LSDA-APF:A Local Obstacle Avoidance Algorithm for Unmanned Surface Vehicles Based on 5G Communication Environment

In view of the complex marine environment of navigation,especially in the case of multiple static and dynamic obstacles,the traditional obstacle avoidance algorithms applied to unmanned surface vehicles(USV)are prone to fall into the trap of local optimization.Therefore,this paper proposes an improved artificial potential field(APF)algorithm,which uses 5G communication technology to communicate between the USV and the control center.The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios.Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks,the algorithm introduces the concept of dynamic artificial potential field.For the multiple obstacles encountered in the process of USV sailing,based on the International Regulations for Preventing Collisions at Sea(COLREGS),the USV determines whether the next step will fall into local optimization through the discriminationmechanism.The local potential field of the USV will dynamically adjust,and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions.The objective function and cost function are designed at the same time,so that the USV can smoothly switch between the global path and the local obstacle avoidance.The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment,and take navigation time and economic cost into account.

Average Secrecy Capacity of the Reconfigurable Intelligent Surface-Assisted Integrated Satellite Unmanned Aerial Vehicle Relay Networks

Integrated satellite unmanned aerial vehicle relay networks(ISUAVRNs)have become a prominent topic in recent years.This paper investigates the average secrecy capacity(ASC)for reconfigurable intelligent surface(RIS)-enabled ISUAVRNs.Especially,an eve is considered to intercept the legitimate information from the considered secrecy system.Besides,we get detailed expressions for the ASC of the regarded secrecy system with the aid of the reconfigurable intelligent.Furthermore,to gain insightful results of the major parameters on the ASC in high signalto-noise ratio regime,the approximate investigations are further gotten,which give an efficient method to value the secrecy analysis.At last,some representative computer results are obtained to prove the theoretical findings.

LSTM-DPPO based deep reinforcement learning controller for path following optimization of unmanned surface vehicle

To solve the path following control problem for unmanned surface vehicles(USVs),a control method based on deep reinforcement learning(DRL)with long short-term memory(LSTM)networks is proposed.A distributed proximal policy opti-mization(DPPO)algorithm,which is a modified actor-critic-based type of reinforcement learning algorithm,is adapted to improve the controller performance in repeated trials.The LSTM network structure is introduced to solve the strong temporal cor-relation USV control problem.In addition,a specially designed path dataset,including straight and curved paths,is established to simulate various sailing scenarios so that the reinforcement learning controller can obtain as much handling experience as possible.Extensive numerical simulation results demonstrate that the proposed method has better control performance under missions involving complex maneuvers than trained with limited scenarios and can potentially be applied in practice.

Cooperative multi-target hunting by unmanned surface vehicles based on multi-agent reinforcement learning

To solve the problem of multi-target hunting by an unmanned surface vehicle(USV)fleet,a hunting algorithm based on multi-agent reinforcement learning is proposed.Firstly,the hunting environment and kinematic model without boundary constraints are built,and the criteria for successful target capture are given.Then,the cooperative hunting problem of a USV fleet is modeled as a decentralized partially observable Markov decision process(Dec-POMDP),and a distributed partially observable multitarget hunting Proximal Policy Optimization(DPOMH-PPO)algorithm applicable to USVs is proposed.In addition,an observation model,a reward function and the action space applicable to multi-target hunting tasks are designed.To deal with the dynamic change of observational feature dimension input by partially observable systems,a feature embedding block is proposed.By combining the two feature compression methods of column-wise max pooling(CMP)and column-wise average-pooling(CAP),observational feature encoding is established.Finally,the centralized training and decentralized execution framework is adopted to complete the training of hunting strategy.Each USV in the fleet shares the same policy and perform actions independently.Simulation experiments have verified the effectiveness of the DPOMH-PPO algorithm in the test scenarios with different numbers of USVs.Moreover,the advantages of the proposed model are comprehensively analyzed from the aspects of algorithm performance,migration effect in task scenarios and self-organization capability after being damaged,the potential deployment and application of DPOMH-PPO in the real environment is verified.

Analysis of Design Directions for Unmanned Surface Vehicles (USVs)

In recent years, because of the development of marine military science technology, there is a growing interest in the unmanned systems throughout the world. Also, the demand of Unmanned Surface Vehicles (USVs) which can be autonomously operated without the operator intervention is increasing dramatically. The growing interests lie in the facts that those USVs can be manufactured at much lower costs, and can be operated without the human fatigue, while can be sent to the hostile or quite dangerous areas that are inherently unhealthy for human operators. The utilization and the deployment of such vessels will continue to grow in the future. In this paper, along with the technological development of unmanned surface vehicles, we investigate and analyze the cases of already developed platforms and identify the trends of the technological advances. Additionally, we suggest the future directions of development.

