Accurate Path Research Articles

Analysis of Innovation Paths Big Data Mining to Big Data Algorithm of Business Administration

In the process of enterprise management, there are some problems such as poor accuracy and long selection time of computer science innovation path, which seriously affect the effective selection of computer science path innovation. Based on a big data mining method, this paper analyzes the path innovation of computer science from three dimensions, constructs the path set of path innovation by least dichotomy, and obtains the optimal innovation path by derivation. Then, the maximum likelihood theory is used to calculate the innovation path and compared it with the previous path innovation methods, comparing the accuracy and calculation time of different innovation paths. MATLAB simulation results show that the big data mining method can improve the accuracy and comprehensiveness of innovation path selection, reaching more than 90%, and control the selection time of the innovation path within 25 seconds, and the overall result is better than the previous path innovation methods. Therefore, the big data mining method can improve the accuracy of computer science innovation path selection and meet the needs of computer science path innovation in business administration. However, in the research of big data mining methods, this paper ignores the analysis of multi-path innovation, which leads to insufficient research depth. In the future, it will further analyze multi-path innovation.

Integrated Decision Making and Motion Control for Autonomous Emergency Avoidance Based on Driving Primitives Transition

Emergency avoidance is an aggressive maneuver with high collision risk. This paper presents an integrated decision making and motion control framework to achieve emergency avoidance in complex driving scenarios. This is realized by combining reasonable decision making based on driving primitives transition and efficient motion control under critical driving conditions. For decision making, the driving primitives including braking, lane changing, and accelerating are generalized to perform a complete emergency avoidance maneuver through the Minimum Safety Spacing analysis, which is numerically executed as a mapping function of the host vehicle's speed and road adhesion coefficient. For motion control, the quintic polynomial-based path planning is formulated as solving a minimum lane changing duration problem. Moreover, the Linear Time-Varying Model Predictive Control combined with the Direct Yaw-moment Control is put forward to realize accurate path tracking control for emergency avoidance. The effectiveness of the proposed scheme is examined under various scenarios based on comprehensive Hardware-in-Loop tests. The results show that the proposed framework can realize timely emergency avoidance with guaranteed vehicle dynamics stability based on proper decision-making and accurate path tracking control.

Online Learning-Informed Feedforward-Feedback Controller Synthesis for Path Tracking of Autonomous Vehicles

High-performance path tracking is a key technology for autonomous vehicles. Feedforward-feedback control architectures are suitable for accurate path tracking with adequate margins of stability. For system modelling in the feedforward component, the learning-based method has been proven to be a promising approach owing to its model-free framework. However, the offline-learned data model trained with collection data is confined by its feature space, resulting in insufficient generalization. As a solution, in this study, we introduce an online learning network – the recurrent high-order neural network (RHONN) – to characterize vehicle behaviors. The RHONN is used to feature vehicle behaviors in a timely manner with a high fidelity and flexible form. The equilibrium at the preview point on the desired path is found based on the online-identified RHONN model, and its induced steering angle is taken as the feedforward command. For the feedback steering controller, the preview point position-based control law incorporating the steady vehicle sideslip angle is adopted to enhance the stability performance. Finally, in the CarSim/Simulink environment, the performance of the designed RHONN-informed feedforward and feedback controller is validated in two typical scenarios – double-lane change and single-turn. The validation results reveal that the proposed approach offers better tracking accuracy in linear and nonlinear regions than other techniques. More notably, the average execution time (3.55 ms) is less than the sampling frequency of the controller (50 ms), which further confirms the applicability and efficiency of the proposed approach.

Parallel unambiguous generalized phase-shifting and T-spline fitting algorithms for optical micro-structured surface 3D topography metrology.

