bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Improved Unscented Kalman Filter for Maneuvering Target Tracking*
Authors: He Xiaoyou , Su Yu , Qiu Yuhe 
Published: April 2022

AbstractThe photoelectric pod provides angular information and distance information for the UAV (Unmanned Aerial Vehicle), and the UAV uses it to estimate the status information of the moving target. Since the measurement information of the photoelectric pod is the angle of sight and relative distance, the measurement equation contains some nonlinear functions in the Cartesian coordinate system, and the output frequency of the photoelectric pod is low. The improved unscented Kalman filter combines the function of prediction and correction, introduces the prior information of the target acceleration constraint, and uses the offline data to obtain steady gain, and then estimates the target state online. The simulation result show that the algorithm can track the target and need less time compared with the traditional unscented Kalman filter.
Path planning of manipulator based on improved RRT algorithm
Authors: Wang YuYu , Tian JunCheng , Liu Zhe , Liu HaoBo , Liu JiaZheng , Xie Lin 
Published: April 2022

Abstract
Aiming at the problem that the 6-DOF manipulator can quickly avoid obstacles in high-dimensional space, the method of path planning based on the improved rapid expansion random tree (RRT) algorithm is proposed. The improved RRT algorithm combines the advantages of the target bias strategy and proposes a method for setting the intermediate bias point. In random sampling, the sampling point is biased to the intermediate bias point,with a certain probability to reduce the blindness of sampling, and use at the same time the path shortening method reduces path nodes and turning points, and optimizes the motion path of the robotic arm. By comparing the simulation results with the RRT and P-RRT algorithms, the effectiveness of the improved RRT algorithm in the calculation time and the number of path nodes is proved.
State Estimation of Wireless Sensor Networks Under False Data Injection
Authors: Jin Qingchen , Chen Xin , Zhang Pengfei , Yuan Jing , Li Shang 
Published: April 2022

Abstract
This paper studies the problem of false external data injection information transmitted through wireless sensor networks. Firstly, this paper models the nonlinear system and improves the unscented Kalman filter to solve the problem of false data injection in the transmission process replaces the abnormal innovation data by setting the innovation confidence interval in advance and deduces the error covariance of this method. Then the stability of the designed algorithm is verified. Finally, the effectiveness of the filtering algorithm designed in this paper is illustrated by tracking the state of the system and the mean square error of the simulation system.
A UAV Coverage Path Planning Algorithm Based on Double Deep Q-Network
Authors: Li Shang , Chen Xin , Zhang Min , Jin Qingchen , Guo Yudi , Xing Shunxiang 
Published: April 2022

Abstract
The UAV path planning method has practical value in the military field and automated production. Based on deep reinforcement learning theory and the characteristics of coverage path planning, this paper designs and implements a set of deep reinforcement learning frameworks suitable for UAV coverage path planning and trains it in the abstract environment model built. The simulation experiment results show that the designed UAV coverage path planning frameworks can consider obstacles, no-fly zones and length constraints, plan a reasonable path to complete the coverage task, and have a certain generalization ability.
Research on mmWave IoV Beam Tracking Based on EKF
Authors: Sun Yu , Feng Chenwei , Lin Weiming , Zhang Lin , Zeng Huangbin , Li Zhuo 
Published: April 2022

Abstract
The rapid development of 5G communication provides a huge opportunity for the research on the Internet of vehicles. With the increasing shortage of low-frequency spectrum resources and the demand for high transmission rates for the architecture of the internet of vehicles, the highly qualified millimeter wave has been combined into the research on the internet of vehicles. However, due to the various characteristics of millimeter wave and the limitation of the complex communication environment of the internet of vehicles, how to achieve stable beam tracking is a big problem that needs to be solved urgently. This paper mainly introduces an extended Kalman filter algorithm with updated threshold to solve the problem of continuous stable beam tracking of millimeter waveInternet of vehicles. Simulation shows that this method has lower tracking error than the original algorithm, and better overcomes the problem of continuous accumulation of tracking error of the original algorithm.
Path planning of mobile robots based on improved RRT algorithm
Authors: Liu Benxue , Liu Chong 
Published: April 2022

