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Summary: In this article, an optimized formation control algorithm is presented for unmanned surface vehicles (USVs) with collision avoidance and prescribed performance. The prescribed formation geometry is designed in the leader‐follower formation architecture, in which each vehicle tracks its intermediary leader with preserving a desired separation. A prescribed performance control design technique is introduced to guarantee the transient and steady‐state performance specifications on formation errors. Radial basis function neural networks (NNs) are employed to approximate modeling uncertainties including damping terms and unmodeled dynamics. Based on an actor‐critic learning strategy, a reinforcement learning (RL) algorithm is proposed to ensure the optimality of formation control and the specified tracking accuracy simultaneously, in which actor NNs take appropriate control behaviors by interacting with the external environment, and critic NNs evaluate the control performance and generate a reinforcement signal to actor NNs for facilitating the improvement of subsequent behaviors. Stability analysis shows that the proposed optimal formation controller achieves semi‐global uniform ultimate boundedness of closed‐loop adaptive systems with prescribed performance. Comparative simulation results illustrate the effectiveness and superiority of the presented control algorithm. [ABSTRACT FROM AUTHOR]

The accurate detection and segmentation of accessible surface regions in water scenarios is one of the indispensable capabilities of surface unmanned vehicle systems. 'Most existing methods focus on accuracy and ignore the lightweight and real-time demands. Therefore, they are not suitable for embedded devices, which have been wildly applied in practical applications.' An edge-aware lightweight water scenario segmentation method (ELNet), which establishes a lighter yet better network with lower computation, is proposed. ELNet utilizes two-stream learning and edge-prior information. Except for the context stream, a spatial stream is expanded to learn spatial details in low-level layers with no extra computation cost in the inference stage. Meanwhile, edge-prior information is introduced to the two streams, which expands the perspectives of pixel-level visual modeling. The experimental results are 45.21% in FPS, 98.5% in detection robustness, 75.1% in F-score on MODS benchmark, 97.82% in precision, and 93.96% in F-score on USV Inland dataset. It demonstrates that ELNet uses fewer parameters to achieve comparable accuracy and better real-time performance. [ABSTRACT FROM AUTHOR]

Summary: Traffic congestion, air pollution, fuel wastage, and car accidents are all exacerbated by increased traffic. Thus, vehicular communications, which refer to information transmission between cars, pedestrians, and infrastructures, have lately gained popularity and been extensively explored due to their enormous potential to enable intelligent transportation and various safety applications. Manually piloted cars and automated vehicles can acquire relevant information via vehicular communications to enhance traffic security and boost entertainment services. The basic concept of automobile clouds was originally published in the literature not long ago, and several suggested structural approaches have been presented in this study thus far. Several academics have concentrated on the structural layout to address various problems and, as a result, satisfy user expectations in order to give dependable services. We examined various vehicular cloud topologies in this study. We also offered a complete summary of current network layer research on allowing efficient vehicle communications and examined specific security, architectural, and reliability concerns in vehicular clouds. Also, the taxonomy of vehicular networks was discussed in terms of the service link between vehicular networks and cloud computing. Ultimately, we discussed the research prospects available. The results showed that security and privacy challenges are among the most important challenges. [ABSTRACT FROM AUTHOR]

With the advancement of science and technology, the development and application of unmanned mobile vehicles (UMVs) have emerged as topics of crucial concern in the global industry. The development goals and directions of UMVs vary according to their industrial uses, which include navigation, autonomous driving, and environmental recognition; these uses have become the priority development goals of researchers in various fields. UMVs employ sensors to collect environmental data for environmental analysis and path planning. However, the analysis function of a single sensor is generally affected by natural environmental factors, resulting in poor identification results. Therefore, this study introduces fusion technology that employs heterogeneous sensors in the Ackerman UMV, leveraging the advantages of each sensor to enhance accuracy and stability in environmental detection and identification. This study proposes a fusion technique involving heterogeneous imaging and LiDAR (laser imaging, detection, and ranging) sensors in an Ackerman UMV. A camera is used to obtain real-time images, and YOLOv4-tiny and simple online real-time tracking are then employed to detect the location of objects and conduct object classification and object tracking. LiDAR is simultaneously used to obtain real-time distance information of detected objects. An inertial measurement unit is used to gather odometry information to determine the position of the Ackerman UMV. Static maps are created using simultaneous localization and mapping. When the user commands the Ackerman UMV to move to the target point, the vehicle control center composed of the robot operating system activates the navigation function through the navigation control module. The Ackerman UMV can reach the destination and instantly identify obstacles and pedestrians when in motion. [ABSTRACT FROM AUTHOR]

Purpose: The research of unmanned air/ground vehicle (UAV/UGV) cooperation has attracted much attention due to its potential applications in disaster rescue and target surveillance. This paper aims to focus on the UAV/UGV cooperative target tracking and enclosing, considering the limits of detection and sensor failures. Design/methodology/approach: The UAV/UGV cooperation structure is designed, contributing to homogeneous consistency and heterogeneous communication. The target tracking of UAVs is converted into a constraint optimization problem involving tracking cost, and the target enclosing of UGVs is modeled as formation control. Findings: The energy estimation pigeon-inspired optimization is developed to generate control inputs for UAVs. And the controller combined with switchable topology is proposed, where the switching rule is flexible in dealing with some emergencies. Practical implications: The proposed structure and algorithms can be easily applied to practice and help design the UAV/UGV control system. Originality/value: The energy estimation mechanism is proposed for the target tracking of UAVs, and the rules of switching topologies ensure the target enclosing process of UGVs. [ABSTRACT FROM AUTHOR]

This study focuses on the research of spray-pump type unmanned surface vehicles (USVs). Based on the analysis of the dynamics and motion characteristics of USVs, a method combining Dubins curves and the particle swarm optimization algorithm is proposed to find the optimal or suboptimal solution for the formation path. The research goal is to switch multiple USVs from an unordered state to a formation state, taking the speed of USVs and the formation endpoint as dynamic variables and integrating Dubins curve theory into the particle swarm optimization algorithm. A theoretical framework for the formation of spray-pump type USVs is proposed. Finally, the formation of three USVs is realized in a simulation platform. [ABSTRACT FROM AUTHOR]