3D topography metrology of optical micro-structured surfaces is critical for controlled manufacturing and evaluation of optical properties. Coherence scanning interferometry technology has significant advantages for measuring optical micro-structured surfaces. However, the current research faces difficulties of designing high accuracy and efficient phase shifting, and characterization algorithms for optical micro-structured surface 3D topography metrology. In this paper, parallel unambiguous generalized phase-shifting and T-spline fitting algorithms are proposed. To avoid phase ambiguity and improve the accuracy of the phase-shifting algorithm, the zero-order fringe is determined by the iterative envelope fitting with Newton's method, and the accurate zero optical path difference is determined by a generalized phase-shifting algorithm. In particular, the calculation procedures of the multithreading iterative envelope fitting with Newton's method and generalized phase shifting are optimized with the graphics processing unit-Compute Unified Device Architecture kernel function. Additionally, to fit the base form of optical micro-structured surfaces and characterize the surface texture and roughness, an effective T-spline fitting algorithm is proposed by optimizing the preimage of the T-mesh with image quadtree decomposition. Experimental results show that the surface reconstruction of optical micro-structured surfaces using the proposed algorithm is more accurate, and the efficiency is 10 times higher than that of current algorithms; the time of the surface reconstruction is less than 1s. Compared with the current B-spline method, the accuracy of roughness characterization using the proposed T-spline algorithm is improved by more than 10%.

Path planning of patrol robot based on modified grey wolf optimizer

AbstractThe grey wolf optimizer (GWO) as a new intelligent optimization algorithm has been successfully applied in many fields because of its simple structure, few adjustment parameters and easy implementation. This paper mainly aims at the defects of GWO in path planning application, such as easily falling into local optimization, poor convergence and poor accuracy, and turn point grey wolf optimization (TPGWO) algorithm is proposed. First, the idea of cross-mutation and roulette is used to increase the initial population of GWO and improve the search range. At the same time, the convergence factor function is improved to become a nonlinear update. In the early stage, the search range is expanded, and in the later stage, the convergence speed is increased, while the parameters in the convergence factor function can be adjusted according to the number of obstacles and the map area to change the turning point of the function to improve the convergence speed and accuracy of the algorithm. The turning times and turning angles of the obtained path are added to the fitness function as penalty values to improve the path accuracy. The optimization test is carried out through 16 test functions, and the test results prove the convergence and robustness of TPGWO algorithm. Finally, the TPGWO algorithm is applied to the path planning of patrol robot for simulation experiments. Compared with the GWO algorithm and Particle Swarm Optimization, the simulation results show that the TPGWO algorithm has better convergence, stability and accuracy in the path planning of patrol robot.

Comparing different data processing methods for determining a Prony series from Dynamic Mechanical Analyzer frequency data

AbstractThis study focuses on using a dynamic mechanical analyzer (DMA) for obtaining frequency domain data suitable for constructing an accurate Prony series for a viscoelastic material. Polymethyl methacrylate (PMMA) specimens were used in each experiment, following the ASTM D4000 and ASTM D5023 standards. The experiments were completed using a frequency range of 1 to 100 Hz at multiple temperatures to capture the long‐term behavior via time–temperature superposition. Prony series were fit to the data from these experiments, and used to establish the time domain viscoelastic modulus of PMMA. The effect of the fitting function, material soak time, noise, and reference temperature selection were each investigated for their effect on the Prony series. The raw data may be processed via four paths, which are then compared for statistical error. The most accurate path resulted in a cumulative error of 2.1% for the storage modulus and 26% for the loss modulus. The impact of these errors on the tip displacement of a reference cantilever beam subject to axial loading for 15 years is a variation of over 25%.

Dynamic Drifting Control for General Path Tracking of Autonomous Vehicles

Taking full advantage of tire saturation in high sideslip drifting maneuvers can substantially improve the handling limits of autonomous vehicles. However, most singular motion control methods in conventional drifting controllers have been conducted, which cannot meet general driving needs. In this paper, a hierarchical dynamic drifting controller (HDDC) is designed under both drifting maneuvers and typical cornering maneuvers to uniformly track the general path within and beyond stability limits. Specifically, the HDDC includes the path tracking layer, vehicle motion control layer, and actuator regulating layer. The first layer is designed to achieve accurate path tracking and generate the desired states, and it is algorithmically adaptable and confirmed by MPC and LQR. The second layer is proposed to integrate drifting and typical cornering control by the dynamic drifting inverse model. The third layer can achieve the steering system and wheel speed control to satisfy corresponding commands. Ultimately, the real-time performance of the controller is verified by the ECU hardware-in-the-loop platform. The results with MPC-HDDC and LQR-HDDC show that the HDDC can achieve integrated control of drifting and typical cornering maneuvers and possess high tracking accuracy.

