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This paper presents the method and results of surveying a dispersed radioactive field using an unmanned air vehicle (UAV) and an unmanned ground vehicle (UGV). A 35 GBq of 140 ${}^{140}$La material was distributed in a specific geometric l‐polygon pattern measuring a 120 m × $\times $ 20 m longitudinal rectangle and an 80 m × $\times $ 10 m transverse rectangle. Two methods were used to determine the amount and distribution of the lanthanum over the polygon including 20 plywood coupons distributed over the area and a UGV equipped with a Kromek GR1® driving over the area. The aerial survey was conducted using an unmanned aerial vehicle UAV carrying the Kromek GR1® while flying a traditional grid pattern and circular pattern at different elevations and speeds over the area. The data collected by the UAV were further postprocessed using N‐Visage, a 3D radiation modeling method developed by Createc, to create a model of the ground activity. This model was compared with the reference data collected on the ground by the UGV and was found to be in agreement with 11%. [ABSTRACT FROM AUTHOR]

It is critical to master a marine robot's energy system characteristics adequately to ensure that the robot can perform long‐endurance tasks in complex and ever‐changing marine environments. This paper presents, for the first time, an energy system modeling and analysis method for a marine robot driven by wind, solar, and wave composite energy, using Yulang II as an example. First, an onshore experiment method was designed, and then the conversion experiment of wind, sunlight, and electrical energy was carried out based on that experiment method. At the same time, based on computational fluid dynamics technology, a resistance pole curve considering wave energy capture is constructed. In addition, the attitude, relative motion, and battery constraints of the robot in the ocean are analyzed to construct the energy system model of Yulang II. Finally, the method and model proposed in this paper were compared and verified by simulation and field tests. The results indicated that the corrected forecast deviation was 5.71% in the typical marine environment of the experiment, which supported the study of ocean energy‐driven unmanned marine vehicles strongly. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) offer many advantages over ground vehicles, including quadruped robots, based on high maneuverability when performing exploration in complex and unknown environments. However, due to their limited computational capability, UAVs require lightweight but accurate state estimation algorithms for reliable exploration. In this paper, we propose a segmented map‐based exploration system based on light detection and ranging (LiDAR)‐based state estimation for UAVs. The proposed system includes capabilities such as exploration, obstacle avoidance, and object detection with localization using three‐dimensional (3D) dense maps generated by tightly coupled LiDAR Inertial Odometry (LIO). Our proposed system is a hybrid system that can switch between guided and exploration modes, making it practical for search and rescue missions in disaster scenarios. The proposed LIO algorithm adapts to its surroundings, allowing for fast and accurate state estimation in complex environments. The proposed exploration algorithm is designed to cover specific regions in the 3D dense map generated by the proposed LIO, with the UAV determining if map points are included within the coverage area. We tested the proposed system in both simulation and real‐world environments and validated that the proposed system outperforms state‐of‐the‐art algorithms in various aspects such as localization accuracy and exploration efficiency in complex environments. [ABSTRACT FROM AUTHOR]

This article researches the problem of trajectory tracking for unmanned marine vehicles (UMVs) under disturbances and denial‐of‐services (DoS) attacks in the wireless channel. An equivalent data‐driven model of UMVs with ocean disturbances is established by using partial form dynamic linearization algorithm. The disturbances and the input of UMVs have different pseudo partitioned Jacobean matrix, and the disturbances are estimated by using extended state observer, which improves the immunity of UMVs to disturbances in the environment. It is the first time that the DoS attacks are considered under the data‐driven model for UMVs, and a novel data‐driven adaptive trajectory tracking control framework is constructed. The article proposes an attack predictive compensation mechanism to mitigate their effects of DoS attacks, which follows the Bernoulli distribution. Based on it, the data‐driven adaptive trajectory tracking controller is designed such that the error of trajectory tracking is convergent under DoS attacks and external disturbances. Finally, the effectiveness of the proposed data‐driven control scheme and the predictive compensation mechanism is validated through the simulations. [ABSTRACT FROM AUTHOR]

The detection of river boundaries is a crucial branch of the intelligent perception of unmanned surface vehicles (USVs), it can be used to determine the driving areas of USVs, and also to ensure driving safety by limiting the effective drivable areas of USVs in the river areas. Aiming to detect the boundaries of incompletely structured river channels, this study proposes a real‐time detection method for river boundaries based on a Light Detection and Ranging (LiDAR) sensor. The point clouds that are disturbed by the water surface noise are filtered firstly, and then the spatial and geometric features are extracted separately from the point cloud detected above the water surface. To prevent the error detection and missing detection, the boundary point information is predicted and calibrated in real time by Extended Kalman Filter (EKF). A planning track generation algorithm for coastal autonomous cruise without relying on high‐precision maps, and a heading and distance adaptive control method by Proportional‐Integral‐Derivative (PID), and different driving line generation methods for driving along the narrow river and wide river are proposed respectively. The experimental data verification of river boundary detection shows that the algorithm is accurate, real‐time, and robust. [ABSTRACT FROM AUTHOR]

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]

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]