Given that the internal states of a system, such as position, velocity, acceleration, and other important factors, naturally obey the integral processes of a physical system in kinematics, this paper presents an adaptive noise filtering system that can reconstruct these system states at the kinematic level. This is done without using any prior knowledge of the statistical properties of measurement noises. In the proposed filtering system here, each noise-contaminated estimated state is filtered by an average filter to compensate for phase delay and amplitude distortion. Unlike existing model-based estimation methods, the dynamic equation is not explicitly used in the proposed method, and the uncertainties in the nonlinear dynamic equation can be isolated. Furthermore, this application is much more straightforward as there are no gains to be processed. To verify our proposed adaptive filtering system, it has been applied to a variable speed path-following control task for unmanned surface vehicles (USVs), where accurate system states must be known. In particular, this paper also proposes a state-constrained finite-time control framework to realize the path-following control objectives. The proposed controller here mainly consists of two parts, i.e., an online state-constrained polynomial planning function and an execution of an algebraic control law. Simulations and experiments have been conducted to validate the effectiveness and reliability of the proposed filtering system and the finite-time controller. The results show that the proposed filtering system considerably outperformed several of conventional observers such as the extended Kalman filter (EKF), the passive observer, as well as the high-order differentiator. • An adaptive filtering system is proposed to reconstruct a system's full states. • It is not necessary to know the statistical properties of measurement noises. • A state-constraint controller is proposed to realize the USV path-following task. • Experiments have been conducted to validate the proposed methods. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV)-assisted inventory management, used for inventory control within an enterprise with large warehousing and manufacturing systems, is currently common practice. There are many applications for UAVs within an enterprise, but they are generally used in tasks that have high risk connotations to perform activities quickly, such as in the cases of large enterprises that generally require inventory of many products quickly and efficiently connected in a smart city environment. Having an up-to-date inventory as quickly as possible in a smart city environment allows users to have the necessary information to verify the availability of products, reducing waiting times. The use of UAVs systems compared to conventional systems, such as unmanned ground vehicles, has several advantages, including the fact that UAVs have their own cameras and can move independently within the warehouse. This paper proposes the development of a Supervisory Control and Data Acquisition system (SCADA) for the inventory management of the company DHL, using drones. This study evaluates the OPC communication protocols for the virtual environment and the 232 serial connection protocol for the real environment, using LabView and Factory I/O programs. For the development of the system, the use of a drone was evaluated for the recognition of the availability of merchandise with the use of infrared technology incorporated in the drone by means of an Arduino. The appropriate use of communication protocols allowed the necessary information to be obtained for the communication of the LabView and Factory I/O environment in which a virtual environment based on the DHL inventory system is available. The system allowed the development of a SCADA system for the management of inventory monitoring in real time of the availability of goods in the virtual environment, allowing to obtain the necessary information for its integration in a smart city environment. [ABSTRACT FROM AUTHOR]

The article presents an overview of several studies in the field of Brain–Computer Interfaces (BCIs), the requirements for the architecture of such promising devices, as well as multi-modal BCI for drone control in a smart-city environment. Distinctive features of the proposed solution are the simplicity of the architecture (the use of only one smartphone for both receiving and processing bio-signals from the headset and transmitting commands to the drone), an open-source software solution for signal processing, generating, and sending commands to the unmanned aerial vehicle (UAV), as well as multimodality of the BCI (the use of both electroencephalographic (EEG) and electrooculographic (EOG) signals of the operator). For bio-signal acquisition, we used the NeuroSky Mindwave Mobile 2 headset, which is connected to an Android-based smartphone via Bluetooth. The developed Android application (Tello NeuroSky) processes signals from the headset and generates and transmits commands to the DJI Tello UAV via Wi-Fi. The decrease (depression) and increase of α - and β -rhythms of the brain, as well as EOG signals that occur during blinking were the triggers for UAV commands. The developed software allows the manual setting of the minimum, maximum and threshold values for the processed bio-signals. The following commands for the UAV were implemented: take-off, landing, forward movement, and backwards movement. Two threads of the smartphone's central processing unit (CPU) were utilized when processing signals in the software to increase the performance: for signal processing (1-D Daubechies 2 (db2) wavelet transform) and updating data on the diagrams, and for generating and transmitting commands to the drone. [ABSTRACT FROM AUTHOR]

Accurate and real-time monitoring of the shoreline through cameras is an invaluable guarantee for the safety of near-shore navigation and berthing of unmanned surface vehicles; existing shoreline detection methods cannot meet both these requirements. Therefore, we propose an improved shoreline detection method to detect shorelines accurately and in real time. We define shoreline detection as the combination of water surface area segmentation and edge detection, the key to which is segmentation. To detect shorelines accurately and in real time, we propose an improved U-Net for water segmentation. This network is based on U-Net, using ResNet-34 as the backbone to enhance the feature extraction capability, with a concise decoder integrated attention mechanism to improve the processing speed while ensuring the accuracy of water surface segmentation. We also introduce transfer learning to improve training efficiency and solve the problem of insufficient data. When obtaining the segmentation result, the Laplace edge detection algorithm is applied to detect the shoreline. Experiments show that our network achieves 97.05% MIoU and 40 FPS with the fewest parameters, which is better than mainstream segmentation networks, and also demonstrate that our shoreline detection method can effectively detect shorelines in real time in various environments. [ABSTRACT FROM AUTHOR]

With the rapid development of offshore exploration, autonomous underwater vehicles (AUVs) have come in great diversities depending on their particular specifications in civil and military practices. We focus on AUV swarms in the shallow ocean for their potentialities in economic and military uses as well as in scientific investigations. Nowadays, the advanced technologies of perception, communication, and control in underwater environment give the AUV abilities to perform certain tasks in the shallow ocean. Still, many missions require the large-scale AUV swarms due to the complexities of the tasks and the uncertainties of the environment. In this paper, we start with the introduction of underwater technologies in AUVs. Then we induce the AUV swarms and present several key methods to form swarms and to keep collaboration. Finally, we propose some necessitates technological advances that can facilitate the application of the AUV swarms in the shallow ocean. [ABSTRACT FROM AUTHOR]

