more than 80 % of the total accumulated emissions, which are dominated by local sources (&gt;70 %) under easterly winds and north-westerly winds. The high accumulated emissions estimated during the pre-monsoon season under north-westerly wind directions are likely to be driven by high NOx emissions locally and in nearby regions (since NOx lifetime is reduced and the boundary layer is relatively deeper in this season). During the post-monsoon season the highest accumulated satellite TCNO2 trajectories are advected from Punjab and Haryana, where satellite TCNO2 is elevated, indicating the potential for the long-range transport of agricultural burning emissions to Delhi. However, accumulated NOx emissions indicate local (70 %) emissions from the transport sector are the largest contributor to the total accumulated emissions. High local emissions, coupled with a relatively long NOx atmospheric lifetime and shallow boundary layer, aid the build-up of emissions locally and along the trajectory path. This indicates the possibility that fire emissions datasets may not capture emissions from agricultural waste burning in the north-west sufficiently to accurately quantify their influence on Delhi air quality (AQ). Analysis of daily ground-based NO2 observations indicates that high-pollution episodes (&gt;90th percentile) occur predominantly in the post-monsoon season, and more than 75 % of high-pollution events are primarily caused by local sources. But there is also a considerable influence from non-local (30 %) emissions from the transport sector during the post-monsoon season. Overall, we find that in the post-monsoon season, there is substantial accumulation of high local NOx emissions from the transport sector (70 % of total emissions, 70 % local), alongside the import of NOx pollution into Delhi (30 % non-local). This work indicates that both high local NOx emissions from the transport sector and the advection of highly polluted air originating from outside Delhi are of concern for the population. As a result, air quality mitigation strategies need to be adopted not only in Delhi but in the surrounding regions to successfully control this issue. In addition, our analysis suggests that the largest benefits to Delhi NOx air quality would be seen with targeted reductions in emissions from the transport and agricultural waste burning sectors, particularly during the post-monsoon season.

The Spatio-Temporal Distribution Characteristics of Carbon Dioxide Derived from the Trajectory Mapping of Ground Observation Network Data in Shanxi Province, One of China’s Largest Emission Regions

In this study, the trajectory mapping domain-filling technology, which can provide more reliable statistical estimates of long-lived gas concentrations in a broader geographical area based on limited station data, is used to map the CO2 concentration data of six ground observation stations to the entire Shanxi Province. The technology combines a dynamical model of the atmosphere with trace gas observations, combining forward and backward trajectories to greatly expand the information on long-lived CO2 gas concentrations over a trajectory path. The mapped results show good agreement with the observation results, which reveals the generalizability of the trajectory mapping domain-filling technology. The results show that the spatio-temporal distribution characteristics of CO2 concentration in the entire Shanxi region is significant: during the five years, the provincial average CO2 concentration exhibits an overall increasing trend. The CO2 concentration increases from the north to the south across the province. Influenced by the economic growth rate and COVID-19, there are differences in the annual variation characteristics of the CO2 concentration across the entire province, with the highest year-on-year growth in 2019 and a year-on-year decrease in 2020. The increasing rate of the CO2 concentration in the northern low-value areas is faster than that in the southern high-value areas. Overall, there is a decreasing trend in the CO2 concentration growth from the north to the south in the entire province. There are seasonal differences in the CO2 concentration distribution across the entire province. The CO2 concentration and amplitude are higher in autumn and winter than they are in spring and summer. This study can provide scientific support and methodological reference for the spatio-temporal distribution characteristics analysis of GHGs at the provincial–regional scale, as well as at the national and global scales.