This article proposes a robust trajectory tracking controller for underactuated unmanned surface vehicles under disturbances. An adaptive integral terminal super‐twisting algorithm is designed for guidance and control, following a cascade strategy. The properties of such algorithm include practical finite‐time (PFT) convergence, robustness against bounded disturbances/uncertainties with unknown boundaries, and chattering attenuation via adaptive gains. A stability analysis is addressed to prove the PFT stability of the overall system. Moreover, the vehicle is subject to wind, waves, and water currents disturbance models to verify the robustness with numerical simulations. Furthermore, real‐time experiments under an extra payload and environmental perturbations with unknown boundaries validate the performance with a prototype. Finally, a comparison against the adaptive super‐twisting algorithm with conventional sliding surface further demonstrates the advantages of the proposed scheme. [ABSTRACT FROM AUTHOR]

Driven by advances in information and communication technologies, an increasing number of industries embrace unmanned and autonomous vehicles for services, such as public transportation, shipping, mapping, and remote surveillance. Unfortunately, these vehicles are vulnerable to passive and active cyber‐physical attacks that can be used for industrial espionage and hijacking attempts. Since attackers can use hijacked vehicles as weapons in terrorist attacks, ensuring the secure operation of such vehicles is critical to prevent the attacks from causing dire financial consequences, or worse, the loss of human lives. This study is motivated by the observation that most cybersecurity studies provide superficial, high‐level descriptions of vulnerabilities and attacks, and the true impact of the described attacks remains unclear. To address this problem, we demonstrate advanced manipulation attacks against an underactuated Unmanned Surface Vehicle (USV) which results in successful hijackings. Using state‐of‐the‐art cryptography, we also show how the signal transmission can be secured to avoid hijacking attempts actively steering the vehicle off course. Through field experiments, we demonstrate how the attacks affect the closed‐loop guidance, navigation, and control system and how the proposed countermeasures prevent these attacks from being successful. Our study is unique in that we provide a complete description of the attacked USV and give a detailed analysis of how spoofed navigation estimates affect the closed‐loop behavior of the underactuated USV. [ABSTRACT FROM AUTHOR]

In order to construct an efficient and reliable global path for unmanned surface vehicles (USVs), this paper proposes a USV global path planning algorithm based on an optimized ant colony algorithm (OACA). The algorithm constructs a USV global path planning model by comprehensively considering energy consumption costs and turning control costs. In order to ensure the adaptability of the model to the task environment, the USV's motion characteristics are analyzed with full consideration of external interference, and on this basis, the characterization of energy consumption costs and turning control costs is completed. In order to obtain the global optimal solution of the model and accelerate the convergence speed of the model, in the initialization process of the ant colony algorithm, the initial pheromone is distributed unevenly by introducing the distance relationship among the intermediate node, the starting point, and ending point to improve the initial search efficiency. In the iterative process of searching for the optimal solution, enhancing the guidance effect of the current optimal solution on the offspring ants by introducing a weight factor to improve the update rule of pheromone, which accelerates the convergence speed of the algorithm. In the last two steps of the deadlock path, the number of lost ants is reduced by introducing a penalty factor to punish the pheromone which can guarantee the diversity of ants, and the algorithm's premature convergence is overcome. The simulation results prove the effectiveness of this algorithm. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In this article, subject to both pose and velocity constraints within narrow waters, a self‐learning‐based optimal tracking control (SLOTC) scheme is innovatively created for an unmanned surface vehicle (USV) by deploying actor‐critic reinforcement learning (RL) mechanism and backstepping technique. To be specific, the barrier Lyapunov function (BLF) is devised to uniformly limit the states within a predefined region pertaining to a smoothly feasible reference trajectory. By virtue of a constrained Hamilton–Jacobi–Bellman (HJB) function, an actor‐critic control structure under backstepping is established by employing adaptive neural network identifiers which recursively updates actor and critic, simultaneously. Eventually, theoretical analysis proves that the entire SLOTC scheme can render all the states remain in the predefined compact set while tracking errors converge to an arbitrarily small neighborhood of the origin. Simulation results on a prototype USV demonstrate remarkable effectiveness and superiority. [ABSTRACT FROM AUTHOR]

Summary: The article proposes a robust adaptive framework for platoon of uncertain unmanned surface vehicles (USVs) subject to stochastic environmental loads. The disturbances induced by waves, wind, and ocean currents in the kinetics are decomposed into deterministic and stochastic components. The deterministic components can be treated as unknown constants, while stochastic components are regarded as Gaussian random disturbances. The stochastic additive noises are also included in the kinematics which stands for the un‐modeled dynamics and uncertainty. A comprehensive model including kinematics and kinetics of each USV agent is then derived as stochastic differential equations including standard Wiener processes. Thus, the problem formulation is much more challenging and practical since both the exogenous disturbances and kinematics states are defined by stochastic differential equations. Dynamic surface control technique, quartic Lyapunov functions synthesis, the projection algorithm, and neural networks are employed in order to guarantee that all the tracking errors are semi‐globally uniformly ultimately bounded in probability. Finally, the simulation experiments quantify the effectiveness of proposed approach. [ABSTRACT FROM AUTHOR]