Post-COVID-19, there are frequent manpower shortages across industries. Many factories pursuing future technologies are actively developing smart factories and introducing automation equipment to improve factory manufacturing efficiency. However, the delay and unreliability of existing wireless communication make it difficult to meet the needs of AGV navigation. Selecting the right sensor, reliable communication, and navigation control technology remains a challenging issue for system integrators. Most of today's unmanned vehicles use expensive sensors or require new infrastructure to be deployed, impeding their widespread adoption. In this paper, we have developed a self-learning and efficient image recognition algorithm. We developed an unmanned vehicle system that can navigate without adding any specialized infrastructure, and tested it in the factory to verify its usability. The novelties of this system are that we have developed an unmanned vehicle system without any additional infrastructure, and we developed a rapid image recognition algorithm for unmanned vehicle systems to improve navigation safety. The core contribution of this system is that the system can navigate smoothly without expensive sensors and without any additional infrastructure. It can simultaneously support a large number of unmanned vehicle systems in a factory. [ABSTRACT FROM AUTHOR]

This article presents the essential abilities and limitations of various sensors used for object recognition in the operation environment of unmanned ground vehicles (UGVs). The use of autonomous and unmanned vehicles for reconnaissance and logistics purposes has attracted attention in many countries. There are many different applications of mobile platforms in both civilian and military fields. Herein, we introduce a newly developed manned–unmanned high-mobility vehicle called TAERO that was designed for public roads and off-road operation. Detection for unmanned mode is required in both on-road and off-road environments, but the approach to identify drivable pathway and obstacles around a mobile platform is different in each environment. Dense vegetation and trees can affect the perception system of the vehicle, causing safety risks or even collisions. The main aim was to define the limitations of the perception system in off-road environments, as well as associated challenges and possible future directions for practical applications, to improve the performance of the UGV in all-terrain conditions. Recorded datasets were used to verify vision and laser-based sensors in practical application. The future directions of work to overcome or minimize the indicated challenges are also discussed. [ABSTRACT FROM AUTHOR]

Integrity monitoring for precise point positioning is critical for safety-related applications. With the increasing demands of high-accuracy autonomous navigation for unmanned ground and aerial vehicles, the integrity monitoring method of high-precision positioning has become an essential requirement. While high precision Global Navigation Satellite Systems (GNSS) positioning is widely used in such applications, there are still many difficulties in the integrity monitoring method for the multi-frequency multi-GNSS undifferenced and uncombined Precise Point Positioning (PPP). The main difficulties are caused by using the measurements of multiple epochs in PPP. Based on the baseline Multiple Hypothesis Solution Separation (MHSS) Advanced Receiver Autonomous Integrity Monitoring (ARAIM) algorithm, this paper discusses the feasibility of the pseudorange-based baseline ARAIM method on the single-epoch PPP based on Real-Time Kinematic (RTK) networks (PPP-RTK) framework to overcome these difficulties. In addition, a new scheme is proposed to transfer the conventional PPP process into the single-epoch PPP-RTK framework. The simulation results using the proposed model are analyzed in this study. The Protection Levels (PLs) estimated by PPP Wide-lane Ambiguity Resolution (PPP-WAR) model with regional corrections can reach the meter level and the PLs estimated by PPP Ambiguity Resolution (PPP-AR) and PPP-RTK models are usually the sub-meter level. Given a horizontal Alert Limit (AL) of 1.5 m, the global coverage of availability above 99.9% for PPP-WAR, PPP-AR, and PPP-RTK can reach 92.6%, 99.4%, and 99.7% respectively. The results using real kinematic data also show that tight PLs can be achieved when the observation conditions are good. [ABSTRACT FROM AUTHOR]

Driver assistance systems have started becoming a key differentiator in automotive space and all major automotive manufacturers have such systems with various capabilities and stages of implementation. The main building blocks of such systems are similar in nature and one of the major building blocks is road lane detection. Even though lane detection technology has been around for decades, it is still an ongoing area of research and there are still several improvements and optimizations that are possible. This paper offers an Optimized Dynamic Origin Technique (Optimized DOT) for lane detection. The proposed optimization algorithm of optimized DOT gives better results in performance and accuracy compared to other methods of lane detection. Analysis of proposed optimized DOT with various edge detection techniques, various threshold levels, various sample dataset and various lane detection methods were done and the results are discussed in this paper. The proposed optimized DOT lane detection average processing time increases by 9.21 % when compared to previous Dynamic Origin Technique (DOT) and 59.09 % compared to traditional hough transform. [ABSTRACT FROM AUTHOR]

A part of the International Atomic Energy Agency (IAEA)'s response to the accident at TEPCO Fukushima Daiichi nuclear power plant was the Action Plan on Nuclear Safety. This report covers a 10-year period during which the UAV industry has matured immensely and there have been a number of significant developments in UAV technology and detector systems tested and deployed (e.g., see Ref. [[1]] and references therein). Development of the UAV-Based System The UAV System The selected UAV system was an Aibotix X6 [[6]]. [Extracted from the article]

When considering the working conditions under which an unmanned surface vehicle (USV) operates, the navigational sensors, which already have inherent uncertainties, are subjected to environment influences that can affect the accuracy, security and reliability of USV navigation. To combat this, multi-sensor data fusion algorithms will be developed in this paper to deal with the raw sensor measurements from three kinds of commonly used sensors and calculate improved navigational data for USV operation in a practical environment. Unscented Kalman Filter, as an advanced filtering technology dedicated to dealing with non-linear systems, has been adopted as the underlying algorithm with the performance validated within various computer-based simulations where practical, dynamic navigational influences, such as ocean currents, provide force against the vessel's structure, are to be considered. [ABSTRACT FROM AUTHOR]

