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Navigation Control and Signal Processing Methods for Multiple Autonomous Unmanned Systems

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 14084

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Special Issue Editors

Prof. Dr. Haoqian Huang

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Guest Editor

College of Energy and Electrical Engineering, Hohai University, Nanjing 210024, China
Interests: Integrated navigation, filtering, underwater vehicle; intelligent sensor

Prof. Dr. Bing Wang

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Guest Editor

College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
Interests: intelligent sensor; nonlinear networked control and application
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yuan Yang

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Guest Editor

School of Instrument Science and Engineering, Southeast University, Nanjing 211189, China
Interests: precise measurement; measurement and fusion technology; filtering method
Special Issues, Collections and Topics in MDPI journals

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Dear Colleagues,

Underwater unmanned system refers to underwater unmanned control systems with certain autonomous capacity and autonomy, which is a combination of artificial intelligenceand real-time control decision systems. Underwater unmanned system integrates a multi-discipline advanced technology such as power, mobile, control, sensor, and artificial intelligence. It is a typical product of mechanized information intelligence and high integration and military and civilian technology. It has been successfully applied to submarine pipeline layoutand exploration, marine mineral resources exploration, marine biological resources survey, submarine reconnaissance, submarine training and other fields. In recent years, the development of sensor technology, control theory, artificial intelligence, signal processing algorithm has promoted the continuous improvement of underwater unmanned system navigation technology, and there have been a variety of novel underwater navigation methods. In summary, underwater unmanned system navigation technology can be divided into the following categories: (1) Inertial navigation: the inertial navigationsystem is based on INS and supplemented by other navigation.INS and DVL combination navigation are important autonomous navigation methods forunderwater unmanned systems; (2) Passive navigation: the geophysical navigation has the characteristics of good concealment and no need to surface for calibration. Itis a research focus for underwater unmanned system; (3) Beacon Navigation: this navigation method applies the sonobuoy underwater positioning navigation system based on GPS to the underwater robot, which can make up for the shortcomings such as the inability to move the array position in the long baseline and the lack of surface and underwater communication ability.

Underwater unmanned system is a multi-disciplinary integrated system. To solve its core problems such as autonomy, interoperability, data link and multi-platform collaboration, the following common key technologies need to be broken through: (1) Autonomous navigation technique: in order to ensure that the AUV can complete various tasks in the complex marine environment autonomously and collaboratively. It is necessary to solve technical problems such as environmental adaptation, autonomous docking and recovery, cooperative control of formation, cooperative navigation and positioning, etc. (2) Underwater data link communication technology: in order to achieve data sharing between multi-platform underwater unmanned systems, we must solve the problem of technical problems such as underwater remote high-speed communication, underwater network and air network interconnection.

This special issue aims to provide advanced control, navigation and signal processing methods for multiple autonomous unmanned systems.

Potential topics to be covered:

New investigation methods and sensors for path planning;
Compensation and calibration algorithms for navigation sensors;
Advanced sensors and information fusion for underwater navigation;
Underwater image enhancement and 3D image reconstruction;
Mobile robot navigation and control based on intelligent learning / bionics；
Cooperative control and navigation in multi-unmanned systems;
Quantum device and intelligent measurement；
Bionic navigation sensors;
New-concept navigation: Brain-like navigation, signals of opportunity navigation;
Underwater data link communication technology;

Prof. Dr. Haoqian Huang
Prof. Dr. Bing Wang
Prof. Dr. Yuan Yang
Guest Editors

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Published Papers (9 papers)

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Research

10 pages, 3075 KiB

Open AccessFeature PaperArticle

Research on Hard, Transparent and Hydrophobic Coating on PMMA Sheet

by Guoqing Wu, Xiaoping Chen, Xuanyu Xie, Pu Zhang, Shenyu Ge, Wei Chen, Xian Zeng and Ruoye Wang

Appl. Sci. 2022, 12(24), 12927; https://doi.org/10.3390/app122412927 - 16 Dec 2022