This paper is concerned with the path following control for an underactuated unmanned surface vehicle (USV) subject to modeling error and input saturation under time‐varying external disturbances. In this note, a novel trajectory linearization control (TLC) method is first introduced in the design of path following controller for an unmanned surface vessel. Adaptive line‐of‐sight (LOS) algorithm is used in the navigation strategy, which not only solves the sideslip angle problem caused by external disturbances but also optimizes the convergence speed. The neural network minimum learning parameter (MLP) method is used to compensate for modeling errors and external disturbances. Compared with multi‐layer neural networks, MLP solves the problem of 'dimension disaster'. Meanwhile, in order to increase the robustness of the system, adaptive technology is employed to compensate for the compensation error of MLP. In addition, an auxiliary dynamic system is introduced into the controller design to address potential input saturation issue. Considering the above practical conditions, it is more convenient for the engineering implementation of the proposed control path following the control system. Using Lyapunov stability analysis theory, it is proven that all error signals in the system are uniformly ultimately bounded. Finally, two simulation experiments are presented to demonstrate the feasibility of the proposed control strategy. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper proposed a novel disturbance observer‐based control state‐error port‐controlled Hamiltonian (DOBC‐SEPCH) control strategy for optimization of energy consumption and enhancement of tracking performance for unmanned surface vehicle (USV) with unknown environmental disturbances via the PCH system techniques. Firstly, an observer is constructed to estimate disturbances. Then, an energy‐based controller is constructed by using the SEPCH system method. In addition, the SEPCH controller provided that the tracking errors converged exponentially to zero. The SEPCH technique is augmented by a disturbance observer. Due to the controller need to be designed in two stages, the stability of the whole system will be difficult to be discussed. We give the proof of the stability of the desired target dynamic system. The robustness and control performance of the system are enhanced. The simulation and comparison results illustrate the performance of this controller. [ABSTRACT FROM AUTHOR]

Integrating advantages of automated driving and mass transport, autonomous buses have broad development prospects. The research on the public acceptance of autonomous buses will help to provide a basis for formulating policies and measures, and thus have important theoretical and practical significance. In this paper, an initial theoretical model of acceptance of autonomous buses was established. An on‐line survey was conducted with a total of 401 valid samples collected. The reliability and validity tests of the data showed that the internal consistency of the questionnaire was high, and the reliability and validity all met the requirements. Based on the results of structural equation modelling, the proposed model was validated and all the hypotheses were confirmed. Attitude would positively affect behavioural intention, and trust and perceived usefulness would positively affect attitudes. Moreover, both perceived ease of use and perceived comfort would have positive effects on perceived usefulness as well as on trust. Perceived risk would be negatively associated with trust. Multi‐group analyses were conducted to test the moderating effects of the individual's education and knowledge of autonomous buses, and the two factors showed significant effects. Based on the results, corresponding measures can be taken to increase the acceptance of autonomous buses. [ABSTRACT FROM AUTHOR]

Ears are responsible for the sense of hearing, which is indispensable for a human to understand the environment. It is similar to an intelligent vehicle where acoustical data contains useful and valuable information to comprehend the environment to enable autonomous driving. One important application of acoustical data processing is to localize sound sources. However, due to the complexity of microphone array setup and the unstable localization performance against noises, few research articles tried to localize surrounding vehicles based on on‐board microphone arrays. The main contribution is the first implementation to combine roof‐mounted microphones and high definition maps as vehicle' s ears for online 3D localization of surround vehicles. This article designed a six‐microphones sensor array dedicated to the on‐board application and improved sound source localization algorithms using the observer technique to adapt to dynamic driving scenarios. A map‐based localization enhancement method is also proposed to make the system more robust to noises and model errors. Feasibility tests are conducted with real vehicles in the scenarios of pass‐by and overtaking. Experimental results validate the feasibility of using microphones to localize surrounding vehicles with lane‐level accuracy. [ABSTRACT FROM AUTHOR]

In case of emergency braking in danger, autonomous vehicle usually uses electronic stability control system to maintain the dynamic stability of the vehicle, and by adjusting the steering system, the vehicle can realize autonomous driving. However, when the autonomous vehicle is in emergency braking on the split‐mu road, due to the inconsistent adhesion coefficient on both sides of the road, the inconsistency of tire force will not only damage the dynamic stability, but also make the vehicle deviate from the planned trajectory. Because the path tracking algorithm of autonomous vehicle needs to detect the trajectory error first, it will lead to the time delay of trajectory error correction, and its correction ability will weaken with the decrease of speed, so that the vehicle's trajectory will deviate unilaterally. Thus, the tracking accuracy is reduced and the driving safety is endangered. This paper proposes a dynamic coordinated control strategy based on dynamics and kinematics. In response to the rapid changes in lateral disturbances, a flexible control transfer mechanism based on vehicle status is constructed, realize dynamic coordinated control based on path tracking algorithm and active steering algorithm. While improving the lateral stability, the trajectory error is reduced. [ABSTRACT FROM AUTHOR]