Fault detection and diagnosis technologies for unmanned ground vehicles are important for ensuring safety and reliability. Due to the complexity and uncertainty of unmanned ground vehicles, it is challenging to realize accurate and fast fault detection and diagnosis. For the purpose of solving the data-driven fault detection and diagnosis problems of unmanned ground vehicles, improving the diagnostic accuracy and shortening the training time, a novel fault detection and diagnosis method is proposed, which is called JITGP-ELM. In the proposed method, a model estimator based on the just-in-time Gaussian process is designed for the online residual generation to cope with the dynamics and nonlinearity of systems. A fault classifier using Extreme Learning Machine is designed for fault identification with residuals extracted by the just-in-time Gaussian process modelling. The proposed method has online adaptability, noise-resistant ability, and high generalization. A field test on a real unmanned ground vehicle's steering-by-wire system demonstrates the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The global greenhouse effect and air pollution problems have been deteriorating in recent years. The power generation in the future is expected to shift from fossil fuels to renewables, and many countries have also announced the ban on the sale of vehicles powered by fossil fuels in the next few decades, to effectively alleviate the global greenhouse effect and air pollution problems. In addition to electric vehicles (EVs) that will replace traditional fuel vehicles as the main ground transportation vehicles in the future, unmanned aerial vehicles (UAVs) have also gradually and more recently been widely used for military and civilian purposes. The recent literature estimated that UAVs will become the major means of transport for goods delivery services before 2040, and the development of passenger UAVs will also extend the traditional human ground transportation to low-altitude airspace transportation. In recent years, the literature has proposed the use of renewable power supply, battery swapping, and charging stations to refill the battery of UAVs. However, the uncertainty of renewable power generation cannot guarantee the stable power supply of UAVs. It may even be very possible that a large number of UAVs need to be charged during the same period, causing congestion in charging stations or battery swapping facilities and delaying the arranged schedules of UAVs. Although studies have proposed the method of that employing moving EVs along with wireless charging technology in order to provide electricity to UAVs with urgent needs, the charging schemes are still oversimplified and have many restrictions. In addition, different charging options should be provided to fit the individual need of each UAV. In view of this, this work attempts to meet the mission characteristics and needs of various UAVs by providing an adaptive flight path and charging plan attached to individual UAVs, as well as reducing the power load of the renewable power generation during the peak period. We ran a series of simulations for the proposed flight path and charging mechanism to evaluate its performance. The simulation results revealed that the solutions proposed in this work can be used by UAV operators to fit the needs of each individual UAV. [ABSTRACT FROM AUTHOR]

Electrostatic discharge is a common phenomenon in daily life, and the electromagnetic pulse radiation generated during discharge can cause great harm to power, communication, sensing, and other equipment, resulting in systems not working properly. To verify the safety and reliability of unmanned vehicle handling systems in complex electromagnetic environments, the interference effect of electrostatic discharge, a common source of electromagnetic interference in life, on unmanned vehicle systems was studied. According to the electrostatic discharge interference mechanism, typical electrostatic discharge mode, and discharge model, an unmanned vehicle handling system was tested for electrostatic discharge according to the ISO10605-2008 standard. Based on the measured data, the effect of electrostatic discharge on the safety and functionality of the unmanned vehicle handling system and its sensors was analyzed, and threshold values for the failure of the unmanned vehicle handling system under different discharge methods, discharge models, and discharge polarity were obtained. When the electrostatic discharge voltage amplitude is only 2 kV, the vehicle's LiDAR data sensor cannot work normally due to the echo reception circuit, and the failure rate of LiDAR continues to increase with increasing discharge voltage. When the discharge voltage is only 4 kV, the millimeter-wave radar is disconnected from the vehicle module due to the electrostatic discharge interference of the transmission cable, and when the discharge voltage is 12 kV, the unmanned vehicle is unable to provide stable and accurate environmental information to avoid collisions. This study provides a reference for the design of electromagnetic protection of unmanned equipment and will have a guiding role in enhancing the construction of reliable, safe, and intelligent equipment in complex electromagnetic environments. [ABSTRACT FROM AUTHOR]

As an important sensor of an unmanned surface vehicle (USV), an electro-optical device is usually used to detect ships and obstacles in USV autonomous navigation and collision avoidance. However, the installation perpendicularity error of the electro-optical device greatly impacts the line-of-sight (LOS) stability control. This error is difficult to eliminate through mechanical calibration because the platform inertial navigation axis cannot be led out. This study aims to establish the model for the perpendicularity error of electro-optical devices during circumferential scanning and analyze its impact on the stability of LOS. In addition, we present a measurement technique for perpendicularity errors utilizing sea–sky line images. Through this method, we find an error function of LOS elevation angle, which is a convex function that can quickly search out high-precision perpendicularity errors step by step. Finally, we measured and compensated the perpendicularity error according to experimental data collected by the electro-optical device. The findings of this research demonstrate that the suggested approach can efficiently mitigate low-frequency disruptions and minor amplitude high-frequency vibrations of LOS in the elevation direction. As a result, it considerably enhances the precision of stability and image observation effect of electro-optical devices. [ABSTRACT FROM AUTHOR]

In this study, we present a hybrid approach of Ant Colony Optimization algorithm (ACO) with fuzzy logic and clustering methods to solve multiobjective path planning problems in the case of swarm Unmanned Surface Vehicles (USVs). This study aims to further explore the performance of the ACO algorithm by integrating fuzzy logic in order to cope with the multiple contradicting objectives and generate quality solutions by in-parallel identifying the mission areas of each USV to reach the desired targets. The design of the operational areas for each USV in the swarm is performed by a comparative evaluation of three popular clustering algorithms: Mini Batch K-Means, Ward Clustering and Birch. Following the identification of the operational areas, the design of each USV path to perform the operation is performed based on the minimization of traveled distance and energy consumption, as well as the maximization of path smoothness. To solve this multiobjective path planning problem, a comparative evaluation is conducted among ACO and fuzzy inference systems, Mamdani (ACO-Mamdani) and Takagi–Sugeno–Kang (ACO-TSK). The results show that depending on the needs of the application, each methodology can contribute, respectively. ACO-Mamdani generates better paths, but ACO-TSK presents higher computation efficiency. [ABSTRACT FROM AUTHOR]