How local is the local inflation factor? Evidence from emerging European countries

We consider whether inflation is a ‘global phenomenon’ for European emerging market economies, as has been claimed for advanced or high-income countries. We find that a global inflation factor accounts for more than half of the variance in the national inflation rates, and show that forecasting models of national headline inflation rates that include global inflation factors generally produce more accurate path forecasts than Phillips curve-type models and models with local inflation factors. Our results are qualitatively unaffected by allowing for sparsity and non-linearity in the factor forecasting models. We also provide some insight as to why global factors are an important determinant of domestic inflation, by considering the country-level characteristics that tend to increase the importance of global factors for domestic inflation.

MPC-Based Collaborative Control of Sail and Rudder for Unmanned Sailboat

In the traditional motion control method of an unmanned sailboat, the sail and rudder are divided into two independent controllers. The sail is used to obtain the thrust and the rudder is used to adjust the yaw angle. The traditional control method does not consider the synergy between the two controllers and ignores the influence of the roll angle on sailing. It is easy for these methods to cause an excessive roll angle and large yaw angle error, which will weaken the safe navigation and accurate path tracking of an unmanned sailboat. This paper presents a collaborative control method of sail and rudder based on model predictive control. A four-degree-of-freedom kinematics and dynamics model of the unmanned sailboat considering roll angle was established, with the yaw angle and roll angle as the control objectives at the same time. The collaborative control method outputs sail angle and rudder angle simultaneously. By comparing the motion of this method and the separation control of sail and rudder under the same wind field conditions, it is verified that the collaborative control has better effects of yaw angle control and roll angle limitation and can obtain a more accurate path tracking effect.

Dynamic Path Planning for Multiple UAVs with Incomplete Information

To address the dynamic path planning for multiple UAVs using incomplete information, this paper studies real-time conflict detection and intelligent resolution methods. When the UAVs execute the task under the condition of incomplete information, the mission strategy of different UAVs may conflict with each other due to the difference in target, departure place, time and other factors. Based on the multi-agent deep deterministic policy gradient algorithm (MADDPG), we designed new global reward and partial local reward functions for the UAVs’ path planning and named the improved algorithm as a complex memory driver-MADDPG (CMD-MADDPG). Thus, the trained UAVs can effectively and efficiently perform path planning tasks in conditions of incomplete information (each UAV does not know its reward function and so on). Finally, the simulation verifies that the proposed method can realize fast and accurate dynamic path planning for multiple UAVs.

Model predictive control for path tracking in cable driven parallel robots with flexible cables: collocated vs. noncollocated control

This paper proposes a novel control scheme for precise path tracking control in cable driven parallel robots (CDPRs) with axially-flexible cables, with particular focus to the challenging case of cable suspended parallel robots (CSPRs). To handle model nonlinearities while ensuring small computational effort, a controller made by two sequential control actions is developed. The first term is a position-dependent, model predictive control (MPC) with embedded integrator to compute the optimal cable tensions ensuring accurate path tracking and fulfilling the feasibility constraints; bounds on the feasible tensions are also included. The second control term transforms the optimal tensions into the commanded motor torques, and hence currents, that are evaluated through the kinetostatic model of the electric motors used for winding and unwinding the cables. Control design is performed through the robot dynamics model, formulated with the assumption of rigid cables. Moreover, the proposed control strategy is presented in two different architectures, collocated control and noncollocated control. Flexibility is handled by penalizing large tension variations in the cost function adopted in the controller design, plus some hard constraints on the maximum tension derivatives. These features, together with the embedding of the integrator within the MPC formulation, ensure smooth control tensions that allow handling the axial flexibility of the cables, although it is not explicitly considered in the controller design.To assess the performances of the proposed control algorithm, a kinematically-determined robot with a suspended, lumped end-effector is simulated by also adopting very flexible cables. Additionally, a simplified dynamic model of the electrical dynamics and the sensor quantization are included to provide a realistic representation of the real environments. The results, together with the fair comparison with a benchmark, corroborate the effectiveness of the proposed approach, its robustness, and its feasibility in real-time controllers due to the wise reduction of the computational effort.