Mathematical modelling and PID control system implementation for quadcopter frame Tarot FY650

PurposeThe purpose of this study is to find the suitable trajectory path of the Numerical model of the Quadcopter. Quadcopters are widely used in various applications due to their compact size and ease of assembly. Because they are quite unstable, autonomous control systems would be used to overcome this problem. Modelling autonomous control is predominant as the research scope faces challenges because of its highly non-linear, multivariable system with 6 degree of freedom.Design/methodology/approachQuadcopters with antonym systems can operate in an unknown environment by overcoming unexpected disturbances. The first objective when designing such a system is to design an accurate mathematical model to describe the dynamics of the system. Newton’s law of motion was used to build the mathematical model of the system.FindingsEstablishment of the mathematical model and the physics behind a four propeller drone for the frame TAROT 650 carbon was done. Simulink model was developed based on the mathematical model for simulating the complete dynamics of the drone as well as location and gusts were included to check the stability.Originality/valueThe control response of the system was simulated numerically results are discussed. The trajectory path was found. The phases with their own parameters can be used to implement the mathematical model for another type of quadcopter model and achieve quick development.

Pedicle screw path planning for multi-level vertebral fixation.

For the spinal internal fixation procedures, connecting rods to the pedicle screws are commonly used in all spinal segments from the cervical to sacral spine. So far, we have only seen single vertebral screw trajectory planning methods in literatures. Joint screw placements in multi-level vertebrae with the constraint of an ipsilateral connecting rod are not considered. In this paper, a screw trajectory planning method that considers screw-rod joint system with both multi-level vertebral constraints and individual vertebral safety tolerance areproposed. The proposed method addresses three challenging constraints jointly for multi-level vertebral fixation with pedicle screws. First, a cylindrical screw safe passage model is suggested instead of a unique mathematical optimal trajectory for a single pedicle. Second, the flexible screw cap accessibility model is also included. Third, the connecting rod is modeled to accommodate the spine contour and support the needed gripping capacity. The retrospective clinical data of relative normal shape spines from Beijing Jishuitan hospital were used in the testing. The screw trajectories from the existing methods based on single vertebra and the proposed method based on multi-level vertebrae optimization are calculated andcompared. The results showed that the calculated screw placements by the proposed method can achieve 88% success rate without breaking the pedicle cortex and 100% in clinical class A quality (allow less than 2mm out of the pedicle cortex) compared to 86.1% and 99.1%, respectively, with the existing methods. Expert evaluation showed that the screw path trajectories and the connecting rod calculated by the new method satisfied the clinical implantationrequirements. The new screw planning approach that seeks an overall optimization for multi-level vertebral fixation is feasible and more advantageous for clinical use than the single vertebral approaches.

Assessing built microclimate with building group frontal projection maps: A sun-path-dependent deep transfer learning neural network approach

Climate conditions play a pivotal role in estimating buildings' energy consumption. For urban-scale building energy dynamic simulation, the typical meteorological year weather represents a universal climate condition for the entire city. However, the varying terrain roughness, environment conditions, human-built architectures jointly formulate a unique microclimate condition for each building. This study intends to develop flexible and reliable approach to assess a building's microclimate condition based on its surrounding environment morphological features. The proposed approach leverages deep transfer learning neural networks that combines seasonal sun path trajectories and front projection maps of building groups. To validate the proposed method, a case study was conducted on a campus environment with wireless environmental sensing systems. The findings of this study demonstrate that the projection matrices and sun path can improve the assessment methods solely based on the dynamic impacts of urban morphological features and can forecast microclimate dynamics for urban-scale building energy simulation.

The Fukuda stepping test is associated with various patterns of displacement

Abstract Objective: This study was to characterize individual path trajectories during the 50-step Fukuda stepping test (FST) and to determine the impact of step height on these trajectories. Methods: Kinematic data from the shoulders and feet were recorded in 12 young healthy adults while performing the FST at a comfortable step height (CoStep) and while stepping with lifting the knees high (HiStep). Path trajectories were depicted by step-by-step displacement of the right and left toes, and body rotation was obtained from the rotation of the shoulders. The effect of step height on the distance traveled and the final anteroposterior (AP) and mediolateral displacements and rotation were determined with paired t-tests. Results: Path trajectories were composed of various combinations of forward displacement, lateral deviation, and rotation to the left or right. In comparison with CoStep, the mean AP displacement and mean distance traveled were significantly shorter in HiStep (P &lt; 0.05) while the mean rotation was significantly larger in HiStep (P &lt; 0.05), but the displacement patterns were not modified. Conclusion: We found that the path trajectories vary greatly among participants, and that step height has a significant impact on the magnitude of displacements and rotation during the FST.