Unmanned surface vehicles (USVs) are increasingly used for ocean missions and services aimed for safer, more efficient, and sustainable routine operations. Path planning is a key component of autonomy addressed to obstacle detection and avoidance. As a multi-optimization nonlinear problem, it should include computational time, optimal path, and maritime traffic standard procedures. This becomes even more challenging for USV technologies propelled by harvesting ocean energy from waves and wind. Sea current state and wind conditions significantly affect the USV energy consumption becoming the path planning approach key for navigation performance and endurance. To improve both aspects, an energy-efficient new path planning algorithm approach based on AI techniques for computing feasible paths in compliance with the Convention on the International Regulations for Preventing Collisions at Sea (COLREG) rules and taking energy consumption into account according to wind and sea current data is proposed. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the path search rules in the traditional path planning methods are divorced from the actual maneuverability of an unmanned surface vehicle (USV), a motion planning method of state prediction rapidly exploring random tree (spRRT) is proposed. This method retains the discrete search of the original rules of RRT while adding the continuity of the motion of USV. Firstly, the state information for each movement (position, yaw angle, velocity, etc.), is calculated based on the mathematical model of USV's motion which takes into account the complete dynamic constraints. Secondly, this information is added to the RRT path search rules to predict the state points that can be reached by the USV. Furthermore, in order to improve search efficiency and reduce cost, spRRT is enhanced by an elliptic sampling domain (spRRT-Informed). The simulation results indicate that spRRT can effectively plan smooth paths for smoothly navigating USV. The inclusion of the USV motion model has improved steering performance by an average of over 40%. Additionally, the spRRT-Informed enhanced with sampling optimization strategy improves performance by at least 10% over spRRT in terms of sailing time and distance of the path. The results of the simulation conducted in a realistic scenario validate that spRRT-Informed can be used as a reference for practical applications. [ABSTRACT FROM AUTHOR]

This article presents a study on the fuzzy control of self-balancing, two-wheel-driven, simultaneous localization and mapping (SLAM)-based, unmanned systems for Agriculture 4.0 applications. The background highlights the need for precise and efficient navigation of unmanned vehicles in the field of agriculture. The purpose of this study is to develop a fuzzy control system that can enable self-balancing and accurate movement of unmanned vehicles in various terrains. The methods employed in this study include the design of a fuzzy control system and its implementation in a self-balancing, two-wheel-driven, SLAM-based, unmanned system. The main findings of the study show that the proposed fuzzy control system is effective in achieving accurate and stable movement of the unmanned system. The conclusions drawn from the study indicate that the use of fuzzy control systems can enhance the performance of unmanned systems in Agriculture 4.0 applications by enabling precise and efficient navigation. This study has significant implications for the development of autonomous agricultural systems, which can greatly improve efficiency and productivity in the agricultural sector. Fuzzy control was chosen due to its ability to handle uncertainty and imprecision in real-world applications. [ABSTRACT FROM AUTHOR]

Track slippage and body sinking of the tracked mining vehicle in the traditional deep-sea mining system are the critical issues for operating stability. To solve this bottleneck problem, a novel ROV-based deep-sea mining system is proposed in this study, in which a remotely-operated vehicle (ROV) towering a sledge-shaped mining robot (MRT) named ROV-based Deep-sea Mining Vehicle (ROVDMV) is instead of the traditional tracked Deep-sea mining vehicle. The design of the ROVDMV can fundamentally overcome the bottleneck problem. However, the complex marine environment and multi-rigid-body design of the ROVDMV pose new challenges for its path-tracking control. Firstly, the dynamic model of the ROVDMV considering the ROV at a fixed depth is established based on the bicycle model, which is mainly used as the control object in the numerical simulation. Secondly, a learning-based path-tracking control strategy is proposed for the path-tracking control of the ROVDMV. In the control strategy, a novel nonparametric learning (NPL) method is introduced to learn the uncertain nonlinear dynamics considering the external disturbances and parametric uncertainty. The NPL method is proven to provide bounded estimated error. Besides, the enhanced NPL method can save approximately 33 % of the computation time, and the average computation time for its optimization control problem is only 12.47 ms. Finally, the numerical results show that the NPL method can learn nonlinear dynamics accurately, and the proposed strategy has proven to be effective. [ABSTRACT FROM AUTHOR]

• A new criterion for the boundedness and asymptotic stability of NCN on time scales under impulsive control is proposed. • The results show that constrained nonlinearity plays an important role in the global boundedness of nonautonomous complex networks under impulsive control. • An application is given for the bounded driving of unmanned vehicles. This paper discusses time-scale type global boundedness of nonautonomous complex networks (NCNs) under impulsive control. By using constraint nonlinearity of NCNs, properties of exponential function and impulsive inequalities, we establish new sufficient conditions for global boundedness and asymptotic stability of NCNs on time scales, and the upper bound of the system can be calculated easily. It is shown that the constraint nonlinearity and impulse interval length will affect the estimation of state bound of NCNs under different network size. It provides a unified criteria for global boundedness of NCNs on continuous, discrete and mixed-time domains and hence improves corresponding existing ones. The nonlinear constraints studied in this paper are widely applied to Lurie systems, ball motion models and so on. Numerical examples are given to illustrate the correctness of the results. Finally, the theoretical results are applied to Lurie systems and bounded driving of unmanned vehicles. [ABSTRACT FROM AUTHOR]