Application of A* Algorithm for Real-time Path Re-planning of an Unmanned Surface Vehicle Avoiding Underwater Obstacles

This paper describes path re-planning techniques and underwater obstacle avoidance for unmanned surface vehicle(USV) based on multi-beam forward looking sonar(FLS). Near-optimal paths in static and dynamic environments with underwater obstacles are computed using a numerical solution procedure based on an A* algorithm. The USV is modeled with a circular shape in 2 degrees of freedom(surge and yaw). In this paper, two-dimensional(2-D) underwater obstacle avoidance and the robust real-time path re-planning technique for actual USV using multi-beam FLS are developed. Our real-time path re-planning algorithm has been tested to regenerate the optimal path for several updated frames in the field of view of the sonar with a proper update frequency of the FLS. The performance of the proposed method was verified through simulations, and sea experiments. For simulations, the USV model can avoid both a single stationary obstacle, multiple stationary obstacles and moving obstacles with the near-optimal trajectory that are performed both in the vehicle and the world reference frame. For sea experiments, the proposed method for an underwater obstacle avoidance system is implemented with a USV test platform. The actual USV is automatically controlled and succeeded in its real-time avoidance against the stationary undersea obstacle in the field of view of the FLS together with the Global Positioning System(GPS) of the USV.

Dynamic modeling of wave driven unmanned surface vehicle in longitudinal profile based on D-H approach

Wave driven unmanned surface vehicle(WUSV) is a new concept ocean robot drived by wave energy and solar energy,and it is very suitable for the vast ocean observations with incomparable endurance.Its dynamic modeling is very important because it is the theoretical foundation for further study in the WUSV motion control and efficiency analysis.In this work,the multibody system of WUSV was described based on D-H approach.Then,the driving principle was analyzed and the dynamic model of WUSV in longitudinal profile is established by Lagrangian mechanics.Finally,the motion simulation of WUSV and comparative analysis are completed by setting different inputs of sea state.Simulation results show that the WUSV dynamic model can correctly reflect the WUSV longitudinal motion process,and the results are consistent with the wave theory.

Path Planning Method Based on D^(*) lite Algorithm for Unmanned Surface Vehicles in Complex Environments

In recent decades,path planning for unmanned surface vehicles(USVs)in complex environments,such as harbours and coastlines,has become an important concern.The existing algorithms for real-time path planning for USVs are either too slow at replanning or unreliable in changing environments with multiple dynamic obstacles.In this study,we developed a novel path planning method based on the D^(*) lite algorithm for real-time path planning of USVs in complex environments.The proposed method has the following advantages:(1)the computational time for replanning is reduced significantly owing to the use of an incremental algorithm and a new method for modelling dynamic obstacles;(2)a constrained artificial potential field method is employed to enhance the safety of the planned paths;and(3)the method is practical in terms of vehicle performance.The performance of the proposed method was evaluated through simulations and compared with those of existing algorithms.The simulation results confirmed the efficiency of the method for real-time path planning of USVs in complex environments.

Trajectory tracking control for underactuated unmanned surface vehicles with dynamic uncertainties

The trajectory tracking control problem for underactuated unmanned surface vehicles(USV) was addressed, and the control system took account of the uncertain influences induced by model perturbation, external disturbance, etc. By introducing the reference, trajectory was generated by a virtual USV, and the error equation of trajectory tracking for USV was obtained, which transformed the tracking problem of underactuated USV into the stabilization problem of the trajectory tracking error equation. A backstepping adaptive sliding mode controller was proposed based on backstepping technology and method of dynamic slide model control. By means of theoretical analysis, it is proved that the proposed controller ensures that the solutions of closed loop system have the ultimate boundedness property. Simulation results are presented to illustrate the effectiveness of the proposed controller.

Radar-Based Collision Avoidance for Unmanned Surface Vehicles'

Unmanned surface vehicles(USVs) have become a focus of research because of their extensive applications. To ensure safety and reliability and to perform complex tasks autonomously, USVs are required to possess accurate perception of the environment and effective collision avoidance capabilities. To achieve these, investigation into realtime marine radar target detection and autonomous collision avoidance technologies is required, aiming at solving the problems of noise jamming, uneven brightness, target loss, and blind areas in marine radar images. These technologies should also satisfy the requirements of real-time and reliability related to high navigation speeds of USVs. Therefore, this study developed an embedded collision avoidance system based on the marine radar, investigated a highly real-time target detection method which contains adaptive smoothing algorithm and robust segmentation algorithm, developed a stable and reliable dynamic local environment model to ensure the safety of USV navigation, and constructed a collision avoidance algorithm based on velocity obstacle(V-obstacle) which adjusts the USV's heading and speed in real-time. Sea trials results in multi-obstacle avoidance firstly demonstrate the effectiveness and efficiency of the proposed avoidance system, and then verify its great adaptability and relative stability when a USV sailing in a real and complex marine environment. The obtained results will improve the intelligent level of USV and guarantee the safety of USV independent sailing.