Abstract
Aiming at the shortcomings of RRT (Rapidly-exploring Research Tree) algorithm such as long time cost and low utilization of sampling points, an improved RRT algorithm is proposed. By adopting the sampling strategy based on dynamic probability, the robot is prevented from falling into a local minimum during the sampling process. At the same time, the adopt of variable step strategy as the random tree expands reduces the number of sampling points. Finally, the initial path is optimized to make it more suitable for the robot to walk. The improved RRT algorithm is compared with the RRT algorithm and the Goal-bias RRT algorithm in both simulations on MATLB and experiments on robot based on ROS (Robot Operating System). And the results show that improved RRT algorithm increases the speed of path planning, reduces the length of path, and the planned path is smoother and more suitable for the real robot to move.
Dual-arm Robot Fast Pick-and-Place on a Moving Conveyor
Authors: Sheng Jiangcong , He Dingxin , Gu Qiang , Gong Dongjun 
Published: April 2022

Abstract
The pick-and-place (PNP) system of multi-robots on moving con- veyors have a wide range of industrial applications. In practice, the most used is first in first out (FIFO) algorithm. However, the efficiency of FIFO is low when the number of objects to be picked is large. To improve the PNP efficiency, we first build a kinematic model of the dual-arm robot and establish a PNP time lookup table based on the inverse kinematic algorithm. Then, we develop a PNP shortest time first out (STFO) algorithm to calculate the high-efficiency picking sequence. Dynamic experiments show that compared with FIFO, we improve the dual-arm robot PNP system efficiency by 20%.
A Method of Robot Grasping Based on Reinforcement Learning
Authors: Lu Liangliang , Zhang Ming , He Dingxin , Gu Qiang , Gong Dongjun , Fu Lei 
Published: April 2022

Abstract
In this paper, we propose a method for robot grasping based on reinforcement learning. A six-degree-of-freedom robot is used to execute the grasping action, and an RGB-D camera is used as a sensing module to collect the information. Different from previous model-based methods, our whole system is data-driven. In order to better grasp a bunch of randomly placed objects, the pushing action is added to spare more space for grasping. The whole process is described as a Markov decision process (MDP). The training is carried out in simulation. Experimental results show that our method can complete the grasping work efficiently, especially in those complicated and difficult scenarios. At the same time, the model can be directly applied in actual grasping tasks which greatly reduce the risk of training in the physical world.
Attitude Control Design and Simulation Analysis of Quadrotor Based on Active Disturbance Rejection Control
Authors: Cheng ZiLong , Liu Jia , Zhang MingFeng , Zhao Nan , Wang MengYuan , Zhang YunXi 
Published: April 2022

Abstract
In this paper, the attitude control problem based on active disturbance rejection control (ADRC) is studied for nonlinear underactuated quadrotor aircraft with parametric uncertainties and modeling disturbances. The simplified dynamics model of the quadrotor is established by using Newton-Euler equation, and the ADRC is applied to control the attitude control of the quadrotor. Finally, through numerical simulations, the performance of the ADRC and traditional PID algorithm are compared, the results show that the attitude control algorithm based on ADRC has better performance, and has excellent ability to resist disturbance.
Research on Swing Prediction of Overhead Line Robot Inspection Arm
Authors: Guo Xinliang , Liu Ronghai , Cai Xiaobin , Shen Feng 
Published: April 2022

Abstract
Overhead lines and various fittings on the lines are prone to damage when exposed to the outdoor environment for a long time. Therefore, overhead line inspection work is required. The use of overhead line robots instead of manual inspections can reduce the labor intensity of personnel and protect their personal safety. The current overhead line robots have the problem of low intelligence and less consideration of the robot swinging with the wire. In this regard, the paper focuses on the swing prediction technology for the large-angle swing of the robot platform, and proposes a method based on real-time variance statistics. The large-angle swing prediction model. Finally, a comparative experiment was conducted with the combined model of empirical mode decomposition and support vector regression based on time series forecasting, and real experimental data were used to verify the rationality and effectiveness of the proposed algorithm and model.
An Improved Genetic Algorithm for Rapid UAV Path Planning
Authors: Liu Jinrong 
Published: April 2022