Cited by 2 | Viewed by 1469

Abstract

In this paper, nano SiO₂ particles modified organic silane coatings were successfully prepared to aim at the application of the self-cleaning coating on PMMA substrate for deep-sea optical windows. The chemicals, surface microstructure, wettability, hardness, adhesion, transparency, water scouring resistance as well as microorganism attachment rate of the coatings were investigated. The results showed that adding SiO₂ nanoparticles into the organic silicon coating can effectively improve the hydrophobicity due to generating a micro-nano structure surface. However, excessive addition would result in a decrease in hydrophobicity, adhesion, as well as transparency, due to the inorganic SiO₂ particle destroying the integrity of the organic coating. The optimal coating was obtained by adding 0.5 wt% nano SiO₂ particles, which possessed a water contact angle of 114.2°, hardness of 4H, adhesion level of 0, and visible light transmittance of 0.886. After 40-h water scouring, the water contact angle decreased to 108.3° and the visible light transmittance decreased to 0.839, suggesting good water scouring resistance. The microorganism attachment rate of the S05 coating was 0.17% after a 6 h immersion test, which was about half that of the PMMA substrate. Full article

(This article belongs to the Special Issue Navigation Control and Signal Processing Methods for Multiple Autonomous Unmanned Systems)

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15 pages, 3079 KiB

Open AccessArticle

A Novel UWB Positioning Method Based on a Maximum-Correntropy Unscented Kalman Filter

by Mujie Zhao, Tao Zhang and Di Wang

Appl. Sci. 2022, 12(24), 12735; https://doi.org/10.3390/app122412735 - 12 Dec 2022

Cited by 7 | Viewed by 1252

Abstract

Aiming at the problem of measurement-information abnormal-error and nonlinear filtering in UWB navigation and positioning, an ultra wideband position algorithm based on a maximum cross-correlation entropy unscented Kalman filter is proposed. The algorithm first obtains the predictive state estimate and the covariance matrix through traceless transformation. Then, it reconstructs observation information using the nonlinear regression method based on the maximum cross-correlation entropy criterion, which enhances the robustness of the unscented Kalman filter algorithm for heavy-tailed noise. The simulation and actual test results show that this algorithm has better positioning accuracy and stability than the traditional filter algorithm in a non Gaussian noise environment. This algorithm effectively solves the problem that UWB indoor location is easily affected by indoor environments, resulting in fixed deviation for that location. Full article

(This article belongs to the Special Issue Navigation Control and Signal Processing Methods for Multiple Autonomous Unmanned Systems)

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19 pages, 25446 KiB

Open AccessArticle

The Full-Field Path Tracking of Agricultural Machinery Based on PSO-Enhanced Fuzzy Stanley Model

by Yu Sun, Bingbo Cui, Feng Ji, Xinhua Wei and Yongyun Zhu

Appl. Sci. 2022, 12(15), 7683; https://doi.org/10.3390/app12157683 - 30 Jul 2022

Cited by 6 | Viewed by 1668

Abstract

The unmanned operation of agriculture machinery in the full field of farmland is an important part of unmanned farm and smart agriculture. Although the autonomous navigation for agriculture robot has been widely studied in literature, research on the full-field path tracking problem of agriculture machinery is rare. In this paper, in order to enhance the adaptivity of path tracking algorithm, an improved fuzzy Stanley model (SM) is proposed based on particle swarm optimization (PSO), where the control gain is modified adaptively according to the tracking error, velocity and steering actuator saturation. The PSO-enhanced fuzzy SM (PSO-FSM) is verified by experiments on numerical simulation and self-driving of mobile vehicle. Simulation results indicate that the PSO-FSM achieves a better result than SM and FSM, where PSO-FSM changes the control gain adaptively under different velocities and actuator saturation conditions, and the maximum lateral errors of SM and PSO-FSM for mobile vehicle autonomous turning are 0. 32 m and 0.03 m, respectively. When the location of the mobile vehicle deviates from the expected path at 4 m in a lateral direction, the distance of the guided trajectory for the mobile vehicle to reach the expected path is no more than 5 m. A preliminary experiment is also carried out for a wheeled combine harvester working on slippery soil, and the result indicates that the maximum lateral tracking error of PSO-FSM is 0.63 m, which is acceptable for the path tracking of a combine harvester with a large operation width. Full article

(This article belongs to the Special Issue Navigation Control and Signal Processing Methods for Multiple Autonomous Unmanned Systems)