Aiming at the challenges to the accurate and stable heading control of underactuated unmanned surface vehicles arising from the nonlinear interference caused by the overlay and the interaction of multi interference, and also the uncertainties of model parameters, a heading control algorithm for an underactuated unmanned surface vehicle based on an improved backpropagation neural network is proposed. Based on applying optimization theory to realize that the underactuated unmanned surface vehicle tracks the desired yaw angle and maintains it, the improved momentum of weight is combined with an improved tracking differentiator to improve the robustness of the system and the dynamic property of the control. A hyperbolic tangent function is used to establish the nonlinear mappings an approximate method is adopted to summarize the general mathematical expressions, and the gradient descent method is applied to ensure the convergence. The simulation results show that the proposed algorithm has the advantages of strong robustness, strong anti-interference and high control accuracy. Compared with two commonly used heading control algorithms, the accuracy of the heading control in the complex environment of the proposed algorithm is improved by more than 50%. [ABSTRACT FROM AUTHOR]

Collaborative exploration using unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) has become increasingly popular in academia over the past decade. This study addresses the task of autonomously landing a UAV on a ground vehicle in rough terrain, which is fundamental, yet challenging, for UAV-UGV collaborative exploration. The method proposed in this paper estimates the relative pose and velocity between UAV and UGV and uses model predictive control for UAV trajectory planning while considering the field of view of camera onboard the UAV. Through a simulation study, we confirmed that the proposed method enables a UAV to accurately track the UGV traversing in rough terrain, and to land on it with 100% success rate at the UGV velocity of 1.0 m/s and still 50% at 3.0 m/s. [ABSTRACT FROM AUTHOR]

With the rapid development of unmanned ground vehicle industry, how to achieve continuous, reliable, and high-accuracy navigation becomes very important. At present, the integrated navigation with global navigation satellite system and strapdown inertial navigation system is the most mature and effective navigation technology for unmanned ground vehicle. However, this technique depends on the signal accuracy of global navigation satellite system. When the receiver cannot capture four or more satellite signals for a long time or the satellite completely invalid, it cannot provide accurate navigation and positioning information for the unmanned ground vehicle. Therefore, this article combine the observation information of strapdown inertial navigation system, global navigation satellite system, and laser Doppler velocimeter to propose a high-precision seamless navigation technique of unmanned ground vehicle based on state transformation extended Kalman filter. Under different land vehicle driving environments and global navigation satellite system signal quality conditions, the seamless navigation technique is evaluated through global navigation satellite system interruption simulation and land vehicle experiments. The experimental results show that the strapdown inertial navigation system/global navigation satellite system/laser Doppler velocimeter tightly coupled integration seamless navigation has good environmental adaptability and reliability and can maintain high navigation accuracy under high frequency global navigation satellite system–signal blockage conditions in urban areas. [ABSTRACT FROM AUTHOR]

To overcome the limitations of a single unmanned surface vehicle (USV), this study investigated the swarm control algorithms of USVs. Among various swarm control methods, a leader–follower swarm control method was selected and studied. The performance of the swarm algorithm proposed in this study was verified through an actual sea area test. A USV was designed and manufactured by dividing the power and communication parts. The power system was equipped with a coulometer to monitor the battery state in real time to protect the system through a switch linked to the coulometer in the case of abnormalities in the battery and to prevent accidents. In addition, a communication system was established to process the sensor data and camera image data of the USV in real time. Consequently, the desired swarm formation was achieved through the separately constructed swarm control algorithm. Before the actual sea area test, individual performance tests of each sensor were conducted. Finally, the performance of the swarm algorithm was verified by conducting a sea area test. The performance of the controllers was confirmed to be good, and the swarm formation was confirmed to be successful. [ABSTRACT FROM AUTHOR]

Considering autonomous navigation of an unmanned ground vehicle (UGV) in off-road environments, it faces various problems, such as semantic perception and motion planning. This paper proposes an intelligent approach to perception and planning for UGV in field environments. Firstly, a semantic image of environment is generated in real time based on an improved Convolutional Neural Network (CNN). Secondly, we provide two practical extensions to an open-source 3D mapping framework. One is the semantic point cloud fusion based on 3D LIDAR and Camera, and the other is the generation of traversability cost map using both semantic and geometric information. Thirdly, we propose a new kinodynamic semantic-aware planner which adds the dynamic window approach to the receding horizon planner so that the latter can meet the kinodynamic while perceiving semantic labels. Finally, the above methods, along with a localization module, are integrated into a complete autonomous navigation system with real-time semantic perception and motion planning (RSPMP). In the experiments, the proposed method was successfully applied for safe autonomous perception navigation in off-road environments. [ABSTRACT FROM AUTHOR]

A deep reinforcement learning method to achieve complete coverage path planning for an unmanned surface vehicle (USV) is proposed. This paper firstly models the USV and the workspace required for complete coverage. Then, for the full-coverage path planning task, this paper proposes a preprocessing method for raster maps, which can effectively delete the blank areas that are impossible to cover in the raster map. In this paper, the state matrix corresponding to the preprocessed raster map is used as the input of the deep neural network. The deep Q network (DQN) is used to train the complete coverage path planning strategy of the agent. The improvement of the selection of random actions during training is first proposed. Considering the task of complete coverage path planning, this paper replaces random actions with a set of actions toward the nearest uncovered grid. To solve the problem of the slow convergence speed of the deep reinforcement learning network in full-coverage path planning, this paper proposes an improved method of deep reinforcement learning, which superimposes the final output layer with a dangerous actions matrix to reduce the risk of selection of dangerous actions of USVs during the learning process. Finally, the designed method validates via simulation examples. [ABSTRACT FROM AUTHOR]

This paper proposes the study of a robust control scheme for an Unmanned Surface Vehicle (USV) in a urban waterway using Visual Odometry (VO) for position estimation of the vehicle instead of traditional sensors. For different applications, a USV is required to operate in autonomous navigation mode and under various disturbances inherent to its environment as currents and waves. To achieve efficient operation, a robust control scheme is required to cope with such disturbances. The autonomous navigation is achieved by using VO to estimate the vehicle's position for denied Global Positioning Systems (GPS) environments. A robust control scheme based on Sliding Modes (SM) control theory is designed and its convergence is guaranteed by means of Lyapunov analysis. The tracking capabilities under disturbance conditions using VO for position estimation are verified in simulation using the virtual world tool from Matlab and Simulink. [ABSTRACT FROM AUTHOR]