Abstract
UAV technologies have advanced rapidly and are widely used in military and civilian fields. For instance, the UAV swarms have been widely applied to oil and gas exploration, geometric mapping, and cargo transport. However, the UAV swarm system requires a more accurate plan before performing any mission. Path planning is one of the essential parts of mission planning because of the higher requirement of robustness and real-time communication. UAV path planning could generate the optimal path starting from the current position to the target in an environment with an obstacle. While the standard genetic algorithm has lacked efficiency in the iteration process and poor stability, a new genetic operator is proposed for the genetic algorithm and applied to the path planning simulation of UAV swarms in this study. A three-dimensional mapping and fitness function are already constructed for the simulation. The simulation result shows the algorithm with improved selection and mutation operator can efficiently and stably converge to the optimal solution.
SLIDING MODE CONTROL OF UNMANNED AERIAL VEHICLE BASED FINITE-TIME DISTURBANCE OBSERVER
Authors: Cui Lichao , Jiang Yulian , Zhang Xudong 
Published: April 2022

AbstractBecause of the advantages of high maneuverability, vertical takeoff and landing, free hovering, and easy portability, quadrotor UAVs have become one of the research hotspots in recent years. Aiming at the finite-time trajectory tracking control problem for quadrotor under unknown environmental disturbance and modeling uncertainty, a fast terminal sliding mode control method using a finite-time disturbance observer is proposed. First, the modeling uncertainty and unknown external disturbance of the quadrotor were combined into lumped disturbance, and a finite-time disturbance observer was designed to estimate the lumped disturbance online. Secondly, a fast terminal sliding mode controller was constructed for the realization of a finite-time tracking control of the quadrotor, and the estimated value of the limited-time disturbance observer was compensated to the controller. Thereby, the influence of compound disturbance was eliminated, the quadrotor can achieve a finite-time trajectory, and the disturbance suppression capability of tracking control was improved. Finally, we conducted simulation experiments and confirmed that the control strategy proposed in this paper is effective.
Research on Guidance and Control Method of Hypersonic UAV Capture Segment Based on Overload
Authors: Zhang Dong , Li Xiujuan , Han Xiao 
Published: April 2022

Abstract
With the implementation of China’s high supersonic aircraft science and technology engineering and the key basic scientific problems of near space vehicles major research programs.In this context, China is also vigorously developing high-speed high-mobility, long range and recyclable aircraft. In view of the tracking problem of a hypersonic vehicle when it reaches the capture segment, a mathod based on overload guidance law is proposed. The relationship between force and head-on angle can be deduced from the relationship between overload and head-on angle, thus transforming the overload control problem into the head-on control problem. Inversion controller design method is adopted for its parameter uncertainty rigid body model, and the derivative of virtual control amount is obtained by dynamic surface method to avoid the problem of differential expansion. Based on the secound-order tracking-differential, a nonlinear interference observer is designed to estimate and compensate for model uncertainties. The simulation results show that the controller can achieve stable tracking of the angle and high reference instructions, and has a strong robustness to the uncertainty effect of the model.
A compound convolutional neural network used to process small perturbation
Authors: Ni W.H , Xu Shu 
Published: April 2022

Abstract
Input samples formed by deliberately adding subtle interference to the data set may cause the model to give false outputs with high confidence. To solve this problem, this paper proposes a deep learning method. Different interference sources can be considered to be independent of each other. Therefore, different networks are trained based on densenet for different interference sources. Then, the data set added to the combined interference item is sent to each neural network, and the output of each network is used as the input of the neural network in the next stage. The next stage of the neural network is used for multi-objective classification based on the output of the neural network. The output of the neural network is disturbed by adding different features to the input data. Because the depth network of each type of interference is independent, when introducing a new interference term, only the depth network and full connection layer of the new interference term need to be trained, rather than the whole network, which greatly simplifies the training process when introducing a new interference source.
Fault diagnosis of Fuel Pump Based on Wavelet Denoising and Deep Learning
Authors: Guo Yudi , Chen Xin , Wang Weizhen 
Published: April 2022