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13 pages, 1376 KiB

Open AccessArticle

A Novel Decoupled Synchronous Control Method for Multiple Autonomous Unmanned Linear Systems: Bounded L₂-Gain for Coupling Attenuation

by Yinsheng Li, Bing Wang and Yuquan Chen

Appl. Sci. 2022, 12(15), 7551; https://doi.org/10.3390/app12157551 - 27 Jul 2022

Cited by 2 | Viewed by 952

Abstract

This paper addresses the distributed optimal decoupling synchronous control of multiple autonomous unmanned linear systems (MAUS) subject to complex network dynamic coupling. The leader–follower mechanism based on neighborhood error dynamics is established and the network coupling term is regarded as the external disturbance to realize the decoupling cooperative control of each agent. The Bounded L₂-Gain problem for the network coupling term is formulated into a multi-player zero-sum differential game. It is shown that the solution to the multi-player zero-sum differential game requires the solution to coupled Hamilton–Jacobi (HJ) equations. The coupled HJ equations are transformed into an algebraic Riccati equation (ARE), which can be solved to obtain the Nash equilibrium of a multi-player zero-sum game. It is shown that the bounded L₂-Gain for coupling attenuation can be realized by applying the zero-sum game solution as the control protocol and the ultimately uniform boundedness (UUB) of a local neighborhood error vector under conservative conditions is proved. A simulation example is provided to show the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Navigation Control and Signal Processing Methods for Multiple Autonomous Unmanned Systems)

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20 pages, 12277 KiB

Open AccessArticle

Research on a Measurement Method for the Ocean Wave Field Based on Stereo Vision

by Hanyu Sun, Guoqing Wu, Xueliang Wang, Tao Zhang, Pu Zhang, Wei Chen and Quanhua Zhu

Appl. Sci. 2022, 12(15), 7447; https://doi.org/10.3390/app12157447 - 25 Jul 2022

Viewed by 1366

Abstract

The wave parameter is an important environmental input condition. Traditional contact wave measurement methods are unable to meet the requirements of high precision, non-contact, and ship wave field assessment. Alternatively, stereo vision technology can realize a non-contact and mobile form of measurement. However, this technology suffers from poor timeliness and adaptability. This paper proposes a comprehensive wave measurement method that is based on stereo vision, wherein the gridding of siftGPU is used to achieve the fast matching of large images. The whole algorithm can be run within 6 s and it guarantees more than 20,000 feature-matching logarithms. Furthermore, by utilizing the least squares method and sea surface wave surface theory, the sea surface base level can be calculated without control points, along with the inversion of the sea wave parameters (wave height, period, and wave direction) and error point fitting. The rationality and superiority of the algorithm were verified through multiple comparison experiments. Compared with the Richard Brancker Research (RBR) wave height meter, the measurement error of the wave height is less than 10%, the period error is less than 0.5 s, and the wave direction error is less than 10° with the proposed method. Full article

(This article belongs to the Special Issue Navigation Control and Signal Processing Methods for Multiple Autonomous Unmanned Systems)

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17 pages, 3407 KiB

Open AccessArticle

Fault Diagnosis Method Based on Time Series in Autonomous Unmanned System

by Zhuoran Xu, Manyi Wang, Qianmu Li and Linfang Qian

Appl. Sci. 2022, 12(15), 7366; https://doi.org/10.3390/app12157366 - 22 Jul 2022

Cited by 2 | Viewed by 1278

Abstract

There are various types of autonomous unmanned systems, covering different spaces of sea, land, and air, and they are comprehensively going deep into multiple fields of national security and social life. Due to the development of technology, the scale of unmanned systems is getting larger and larger, the number of components in the system is increasing, and the operating environment of the system is also becoming more and more complex. Therefore, the probability of failure of the components of the system will also be significantly increased. In order to eliminate the impact of the fault in time, the fault diagnosis method is significant. Considering the differences of components in autonomous unmanned systems, if a specific fault diagnosis algorithm is designed for each type of component, it will bring difficulties to the coordinated control of the system. Therefore, this paper analyzes the data characteristics of unmanned autonomous system devices (such as sensors) and finds that these data have time series. Therefore, the data of different devices can be converted into time series, and a general fault diagnosis algorithm suitable for most devices can be studied. The fault diagnosis algorithm is based on the clustering algorithm. In order to improve the clustering effect, the time series of different devices are represented by Gaussian mixture clustering to reduce the computational complexity of the clustering calculation. Then, a time series similarity measurement method based on the improved Markov chain is proposed. This method can better distinguish normal samples from abnormal samples so as to classify and identify faults effectively. Full article