General temporal ghost imaging model with detection resolution and noise.

Improving imaging quality while reducing the sampling time simultaneously is a crucial challenge that limits the practical application of temporal ghost imaging (TGI). To improve the performance of TGI, various methods have been proposed and verified. However, a work analyzing in detail the influence of intensity accuracy and detection noise of TGI is still absent. Here, we establish an evaluation model to quantify the imaging quality of TGI and differential TGI (DTGI). Our model considers the intensity detection accuracy, threshold, and noise of the test path during image reconstruction and quantifies their influences by developing general imaging formulas of (D)TGI. We also simulate the imaging of (D)TGI numerically. The evaluation demonstrates that (D)TGI is relatively not sensitive to detection accuracy and thresholds of the test path, and image quality is degraded slightly even when those parameters turn much worse. (D)TGI is relatively robust to detection noise but will be unable to reconstruct the object when noise is too strong. DTGI does not show clear advantages over TGI. Our work develops an effective model to quantify the image quality with practical parameters and is significant to real applications of (D)TGI.

Robot-mounted sensing and local calibration for high-accuracy manufacturing

Positional accuracy is critically important for application of industrial robots in fixtureless precision manufacturing; thus, this paper presents a method for simultaneously calibrating the kinematic parameters of robot and robot-mounted measurement devices. In the modeling step, the Modified Denavit–Hartenberg parameters are extended to include the world and tool frames within a minimal parameter set using a systematic approach. Closed-loop calibration is performed using the data collected in the local target workspace using the robot-mounted measurement device. The presented modeling and calibration methods are applied to a case study in which a machining task is performed using a DENSO VS-6556 W industrial robot equipped with a cutting tool, force sensor, and laser profile scanner. In the case study, local models are calibrated for the laser and tool frames using common 1-2-3 blocks as calibration artifacts . The robot/laser model is identified by scanning the artifacts with a robot-mounted laser scanner , and the robot/tool model is calibrated by touching the artifacts with a robot-mounted force sensor. In this application, the robot/laser model is used to scan and register a sheet metal workpiece, and the robot/tool model is used to plan a tool path to machine the periphery. Positional accuracy of the tool path was evaluated to be ± 0.15 mm within the target workspace, which shows significant improvement compared with the mean error of 0.8 mm achieved by global calibration of the robot/tool model. • Simultaneous calibration of robot and extrinsic parameters of robot-mounted sensors. • Closed-loop calibration without external measurement devices. • Local calibration to improve accuracy within target workspace. • Systematic approach to include world and tool/sensor frame in MDH parameter set. • Case study for high-accuracy robotic machining application.

Underwater Pollution Tracking Based on Software-Defined Multi-Tier Edge Computing in 6G-Based Underwater Wireless Networks

The forthcoming 6G networks are expected to provide a vision of overlapping aerial-ground-underwater wireless networks. Meanwhile, the rapid development of the Internet of Underwater Things (IoUTs) brings forth many categories of Autonomous Underwater Vehicle (AUV)-assisted Underwater Wireless Networks (UWNs). In this paper, we argue that the AUV-assisted UWNs can be intelligently utilized to track underwater pollution. To perform smart underwater pollution tracking, we propose the paradigm of AUV flock-based networking system and Software-Defined Networking (SDN)-enabled AUV flock Networking System (SDN-AUVNS). We introduce the concept of Mobile Edge Computing (MEC) into the control of SDN-AUVNS and propose the upgrade of the control plane of the SDN-AUVNS to with the multi-tier edge computing ability. By the proposed system architecture, we adopt the artificial potential field theory to construct the network controlling model. And we present the underwater tracking model for SDN-AUVNS, especially for the underwater pollution equipotential line of a particular concentration. Furthermore, to provide accurate path planning for the equipotential line tracking, we utilize the linearizability mechanism to optimize and revise the control input for the SDN-AUVNS. Lastly, we give a fast united control algorithm that can intelligently schedule the SDN-AUVNS to track underwater pollution equipotential lines. In particular, we propose a smart approach with the name of ’Inverse Distance Weighting’ to optimize the detection sample of the SDN-AUVNS. Evaluation results indicate that our proposal is able to track/survey the equipotential lines within a satisfactory error.