We propose an effective vehicle tracker and speed estimation method from Unmanned Aerial Vehicles (UAVs) videos that can be deployed on UAV-embedded edge devices. Our tracker uses segmentation-derived vehicle regions to initialise a MOSSE tracker. This enables road operators to make multipurpose use of segmentation outputs while still being able to track the vehicles across frames. The vehicle speed is estimated using flight parameters derived from the UAV's flight computer and the vehicle displacement across frames. We trained CABiNet on the UAVid urban segmentation benchmark dataset and finetuned it on a dataset collected at our study site. A mean Intersection over Union (mIoU) of 0.73 was obtained for the vehicle class. Our segmentation-initialised MOSSE tracker was evaluated on the VisDrone Multi-Object Tracking (MOT) benchmark dataset and compared against traditional methods that utilise object regions for tracker initialisation. Our approach yielded a Multi-Object Tracking Precision (MOTP) of 0.872 compared to 0.830 when using YOLOv4. Our vehicle speed estimations approach was evaluated using a privately collected ground truth vehicle speed dataset. Our approach yielded a Root Mean Square Error (RMSE) between 3.42 and 16.12 km/hr across different flight configurations. Finally, our approach was deployed on an NVIDIA Jetson Xavier NX edge device and could be executed at 8 Frames Per Second (FPS). The results indicate that our approach is a simple yet fast alternative to traditional tracking methods while producing multipurpose segmentation information. [ABSTRACT FROM AUTHOR]

Improving models for managing the networks of firefighting unmanned ground vehicles in crowded areas, as a recommendation system (RS), represented a difficult challenge. This challenge comes from the peculiarities of these types of networks. These networks are distinguished by the network coverage area size, frequent network connection failures, and quick network structure changes. The research aims to improve the communication network of self-driving firefighting unmanned ground vehicles by determining the best routing track to the desired fire area. The suggested new model intends to improve the RS regarding the optimum tracking route for firefighting unmanned ground vehicles by employing the ant colony optimization technique. This optimization method represents one of the swarm theories utilized in vehicles ad–hoc networks and social networks. According to the results, the proposed model can enhance the navigation of self-driving firefighting unmanned ground vehicles towards the fire region, allowing firefighting unmanned ground vehicles to take the shortest routes possible, while avoiding closed roads and traffic accidents. This study aids in the control and management of ad–hoc vehicle networks, vehicles of everything, and the internet of things. [ABSTRACT FROM AUTHOR]

Aiming at the situation that small unmanned surface vehicle (USV) encounters unknown disturbance during low speed sailing, a course controller with finite time stability is designed. To solve this problem, we construct an undisturbed ideal navigation model which simply meets the stability requirements, and constructs an adaptive sliding mode surface. The control under finite time approach law is also introduced. The model under perturbation can land on the sliding mode surface in finite time and then synchronize with the ideal navigation model. The adaptive control was applied in the implementation of power control for the thruster structure, so as to ensure the tracking of the desired course within the finite time, and satisfy the needs for the stable system performance. Lyapunov direct method is used to strictly prove that the designed controller can ensure the system which converges to the steady state value in a given time period. Simulation results show that the designed adaptive finite-time controller can ensure the stable course tracking of the USV with thruster structure at low speed, and meet the requirements of the course robustness of the USV under dynamic conditions. [ABSTRACT FROM AUTHOR]

This paper is concerned with the output tracking control for a class of network-based nonlinear unmanned marine vehicle (UMV) systems subject to denial-of-service (DoS) attacks, where a Takagi–Sugeno (T–S) fuzzy approach is proposed to grapple with the nonlinearity of UMV. A semi-Markov chain is introduced to reveal the switches of different attack degrees on actuators. The transition probabilities (TPs) and the sojourn-time probability density functions (ST-PDFs) of the introduced semi-Markov chain are partially unavailable because of unpredictable DoS attacks. Through introducing the upper bound of the sojourn time for each attack mode and giving an enlarged known threshold to unavailable TPs and/or ST-PDFs, the sufficient conditions for the σ -mean square stability (σ -MSS) with performance of output tracking system are derived in the form of linear matrix inequalities (LMIs). Attack-tolerant controllers are then designed to overcome those stochastic DoS attacks. Finally, the effectiveness of the proposed control scheme is verified by a simulation study. [ABSTRACT FROM AUTHOR]

Cooperative formation control of unmanned ground vehicles (UGVs) has become one of the important research hotspots in the application of UGV and attracted more and more attention in the military and civil fields. Compared with traditional formation control algorithms, reinforcement-learning-based algorithms can provide a new solution with a lower complexity for real-time formation control by equipping UGVs with artificial intelligence. Therefore, in this paper, a distributed deep-reinforcement-learning-based cooperative formation control algorithm is proposed to solve the navigation, maintenance, and obstacle avoidance tasks of UGV formations. More importantly, the hierarchical triangular formation structure and the newly designed Markov decision process for UGV formations of leader and follower attributes make the control strategy learned by the algorithm reusable, so that the formation can arbitrarily increase the number of UGVs and realize a more flexible expansion. The effectiveness and scalability of the algorithm is verified by formation simulation experiments of different scales. [ABSTRACT FROM AUTHOR]

The application of robotic systems is widespread in all fields of life and sport. Tennis ball collection robots have recently become popular because of their potential for saving time and energy and increasing the efficiency of training sessions. In this study, an unmanned and autonomous tennis ball collection robot was designed and produced that used LiDAR for 2D mapping of the environment and a single camera for detecting tennis balls. A novel method was used for the path planning and navigation of the robot. A fuzzy controller was designed for controlling the robot during the collection operation. The developed robot was tested, and it successfully detected 91% of the tennis balls and collected 83% of them. [ABSTRACT FROM AUTHOR]