Robot kinematics analysis and trajectory planning based on artificial potential field method

Abstract In order for a robot to complete a given task, it must first be made to autonomously reach a specified target location, so optimizing the robot path trajectory planning is a prerequisite for the use of robots. In this paper, for the two problems of the traditional artificial potential field method of target unreachable and local optimization, we first improve the repulsive potential field function so that the robot’s gravitational force and repulsive force are zero at the target position and then construct the robot kinematic analytical model by setting the virtual target point away from the local minimum value point. On this basis, the improved algorithm is used to compare simulation experiments in three environments with the real trajectory planning test in the showroom. In the climate “near the obstacle of the target point” and the pure U-shaped area environment, the robot of the traditional APF algorithm cannot reach the target point. However, the algorithm in this paper reaches the target point in all three environments, with a time taken of only 21, 33, and 42 seconds, respectively. In the real trajectory planning of the showroom, the improved algorithm in this paper reaches the target point quickly and accurately, with a total path trajectory length of 77.7835m, a time of 43 seconds, and a total turning angle of 793°. This paper provides an effective method for planning robot path trajectories in a complex and variable environment.

ST-TrackNet: A Multiple-Object Tracking Network Using Spatio-Temporal Information

Multiple-object tracking (MOT) is a crucial component in autonomous driving systems. However, inaccurate object detection is always the bottleneck for MOT. Most detectors are not designed to take the temporal information across consecutive frames into consideration. To take advantage of such information, we design a novel data representation, the spatio-temporal (ST) map, which collects a batch of detection results spatio-temporally, and we train a novel network, ST-TrackNet, to assign predicted track IDs to each positive detection across a sequence. With our ST map detection fed into the tracker, the correlation of objects between adjacent frames becomes prominent, which improves the performance of the tracker in the data association step. Moreover, the long-term trajectory in a sequence also helps to refine the detection results. We train and evaluate our network on the KITTI dataset, a CARLA simulation dataset, and a dataset recorded in a factory environment. Our approach generally achieves superior performance over the state-of-the-art. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —We investigate the MOT problem in this paper. A spatio-temporal pipeline is proposed to provide a solution to this problem. Object detection results produced by off-the-shelf object detectors are used to form the proposed ST maps. In low signal-to-noise ratio (SNR) situations, our proposed framework can achieve more accurate and robust tracking results with more false-positives. Due to the simplicity and modular design of our framework, it can be applied directly after the detection stage to achieve the online tracking task. The proposed method is evaluated on several datasets, and the experimental results demonstrate its effectiveness. Our method can also be used for other autonomous driving applications, such as path planning and trajectory prediction.

Path Planning and Trajectory Tracking Control of Quadrotor Dragging System in Marine Environment

Path Planning and Trajectory Tracking Control of Quadrotor Dragging System in Marine Environment

A multi-electrode electroelastomer cylindrical actuator for multimodal locomotion and its modeling

Dielectric elastomer actuators (DEAs) have received widespread attention in human-robot interaction and biomedical engineering due to their merits of large deformation, fast response, and excellent biocompatibility. The cylindrical DEA is one of the preferred structures, combining flexibility and compactness. However, the inherent non-linear behavior between the voltage and the actuator makes predicting the output performance during the design process difficult and encumbers its application, especially for multiple degrees of freedom. Herein, a dielectric elastomer spring-roll bending actuator capable of multimodal spatial locomotion using three pairs of electrodes is proposed in this paper. The electromechanical coupling behavior of the actuator under unidirectional bending deformation is described by analyzing the stress process between the spring and the film. The system's damping and excitation frequency relationship is obtained via a semi-analytical method. The experimental results validate the actuator design and show good consistency with the theoretical model. A prototype soft robot capable of running and turning is successfully demonstrated. The actuator also achieves various spatial trajectory paths via the cooperation between electrodes. Thus, the proposed scheme and modeling shed new light on the design and control of dielectric elastomers for multimodal deformation.