Real-time path correction of industrial robots in machining of large-scale components based on model and data hybrid drive

Industrial robots are increasingly used in machining of large-scale components due to advantages of high repeatability, large workspace and low cost. Nevertheless, applying industrial robots to high-accuracy machining of large-scale components remains a challenge, where the major hurdle is the insufficient manipulator stiffness due to joint flexibility. When the robot is performing a machining task, joint flexibility-induced position errors between motor and link called joint position errors (JPEs), as the main source of robot deformations, make the robot deviate from the desired path. For most industrial robots, due to the lack of link-side encoders, it is difficult to obtain the JPEs by direct measurement and compensate them in the controller, which deteriorates the path accuracy of the robot during machining greatly. To address this problem, this paper presents a real-time path correction approach of industrial robots based on JPE estimation and compensation with requiring only motor-side measurements and external wrenches. The proposed approach is divided into three steps. First, to estimate the actual link position of the robot in real-time, the dynamics of a manipulator with joint flexibility called flexible dynamics (FD) is introduced. Second, by taking both FD and disturbance dynamics into account, a novel link state estimator called flexible-dynamics based disturbance Kalman filter (FDBDKF) is developed, and thus JPEs can be estimated in real-time. Third, a data-driven locally weighted projection regression (LWPR)-based JPE prediction and compensation method is developed to further improve the compensation accuracy of the JPEs. Simulation and experimental results, obtained on a 6-DOF industrial robot, demonstrate the feasibility and effectiveness of the proposed approach. Experimental results show significant improvement (>80%) in the path accuracy of a simple material removal process corrected using the proposed approach.

Empirical Path Loss Modelling for Selected LTE Networks in FUTA Campus, Ondo State, Nigeria

Deployed Long Term Evolution (LTE) networks in Nigeria can barely meet the desired 100 Mbps downlink throughput leading to unsatisfactory quality of experience by mobile users. Typically, mobile network operators (MNOs) rely on network planning tools designed for generalized environments. These tools employ legacy propagation models that may not be suited to the operational environments under consideration. As such, the efficiency of such legacy path loss models suffers when they are used in environments different from those for which they have been designed, and this poses a major challenge to the MNOs. This is because the Nigerian geographical areas and topographical features vary widely from the areas where the legacy models were developed. Several studies in Nigeria and other African countries have shown that the legacy path loss models perform unsatisfactorily when compared with field measurement data. To address this challenge and enable accurate path loss prediction for an urban campus environment, extensive measurements at 2600 MHz were carried out in the main campus of the Federal University of Technology Akure (FUTA), Ondo State, Nigeria. The measurement results were compared with the path loss predictions from the commonly-used legacy propagation models (Free space and 3GPP TR 36.873). The results show that the legacy path loss models under-predict the path loss averagely by 20-40 dB, and up to 88 dB in some cases, for the considered environment. Root mean square error (RMSE) values in the range of 1.895 and 9.159 were also observed along the routes. The measurement results will enable the MNOs to adjust the path losses in order to deliver improved quality of service.

Improved ant colony algorithm for path planning of mobile robots based on compound prediction mechanism

For the issues of the ant colony algorithm (ACO) to solving the problems in mobile robot path planning, such as the slow optimization speed and the redundant paths in planning results, a high-precision improved ant colony algorithm (IPACO) with fast optimization and compound prediction mechanism is proposed. Firstly, aiming at maximizing the possibility of optimal node selection in the process of path planning, a composite optimal node prediction model is introduced to improve the state transition function. Secondly, a pheromone model with initialize the distribution and “reward or punishment” update mechanism is used to updates the global pheromone concentration directionally, which increases the pheromone concentration of excellent path nodes and the heuristic effect; Finally, a prediction-backward mechanism to deal with the “deadlock” problem in the ant colony search process is adopted in the IPACO algorithm, which enhance the success rate in the ACO algorithm path planning. Five groups of different environments are selected to compare and verify the performance of IPACO algorithm, ACO algorithm and three typical path planning algorithms. The experimental simulation results show that, compared with the ACO algorithm, the convergence speed and the planning path accuracy of the IPACO algorithm are improved by 57.69% and 12.86% respectively, and the convergence speed and the planning path accuracy are significantly improved; the optimal path length, optimization speed and stability of the IPACO algorithm are improved. Which verifies that the IPACO algorithm can effectively improve the environmental compatibility and stability of the ant colony algorithm path planning, and the effect is significantly improved.