Abstract
Aiming at the problem that the signal collected by the sensor of aircraft fuel system contains much noise, which masks the effective fault characteristics, a fault diagnosis method of aircraft fuel pump based on wavelet threshold denoising and convolutional neural network is proposed. Wavelet threshold denoising is added to the traditional data preprocessing to eliminate the noise contained in the data; the convolution neural network is used to build a fault detection model to identify and locate the fault of the fuel pump. The experimental results show that this method can accurately and effectively identify and locate the fuel pump fault based on the noisy signal, and the recognition accuracy is more than 85%, which proves the effectiveness of this method for aircraft fuel pump detection.
A new type of microgrid power quality improvement strategy based on harmonic suppression
Authors: Wang Mian , Liu TianYu 
Published: April 2022

Abstract
Current harmonics and distorted voltage are two important factors that affect the power quality of microgrids. Combining with the operating characteristics of microgrids, this article uses a new strategy to control grid-connected inverters and UPQC to improve grid quality. Pointed out its advantages relative to traditional active filters, and in view of the current harmonic detection problems, In this paper, the inverter adopts an improved ip-iq harmonic detection method to improve the accuracy of harmonic current detection and has strong interference. The UPQC structure uses SVPWM and PWM control methods as its new control strategies on the series side and parallel side to compensate for distortion voltage and harmonic current. A detailed model is built in MATLAB/Simulink to verify the feasibility of this method.
A Fast Path Planing Solution For Multiple Obstacle Spaces
Authors: Chen Xiaolei , Shen Haoyang , Yuan Junling , Han Lu 
Published: April 2022

Abstract
Among the many path planning algorithms, the RRT* algorithm and the Informed RRT* algorithm are the most popular. Both of them can complete the single path planning effectively. However, because the planning effect of these algorithms is limited by the number of iterations, the optimal solution cannot be planned in a limited time. Therefore, a new algorithm-Self-Optimizing Growing Tree (SOGT) is proposed in this paper. On the premise of ensuring complete probability, heuristic bias sampling is used to improve the efficiency of the algorithm to explore free space. Then, the initial path is optimized by the maximum distance and the inflection point optimization using prior information to obtain an optimal path. In the simulation experiments, we give the comparison results of numerical simulations of SOGT algorithm, RRT* algorithm and Informed RRT* algorithm. The experiment proves that the SOGT algorithm can get an optimal path in less time, which fully reflects the characteristics of the algorithm with good real-time performance and strong adaptability.
Neural Network based Identification and Self-tuning PID Control for Spacecraft Thermal Vacuum Tests
Authors: Wu Dongliang , Zhu Xi , Wen Jing , Zhu Lin , Feng Yao 
Published: April 2022

Abstract
This paper deals with the high-performance temperature control issues of spacecraft thermal vacuum tests. More precisely, the novel model identification and self-tuning PID control method is developed based on neural network. The corresponding controller design details are provided and the experiment results are given for verifying considerable advantages with our proposed control strategy in the end.
A Distributed Control for Ground Target Tracking of Nonlinear Unmanned Aerial Vehicles Formation
Authors: Jia Jiangbo , Chen Xin , Zhang Min , Li Zhen 
Published: April 2022

Abstract
This paper investigates a circular formation control strategy for multiple unmanned aerial vehicles (multi-UAV) formation for ground moving target tracking. Each UAV obtains the observation value of target information through sensors and exchanges data with its neighbors. Since the formation cannot get the parameter of the target directly, we proposed a distributed error observer. A new guidance law based on the error value obtained has been designed, and it was proved through the interconnection system that it was consistent with Lyapunov stability. Finally, a simulation example is used to illustrate the effectiveness of the proposed guidance law.
A two-stage modular blind denoising algorithm based on real scene
Authors: Ding Yuehao , Wu Hao , Yuan Guowu 
Published: April 2022