Melt pool instability in surface polishing by laser remelting: preliminary analysis and online monitoring with K-means clustering

Surface polishing by laser remelting (SP-LRM) is a novel, versatile, high-speed, and low-cost advanced manufacturing technology for producing high-quality surface finishes. The process utilizes a high-power laser that delivers a large amount of instantaneous energy melting a superficial thin layer of material to a molten state. This allows the melt pool to flow driven by thermocapillary and surface tension forces. The target of the process is to melt, reallocate, and resolidify surface peaks into valleys in order to yield a low surface roughness (Sa). SP-LRM, complexity arises from instabilities occurring during laser-material interactions, resulting from non-linear thermodynamics, initial surface topography, overheating, abrupt changes in laser path trajectories involving acceleration and deceleration, and various other factors. These process instabilities significantly affect the attainment of a desired smooth final surface. Presently, the identification of anomalies resulting from a specific set of laser parameters in laser remelting (LRM) is performed offline by assessing the surface topography of LRM using optical profilometer and correlating it with surface non-uniformities that are indicative of process instabilities. This study streamlines the anomaly detection process and identifies the presence of irregularities using an unsupervised clustering machine learning (ML) technique, specifically K-means clustering. During the laser remelting (LRM) process, a high-speed near-infrared (NIR) camera captures relative thermal emission images, which are then classified into a minimum of three clusters using the K-means algorithm. These clusters correspond to positive and negative axial laser beam positions, indicating shifts in the laser spot on the image, and the stability states of laser-material interactions. Preliminary findings show promising results in the employment of artificial intelligence (AI) to enhance LRM as a conventional industrial technology for polishing and structuring tooling surfaces.

Specular Path Generation and Near-Reflective Diffraction in Interactive Acoustical Simulations.

Most systems for simulating sound propagation in a virtual environment for interactive applications use ray- or path-based models of sound. With these models, the "early" (low-order) specular reflection paths play a key role in defining the "sound" of the environment. However, the wave nature of sound, and the fact that smooth objects are approximated by triangle meshes, pose challenges for creating realistic approximations of the reflection results. Existing methods which produce accurate results are too slow to be used in most interactive applications with dynamic scenes. This paper presents a method for reflections modeling called spatially sampled near-reflective diffraction (SSNRD), based on an existing approximate diffraction model, Volumetric Diffraction and Transmission (VDaT). The SSNRD model addresses the challenges mentioned above, produces results accurate to within 1-2 dB on average compared to edge diffraction, and is fast enough to generate thousands of paths in a few milliseconds in large scenes. This method encompasses scene geometry processing, path trajectory generation, spatial sampling for diffraction modeling, and a small deep neural network (DNN) to produce the final response of each path. All steps of the method are GPU-accelerated, and NVIDIA RTX real-time ray tracing hardware is used for spatial computing tasks beyond just traditional ray tracing.

Semantic-Region Aware Model Predictive Trajectory Tracking in Automated Guided Vehicles

Autonomous routing through curved path for point-to-point mobile robot is a well-established challenging problem in the area of intelligent path navigation. Point-to-point collision-free locomotion of AGV (Automated Guided Vehicle) requires precise differentiation of movable path from other environmental visual noise. As these path navigating depend on various visual cues and precepts, naturally the semantic meaning of path in contrast to other closely available objects on the visual window requires significant amount of understanding of the input from precepts. In this paper, it is experimentally established that semantic segmentation has proved to be a concise method in making comparative better segregation of paths from midway structured obstacles that are with high computational cost. The claims are also evident from surveyed works. The challenge of reducing high computational performance with efficient segmentation of movable area has been addressed in this research work with a focus on deriving better filtered accessible paths with a degree of algorithmic confidence. Further, this segmented result is taken for curved trajectory tracking by the AGV. A systematic percept mediated deep based Semantic-region Aware Model Predictive Trajectory Tracking(SAMP-TT)Algorithm has been proposed to solve the problem of precise obstacle detection on curved path trajectories. RGB (Red, Green Blue) image of outdoor multi-sectioned area has been collected by Intel Realsense D455 which worked as input feed to the respective neural network models. The performance of the proposed technique has been benchmarked with existing segmentation models including ENET (Efficient Neural Network), U-NET to confirm the considerable reliability and accuracy of the presented algorithm. It has been followed by the state-of-art comparative visual result analysis of different taken models with the proposed architecture.