Virtual element method for mixed-mode cohesive fracture simulation with element split and domain integral

We present a computational framework for mixed-mode cohesive fracture simulation based on the virtual element method (VEM). To represent an arbitrary crack path, the element splitting scheme is developed on a polygonal mesh to capitalize its flexibility in element shape. For the accurate evaluation of a crack-tip stress field and crack propagation direction, the virtual grid-based stress recovery scheme is tailored for VEM in conjunction with the maximum strain energy release rate criterion. The mixed-mode fracture examples are illustrated to validate the accuracy and robustness of the proposed computational scheme. Numerical results demonstrate that the domain integral method with the stress recovery scheme captures an accurate crack path without oscillation under the biaxial tensile stress state. Furthermore, the computed cracks using the element splitting scheme show that smooth and curved patterns on polygonal elements are in good agreement with the experimental results.

Verification of blood flow path reconstruction mechanism in distal sigmoid colon and rectal cancer after high IMA ligation through preoperative and postoperative comparison by manual subtraction CTA

IntroductionWe aimed to investigate manual subtraction computed tomography angiography (MS-CTA) to further confirm the distribution and classification of LCA (left colic artery) ascending/descending branches, then observe the postoperative blood flow path to illustrate how the above branches evolved to postoperative blood path. Material and methods89 patients with distal sigmoid and rectal cancer were referred in our observation and underwent MS-CTA between June 2020 and March 2022. We classified the distribution of LCA and confirmed whether there exists AMCA (accessory middle colic artery). Then we planned blood flow path based on the classification of LCA branches before operation. High ligation was applied in regular radical surgery. During operation, we carefully protect the bifurcation of ascending and descending LCA. Then we compared the planned blood flow path with the actual postoperative blood flow path to verify the mechanism we proposed previously. ResultsOf 89 patients, 82 cases met our criteria, we summarized 6 distribution pattens of LCA ascending and descending branches. These preoperative pattens are consistent with the inspection during operation. The postoperative blood flow path of 6 pattens is evolved from the above adjacent anastomotic branches and is consistent with the planned blood flow path. We also found 2 cases with IMA stenosis and 1 case with SMA stenosis under pathological condition, and their compensatory blood flow path is in accordance with our theory. The rate of the anastomotic leakage in our study group is relatively low (7.3%). ConclusionMS-CTA could confirm the distribution of LCA and AMCA, display accurate postoperative blood reconstruction path after IMA high ligation, and it further verified the mechanism we proposed previously, which is the proximal anastomotic branches forming new blood flow path from high-pressure area to the low-pressure area. This mechanism might be helpful for performing accurate laparoscopic sigmoid and rectal cancer surgery.

A computation effective singularity avoidance method for the underwater manipulator with a non-spherical wrist

In order to perform increasingly complex underwater tasks, we have designed a hydraulic underwater non-spherical wrist manipulator with a binocular vision system and a six-dimensional force sensor mounted behind the end-effector, which can improve the path accuracy and automation capabilities of the system. The system lays a foundation for the high precision and autonomous deep-sea operation and can improve its intelligence level. In this paper, we focus on solving the singularity avoidance problem of the hydraulic underwater manipulator for accurate control, and propose a computationally efficient singularity avoidance method to improve the path accuracy of the manipulator with the non-spherical wrist. Firstly, the singular configurations of the manipulator are analyzed. Secondly, the Jacobian is decomposed into sub-block matrices to obtain the singular factors according to the singular configurations. Thirdly, the singularities are avoided by the approximate damped reciprocal method. Theoretical analysis shows that the computation costs of the proposed method are only one-third to one-half of that of the traditional methods. The simulation results prove that the proposed method can largely improve the path accuracy of the manipulator with less computation costs, which shows that our method would have a certain significance in improving the precision and real-time response of deep-sea operations.