Abstract
The use of deep learning methods has developed rapidly in image denoising. Now deep learning methods have a good improvement in denoising effects compared to traditional methods, but there are also some problems. On the one hand, these denoisers are more used to remove the noise of a specific distribution, while the image noise distribution in real life is not fixed, so it is more difficult and more practical to denoise the real image. On the other hand, more and more complex network structures and deeper and deeper network models seem to have become a necessary condition for better denoising effects, but its improvement is not linear, and deeper networks are likely to be removed. The improvement in noise effect is minimal. Because of this, we designed a relatively simple network structure to remove real image noise, which includes the noise level estimation stage using the channel attention mechanism, and the non-blind noise reduction stage using the micro-branch structure we designed. Experiments show that our method has good visual perception quality compared with other methods on commonly used image denoising data sets.
Improvement of the Seagull Optimization Algorithm and Its Application in Path Planning
Authors: Chen Jing , Chen Xin , Fu Zaifei 
Published: April 2022

Abstract
Seagull Optimization Algorithm (SOA) is an emerging intelligent optimization algorithm proposed in recent years. This paper proposes an improvement in the SOA based on Levy flight(LSOA), which aims to solve the problems of decreasing exploration ability and easing to fall into local extreme values in the late stage of SOA. LSOA could increase the diversity of the seagull population, improve the exploration ability of SOA. Then selecting the classic 23 benchmark functions for simulation experiments and comparing with the other five intelligent algorithms in recent years. The result shows that LSOA has improved stability, convergence speed, and search accuracy, effectively avoiding local optimum conditions, with broad research prospects. Finally, LSOA is applied to the path planning problem, proving the algorithm’s feasibility and having a good optimization effect.
A Multi-feature Combination Method for Tracking of Marine Ships
Authors: Yang Lichun , Yang Dan , Wang Fan 
Published: April 2022

Abstract
Maritime images and videos provide a variety of kinematic traffic information, including traffic volume, ship speeds and headings, which significantly benefits remote maritime traffic management and maritime safety improvement. To track multiple moving objects robustly from the complex original maritime image sequence, we propose an ensemble ship tracking framework with multiple feature extraction and Dempster–Shafer theory of evidence. Spatial features such as statistical features of gray values and geometric features in the image sequence rather than the motion characteristics are extracted, and the D-S theory is applied for combination of feature evidence to perform the correct association of multiple objects. We demonstrate the performance of the proposed ship tracking algorithm on a typical marine traffic scene through the marine surveillance video. Experimental results show that the proposed method can achieve continuous tracking of multiple marine moving objects with strong stability, and have the potential to improve marine transportation and fishery safety.
Neighbor Reward with Optimal Reciprocal Collision Avoidance for Swarm Agents*
Authors: Du Linlin , Tang Hao , Li Pengfei , Ma Tao , Ge Shuangquan , Cao Kang 
Published: April 2022

Abstract
Navigating in an unknown area safely is counted as the underlying work which can support swarm agents for more complex tasks. When available information of search regions are lacking, agents make real-time action decisions according to surrounding environments they have perceived. For swarm agent system, connectivity maintenance and collision avoidance are both essential. Based on optimal Reciprocal Collision Avoidance (ORCA) algorithm, we proposed a method that agents can provide assistances to surrounding agents by spreading the status information of themselves, which is the neighbor reward method (NRM). This kind of status information contains ambient information and perceptions of the task which are transferred to reward data for convenient and uniform distributions. In other words, individuals utilize inter-neighbor interactions to achieve the same high-level goal, as well as result in an intelligent independent swarm agents system.
This method solves the velocity selection problem of ORCA and optimizes the obstacle avoidance of the original NRM. The algorithm has been integrated in ROS framework and simulated on GAZEBO. In the tested scenario, our method is efficient for swarm agents collision avoidance in decentralized way.