(This article belongs to the Special Issue Navigation Control and Signal Processing Methods for Multiple Autonomous Unmanned Systems)

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18 pages, 3716 KiB

Open AccessArticle

In-Motion Coarse Alignment Method Based on Position Loci and Optimal-REQUEST for SINS

by Haoqian Huang and Jiaying Wei

Appl. Sci. 2022, 12(14), 7113; https://doi.org/10.3390/app12147113 - 14 Jul 2022

Viewed by 1030

Abstract

In this paper, an improved in-motion coarse alignment method is proposed for a strapdown inertial navigation system (SINS) using position loci obtained from the Global Positioning System (GPS). The difference from the popular coarse alignment methods is that the proposed algorithm uses GPS position loci information to form the vector observation, and does not need velocity information, which expands the application range of in-motion coarse alignment. In addition, this paper utilizes the Optimal-REQUEST algorithm to reduce the influence of random errors contained in the vector observation. The Optimal-REQUEST algorithm is an adaptive iterative updating algorithm, which can adaptively adjust the gain of the filter according to the loss function. Simulation results confirmed that the proposed algorithm can suppress the impact of random errors effectively. The pitch, roll and yaw angles calculated by the proposed algorithm were improved by 51.95%, 53.80% and 63.03% compared with the comparison algorithms. Full article

(This article belongs to the Special Issue Navigation Control and Signal Processing Methods for Multiple Autonomous Unmanned Systems)

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18 pages, 1595 KiB

Open AccessArticle

The LOS/NLOS Classification Method Based on Deep Learning for the UWB Localization System in Coal Mines

by Yuxuan Zhao and Manyi Wang

Appl. Sci. 2022, 12(13), 6484; https://doi.org/10.3390/app12136484 - 26 Jun 2022

Cited by 6 | Viewed by 1833

Abstract

A localization system is one of the basic requirements for coal mines. Ultra-wideband (UWB), as a technology with broad application prospects, is considered to have great potential in the absence of satellite signals, especially in the underground mine environment, as it can provide rescue assistance. However, state-of-the-art UWB position systems in coal mines cannot efficiently differ the line-of-sight from all communication links, which results in deterioration of the localization accuracy. In this paper, we propose a LOS/NLOS classification method based on a deep learning algorithm. Specifically, we utilize the Generative Adversarial Networks (GAN) to generate diagnostic data for frame transmission under non-line-of-sight (NLOS) condition. Then, a Convolutional Neural Network (CNN) is adopted to identify the NLOS communication. Finally, the trilateral centroid positioning algorithm (TCPA) based on ranging data is used to verify the effect of our method for a localization system in coal mines. Field experiments show that our method can accurately differ the LOS/NLOS with the accuracy of 91.19%. The TCPA algorithm with our method can obtain 3.11% improvement compared with the scenario without using our method. Full article

(This article belongs to the Special Issue Navigation Control and Signal Processing Methods for Multiple Autonomous Unmanned Systems)

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20 pages, 3662 KiB

Open AccessArticle

A Two-Objective ILP Model of OP-MATSP for the Multi-Robot Task Assignment in an Intelligent Warehouse

by Jianqi Gao, Yanjie Li, Yunhong Xu and Shaohua Lv

Appl. Sci. 2022, 12(10), 4843; https://doi.org/10.3390/app12104843 - 11 May 2022

Cited by 5 | Viewed by 1923

Abstract

Multi-robot task assignment is one of the main processes in an intelligent warehouse. This paper models multi-robot task assignment in an intelligent warehouse as an open-path multi-depot asymmetric traveling salesman problem (OP-MATSP). A two-objective integer linear programming (ILP) model for solving OP-MDTSP is [...] Read more.

O (n!)

. We can solve the small multi-robot task assignment problem by solving the two-objective ILP model using the Gurobi solver. The multi-chromosome coding-based genetic algorithm has a smaller search space, so we use it to solve large-scale problems. The experiment results reveal that the two-objective ILP model is very good at solving small-scale problems. For large-scale problems, both EGA and NSGA3 genetic algorithms can efficiently obtain suboptimal solutions. It demonstrates that this paper’s multi-robot work assignment methods are helpful in an intelligent warehouse. Full article

(This article belongs to the Special Issue Navigation Control and Signal Processing Methods for Multiple Autonomous Unmanned Systems)

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