The trajectory tracking method for an unmanned surface vehicle (USV) using integral terminal sliding mode integral backstepping adaptive control (ITSMIBAC) scheme is presented in this paper, which takes into account external disturbances and uncertain model parameters. Furthermore, finite-time convergence and excellent track tracking effect are guaranteed by the differential equations that is made up of the integral terminal sliding mode function. In particular, a backstepping method adding integral term is used to ensure the globally asymptotic stability of system and make the differential equations to converge to zero. The upper bound of the disturbances including external disturbances and uncertain model parameters is estimated by an adaptive control law. Finally, the effectiveness and the feasibility of the proposed controller are demonstrated by numerical example and comparison result through tracking circular orbit. [ABSTRACT FROM AUTHOR]

This paper is concerned with the first work on the integration of digital twin (DT), 5G, cloud platform, and virtual reality (VR) technologies for unmanned aerial vehicles (UAVs) autonomy development. DT focuses on connecting the virtual and physical world as an emerging strategic technology. Initially, it was implemented through mirror models of physical objects to realize the monitoring of their whole life cycle in the manufacturing area. In recent years, DT technologies have been applied in different fields, and some typical DT solutions have been proposed to solve complex system problems. In this paper, we study the problem of how to combine the DT and other emerging technologies for UAV autonomy development and supervision, aiming to propose a basic DT framework to integrate DT and UAVs as reference rules for building DT systems, which includes four parts, that is, Virtual Space, Real Space, Service Center, and Data and Model Processing Center. Based on the proposed basic DT framework, a cloud-based DT system is then further constructed in which cloud platform, 5G, and VR are integrated seamlessly. The running and implementation processes of each subsystem are introduced in detail. Multiple experiments are conducted to verify the usefulness of proposed DT system, that is, real-time system monitoring and cloud processing, VR connection, human–robot interaction through VR technology, and so on. The experimental results show that the proposed DTUAV system can be used in the interaction of virtual and physical systems, remote supervision, intelligence integration of swarm of unmanned vehicles, and so on. The development in our work introduces the DT into unmanned system applications and can promote relevant research in this direction. All implementation codes of the system will be shared in https://github.com/DTUAV. [ABSTRACT FROM AUTHOR]

• Teleoperation of vehicles has safety and economic benefits. • Time delay in the cellular network is a key challenge for safe teleoperation. • Effects of small delays on performance in simulated inter-urban scenarios were tested. • A 250 ms delay significantly impinged on performance relative to a 50 ms delay. • Policy makers should consider that even small delays matter. Time delay in communication networks is a major challenge in controlling vehicles from remote centers (also known as teleoperation). In the current study, we investigated the effects of time delay in controlled and partly controlled driving environments at higher driving speeds and in relatively short time delays (50 ms, 150 ms, and 250 ms) which characterise the 5G era. Participants (N = 72) faced several challenges while operating a vehicle in a simulated scenario. The challenges started with following a lead vehicle on a curve, continued with responding to a sudden brake of the lead vehicle, and concluded with highway driving among other simulated road users. The speed, the distance to other vehicles, the brake and swerving of the vehicle and the crash rate were measured during each scenario. At the end of the scenario, participants completed the NASA-TLX. We found that at a time delay of 250 ms (but not 150 ms), following a lead vehicle on a curved road was characterized by a higher standard deviation of speed and distance to the lead vehicle and stronger swerving than at the shorter time delay of 50 ms. Swerving was also stronger at the 250 ms than at the 50 ms delay on the highway. Finally, crashes, which occurred mainly on the highway and in response to the sudden brake of the lead vehicle (before the highway), were 20 % (not statistically significant) higher at the 250 ms than at the 50 ms delay. The gap in collision frequency between the 150 ms and 50 ms delay was lower (8 %) and not statistically significant. This relatively small gap corresponded with the insignificant changes between 150 ms and 50 ms delay on the other indices of driving safety that we analyzed (e.g., braking and swerving). Regarding the NASA-TLX, participants rated the 250 ms delay higher than the other delays on almost all dimensions except for effort on which they did not distinguish between the 250 ms and the 150 ms delay. Our findings expand current knowledge on the effects of time delay on teleoperation. In the Discussion section, we translate this knowledge into practical recommendations regarding possible delays in teleoperation and possible countermeasures for the time delay. [ABSTRACT FROM AUTHOR]

Aiming at the problems of discontinuous curvature of the planned path, difficulty in finding the optimal reference path and high control accuracy requirements in tracking control for unmanned vehicles in the process of parallel parking, an optimal reference path planning method based on arc line and multi-objective optimization function and a path tracking control strategy based on nonlinear model predictive control are proposed. Firstly, the upper and lower boundaries of the exercisable area are obtained by the tangent of the arc and the straight line, and the multi-objective optimization function is designed to obtain the optimal arc-line combination, and the optimal reference path is obtained by polynomial curve fitting. Secondly, a path-following controller is designed using nonlinear model predictive control. Finally, a comparative analysis is carried out in MATLAB/Simulink and Carsim with the controller based on pure tracking design. The results show that the optimal reference path curvature obtained by the proposed method is continuous without sudden change, and the designed tracking controller has better tracking accuracy. [ABSTRACT FROM AUTHOR]

An integral part of population monitoring within fisheries is ground‐based surveys of fish redds. Remotely piloted vehicles or drones (RPVs) could provide a complementary method but need verification due to a host of methodological differences. To compare methods, we counted summer Chinook redds (Oncorhynchus tshawytscha) (~6 m2 in size) using RPVs and compared them to ground‐based counts in the Wenatchee River (WA, USA). We found individual aerial counts were many times twice the corresponding ground counts. We also found large inter‐observer variability among aerial counters. The coefficient of variation among multiple aerial counts were 37%, 38%, and 50% across three sites, which are comparable to published variation in ground counts. We attribute inter‐observer variability to inherent uncertainties in redd identification similar to ground counting, and importantly, we did not see evidence that the clarity of substrate in the image influenced observer bias. Overall, our data suggest that redd counting using RPVs is an effective method, particularly in high‐density spawning locations. We conclude that RPV imagery accurately identifies redds in a clear, relatively wide (60 m) river, but suggest continuing research into increasing precision, limiting observer variability, and assessing the accuracy across methods and locations. [ABSTRACT FROM AUTHOR]