Serret-Frenet frame based on path following control for underactuated unmanned surface vehicles with dynamic uncertainties

The path following problem for an underactuated unmanned surface vehicle(USV) in the Serret-Frenet frame is addressed. The control system takes account of the uncertain influence induced by model perturbation, external disturbance, etc. By introducing the Serret-Frenet frame and global coordinate transformation, the control problem of underactuated system(a nonlinear system with single-input and ternate-output) is transformed into the control problem of actuated system(a single-input and single-output nonlinear system), which simplifies the controller design. A backstepping adaptive sliding mode controller(BADSMC)is proposed based on backstepping design technique, adaptive method and theory of dynamic slide model control(DSMC). Then, it is proven that the state of closed loop system is globally stabilized to the desired configuration with the proposed controller. Simulation results are presented to illustrate the effectiveness of the proposed controller.

IAP's Solar-Powered Unmanned Surface Vehicle Actively Passes through the Center of typhoon Sinlaku(2020)

The solar-powered marine unmanned surface vehicle(USV) developed by the USV team of the Institute of Atmospheric Physics is a rugged, long-duration, and autonomous navigation vessel designed for the collection of longrange, continuous, real-time, meteorological and oceanographic measurements, especially under extreme sea conditions(sea state 6–7). These solar-powered USVs completed a long-term continuous navigation observation test over 26 days.During this time, they coordinated double-USV observations and actively navigated into the path of Typhoon Sinlaku(2020) before collecting data very close to its center during the 2020 USV South China Sea Typhoon Observation Experiment. Detailed high temporal resolution(1 min) real-time observations collected by the USV on the typhoon were used for operational typhoon forecasting and warning for the first time. As a mobile meteorological and oceanographic observation station capable of reliable, automated deployment, data collection, and transmission, such solar-powered USVs can replace traditional observation platforms to provide valuable real-time data for research, forecasting, and early warnings for potential marine meteorological disasters.

A novel ocean bathymetry technology based on an unmanned surface vehicle

In ocean bathymetry, the instantaneous depth measured by survey ships or by unmanned surface vehicles(USVs)cannot be directly taken as the chart depth because of the effect of waves and the tide. A novel ocean bathymetry technology is proposed based on the USV, the aim is to evaluate the potential of the USV using a real-time kinematic(RTK) and a single beam echo sounder for ocean bathymetry. First, using the RTK height of the USV with centimeter-level precision, the height of the sea level is obtained by excluding wave information using a low pass filter. Second, the datum distance between the reference ellipsoid and the chart depth is obtained by a novel method using tide tables and the height of the sea level from the USV. Previous work has usually achieved this using long-term tidal observation from traditional investigations. Finally, the chart depth is calculated using the transformation between the instantaneous depth of the USV measurement and the datum of the chart depth.Experiments were performed around the Wuzhizhou Island in Hainan Province using the unmanned surface bathymetry vehicle to validate the proposed technology. The successful results indicate the potential of the bathymetry technology based on the USV.

Modeling and Simulation of Automatic Berthing based on Bow and Stern Thruster Assist for Unmanned Surface Vehicle

In order to solve the technical problems of autonomous berthing of the Unmanned Surface Vehicle(USV),this research has met the requirements of maneuverability berthing under different conditions by effectively using the bow and stern thrusters,which is a technological breakthrough in actual production and life.Based on the MMG model,the maneuverability mathematical model of the USV with bow and stern thruster was established.And the motion simulation of USV maneuvering was carried out through the numerical simulation calculation.Then the berthing plan was designed based on the maneuverability analysis of the USV low-speed motion,and the simulation of automatic berthing for USV was carried out.The research results of this paper can be of certain practical significance for the USV based on the support of the bow and stern thruster in the berthing.At the same time,it also provides a certain theoretical reference for the handling of the USV automatic berthing.

Identification of an Underactuated Unmanned Surface Vehicle

Hydrodynamic coefficients strongly affect the dynamic performance of underactuated unmanned surface vehicle (USV) . Towing tank test is the traditional approach to identify these coefficients,however, the obtained values are not completely reliable since experimental difficulties and errors are involved. In this paper,an extended Kalman filter (EKF) method and a least squares (LS) method are proposed,only using onboard sensor data for identification of a small underactuated USV. The vehicle prototype as well as the system integration is delineated. Performance of the identification is evaluated by comparing the estimated coefficients,and the feasibility and accuracy of the proposed approach is demonstrated by simulation.