Optimization of Horizontal Well Location and Completion to Improve Oil Recovery for an Iraqi Field

Exploitation of mature oil fields around the world has forced researchers to develop new ways to optimize reservoir performance from such reservoirs. To achieve that, drilling horizontal wells is an effective method. The effectiveness of this kind of wells is to increase oil withdrawal. The objective of this study is to optimize the location, design, and completion of a new horizontal well as an oil producer to improve oil recovery in a real field located in Iraq. “A” is an oil and gas condensate field located in the Northeast of Iraq. From field production history, it is realized the difficulty to control gas and water production in this kind of complex carbonate reservoir with vertical producer wells. In this study, a horizontal well design with multi-stage completion is studied and proposed to find optimal oil recovery in the southeast region of the selected field. A bulk oil well sector model is used to simulate the fluid flow of a single-porosity/single-permeability model. Then, a sensitivity analysis has been run to optimize; the well trajectory path, different scenarios on well oil and water production potential, and well completion design. The result of the well sector simulation indicates that the well trajectory with an Azimuth of 89 degrees and with a multi-stage completion design has better production performance under water production constraints. Optimum oil production rates of 1000 to 2000 STB/day, as delaying and controlling early gas and water production challenges is achieved.

Automated Flight Technology for Integral Path Planning and Trajectory Tracking of the UAV

In view of the problem that path planning and trajectory tracking are rarely solved simultaneously in the current research, which hinders their practical implementation, this paper focuses on enhancing the autonomous flight planning capability of unmanned aerial vehicles (UAVs) by investigating integrated path planning and trajectory tracking technologies. The autonomous flight process is divided into two sub-problems: waypoint designing/optimizing and waypoint tracking. Firstly, an improved DB-RRT* algorithm is proposed for waypoint planning to make the algorithm have higher planning efficiency, better optimization results, and overcome the defects of accidental and low reliability of single RRT* planning results. Secondly, the scheme of “offline design + online flight” is adopted to lead the UAV to fly online according to the waypoints’ instructions by using the sliding mode guidance based on angle constraint with finite-time convergence so that it can fly to the destination autonomously. In order to check the performance of the proposed algorithm, a variety of simulations are conducted to verify the feasibility of the proposed algorithm.

Path Planning and Trajectory Tracking for Autonomous Obstacle Avoidance in Automated Guided Vehicles at Automated Terminals

During the operation of automated guided vehicles (AGVs) at automated terminals, the occurrence of conflicts and deadlocks will undoubtedly increase the ineffective waiting time of AGVs, so there is an urgent need for path planning and tracking control schemes for autonomous obstacle avoidance in AGVs. An innovative AGV autonomous obstacle avoidance path planning and trajectory tracking control scheme is proposed, effectively considering static and dynamic obstacles. This involves establishing three potential fields that reflect the influences of obstacles, lane lines, and velocities. These potential fields are incorporated into an optimized model predictive control (MPC) cost function, leveraging artificial potential fields to ensure effective obstacle avoidance. To enhance this system’s capability, a fuzzy logic system is designed to dynamically adjust the weight coefficients of the hybrid artificial potential field model predictive controller, strengthening the autonomous obstacle avoidance capabilities of the AGVs. The tracking control scheme includes a fuzzy linear quadratic regulator based on a fuzzy logic system, a dynamics model as a lateral controller, and a PI controller as a longitudinal tracker to track the pre-set trajectory and speed autonomously. Multi-scenario simulation tests demonstrate the effectiveness and rationality of our autonomous obstacle-avoidance control scheme.