Energy-efficient joint UAV secure communication and 3D trajectory optimization assisted by reconfigurable intelligent surfaces in the presence of eavesdroppers

We consider a scenario where an unmanned aerial vehicle(UAV),a typical unmanned aerial system(UAS),transmits confidential data to a moving ground target in the presence of multiple eavesdroppers.Multiple friendly reconfigurable intelligent surfaces(RISs) help to secure the UAV-target communication and improve the energy efficiency of the UAV.We formulate an optimization problem to minimize the energy consumption of the UAV,subject to the mobility constraint of the UAV and that the achievable secrecy rate at the target is over a given threshold.We present an online planning method following the framework of model predictive control(MPC) to jointly optimize the motion of the UAV and the configurations of the RISs.The effectiveness of the proposed method is validated via computer simulations.

Secrecy Efficiency Maximization in Intelligent Reflective Surfaces Assisted UAV Communications

This paper focuses on the secrecy efficiency maximization in intelligent reflecting surface(IRS)assisted unmanned aerial vehicle(UAV)communication.With the popularization of UAV technology,more and more communication scenarios need UAV support.We consider using IRS to improve the secrecy efficiency.Specifically,IRS and UAV trajectories work together to counter potential eavesdroppers,while balancing the secrecy rate and energy consumption.The original problem is difficult to solve due to the coupling of optimization variables.We first introduce secrecy efficiency as an auxiliary variable and propose relaxation optimization problem,and then prove the equivalence between relaxation problem and the original problem.Then an iterative algorithm is proposed by applying the block coordinate descent(BCD)method and the inner approximationmethod.The simulation results show that the proposed algorithm converges fast and is superior to the existing schemes.In addition,in order to improve the robustness of the algorithm,we also pay attention to the case of obtaining imperfect channel state information(CSI).

Trajectory-tracking control of an unmanned surface vehicle based on characteristic modelling approach:Implementation and field testing

In this study,a practical adaptive control scheme is proposed for the trajectory tracking of an unmanned surface vehicle via the characteristic modelling approach.Therefore,accurate tracking control can be achieved in the presence of unknown time‐varying model parameters and environmental disturbances.The control scheme comprises a trajectory guidance module based on the virtual target approach and a tracking control module designed by characteristic modelling theory.Firstly,the ideal control commands of the yaw speed and surge speed are generated using the position errors between the vehicle and the virtual target.Then,a second‐order characteristic model for the heading and surge speed channel is developed.The parameters of the model are updated by a real‐time parameter identification algorithm.Based on this model,an integrated adaptive control law is designed which consists of golden‐section control,feed‐forward control and integral control.Finally,the development processes of the vehicle platform and the control algorithms are described,and the results of simulation and field experiments are presented and discussed.

基于MPC的USV自主航行仿真研究

水面无人艇的主要任务包括按照设定航线循迹航行以及在面临复杂环境下能够安全经济地避开海上静态障碍物和/或动态障碍物并最终顺利到达目的地。本文基于模型预测控制的设计框架,在船舶运动模型中充分考虑船舶的水动力特性以及风流等外界的干扰,以航迹偏移量、航速变化量、碰撞危险度和规则符合度构造评价函数,以无人艇执行机构的动力为约束条件,设计实现了一种水面无人艇自主航行模型。通过仿真实验,验证了所设计的控制算法可实现水面无人艇在期望航迹上的自主航行。

Attacking Strategy of Multiple Unmanned Surface Vehicles with Improved GWO Algorithm Under Control of Unmanned Aerial Vehicles

Unmanned combat system is one of the important means to capture information superiority,carry out precision strike and accomplish special combat tasks in information war.Unmanned attack strategy plays a crucial role in unmanned combat system,which has to ensure the attack by unmanned surface vehicles(USVs)from failure.To meet the challenge,we propose a task allocation algorithm called distributed auction mechanism task allocation with grey wolf optimization(DAGWO).The traditional grey wolf optimization(GWO)algorithm is improved with a distributed auction mechanism(DAM)to constrain the initialization of wolves,which improves the optimization process according to the actual situation.In addition,one unmanned aerial vehicle(UAV)is employed as the central control system to establish task allocation model and construct fitness function for the multiple constraints of USV attack problem.The proposed DAGWO algorithm can not only ensure the diversity of wolves,but also avoid the local optimum problem.Simulation results show that the proposed DAGWO algorithm can effectively solve the problem of attack task allocation among multiple USVs.