As the problem of surface garbage pollution becomes more serious,it is necessary to improve the efficiency of garbage inspection and picking rather than traditional manual methods.Due to lightness,Unmanned Aerial Vehi...As the problem of surface garbage pollution becomes more serious,it is necessary to improve the efficiency of garbage inspection and picking rather than traditional manual methods.Due to lightness,Unmanned Aerial Vehicles(UAVs)can traverse the entire water surface in a short time through their flight field of view.In addition,Unmanned Surface Vessels(USVs)can provide battery replacement and pick up garbage.In this paper,we innovatively establish a system framework for the collaboration between UAV and USVs,and develop an automatic water cleaning strategy.First,on the basis of the partition principle,we propose a collaborative coverage path algorithm based on UAV off-site takeoff and landing to achieve global inspection.Second,we design a task scheduling and assignment algorithm for USVs to balance the garbage loads based on the particle swarm optimization algorithm.Finally,based on the swarm intelligence algorithm,we also design an autonomous obstacle avoidance path planning algorithm for USVs to realize autonomous navigation and collaborative cleaning.The system can simultaneously perform inspection and clearance tasks under certain constraints.The simulation results show that the proposed algorithms have higher generality and flexibility while effectively improving computational efficiency and reducing actual cleaning costs compared with other schemes.展开更多
Modern defense systems are developing towards systematization.intellectualization and automation,which include the collaborative defense system on the sea between multiple unmanned surface vehicles(USVs)and unmanned a...Modern defense systems are developing towards systematization.intellectualization and automation,which include the collaborative defense system on the sea between multiple unmanned surface vehicles(USVs)and unmanned aerial vehicles(UAVs).UAVs can fly in high altitude and collect marine environment information on patrolling.Furthermore,UAVs can plan defense paths for USVs to intercept intruders with full-assignment or reassignment strategies aiming at maximum overall benefits.Thus,we propose dynamic overlay reconnaissance algor计hm based on genetic idea(GI-DORA)to solve the problem of multi-UAV multi-station reconnaissance.Moreover,we develop continuous particle swarm optimization based on obstaele dimension(OD-CPSO)to optimize defense path of USVs to intercept intruders.In addition,under the designed defense constraints,we propose dispersed particle swarm optimization based on mutation and crossover(MC-DPSO)and real-time batch assignment algorithm(RTBA)in emergency for formulating combat defense mission assignment strategy in different scenarios.Finally,we illus trate the feasibility and effectiveness of the proposed met hods.展开更多
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 c...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.展开更多
In this paper,the formation control problem is investigated for a team of uncertain underactuated surface vessels(USVs)based on a directed graph.Considering the risk of collision and the limited communication range of...In this paper,the formation control problem is investigated for a team of uncertain underactuated surface vessels(USVs)based on a directed graph.Considering the risk of collision and the limited communication range of USVs,the prescribed performance control(PPC)methodology is employed to ensure collision avoidance and connectivity maintenance.An event-triggered mechanism is designed to reasonably use the limited communication resources.Moreover,neural networks(NNs)and an auxiliary variable are constructed to deal with the problems of uncertain nonlinearities and underactuation,respectively.Then,an event-triggered formation control scheme is proposed to ensure that all signals of the closed-loop system are uniformly ultimately bounded(UUB).Finally,simulation results are presented to demonstrate the effectiveness of the proposed control scheme.展开更多
Maritime target recognition and image perception enhancement are gradually being promoted and applied in ocean engineering. This paper proposes the attentional multi-pixel fusion(AMF) algorithm for the intelligent nav...Maritime target recognition and image perception enhancement are gradually being promoted and applied in ocean engineering. This paper proposes the attentional multi-pixel fusion(AMF) algorithm for the intelligent navigation of unmanned surface vessels(USVs). The algorithm preprocesses the image pixel matrix in blocks, computes the mapping between regional and full-pixel matrices, and adaptively equalizes the mapping weights via a Gaussian-fuzzy matrix.This approach guarantees the preservation of the target contour and texture information. Compared with five classic enhancement algorithms, the AMF algorithm improves the peak signal-to-noise ratio(PSNR) and structural similarity index(SSIM). Experimental validation via YOLOv8 for maritime target detection demonstrates 2.1% and 2.4%improvements in the evaluation indices over training on 4000 original images, with shorter training times and lower confusion rates. In maritime target ranging, the AMF algorithm, coupled with the ISR method, exhibits the lowest improved stereo ranging mean absolute error and standard deviation values and higher similarity between the regional and full-pixel matrices. In summary, the AMF algorithm excels in target detection and ranging, offering promising applications in ocean engineering, such as marine resource exploitation, path planning, and intelligent collaboration among unmanned vessels.展开更多
Offboard active decoys(OADs)can effectively jam monopulse radars.However,for missiles approaching from a particular direction and distance,the OAD should be placed at a specific location,posing high requirements for t...Offboard active decoys(OADs)can effectively jam monopulse radars.However,for missiles approaching from a particular direction and distance,the OAD should be placed at a specific location,posing high requirements for timing and deployment.To improve the response speed and jamming effect,a cluster of OADs based on an unmanned surface vehicle(USV)is proposed.The formation of the cluster determines the effectiveness of jamming.First,based on the mechanism of OAD jamming,critical conditions are identified,and a method for assessing the jamming effect is proposed.Then,for the optimization of the cluster formation,a mathematical model is built,and a multi-tribe adaptive particle swarm optimization algorithm based on mutation strategy and Metropolis criterion(3M-APSO)is designed.Finally,the formation optimization problem is solved and analyzed using the 3M-APSO algorithm under specific scenarios.The results show that the improved algorithm has a faster convergence rate and superior performance as compared to the standard Adaptive-PSO algorithm.Compared with a single OAD,the optimal formation of USV-OAD cluster effectively fills the blind area and maximizes the use of jamming resources.展开更多
针对无人水面艇(unmanned surface vehicle,USV)自主航行过程中的避障与遵守海事交通规则之间潜在的冲突问题,设计基于生物启发神经网络并且遵守《1972年国际海上避碰规则》(Convention on the International Regulations for Preventin...针对无人水面艇(unmanned surface vehicle,USV)自主航行过程中的避障与遵守海事交通规则之间潜在的冲突问题,设计基于生物启发神经网络并且遵守《1972年国际海上避碰规则》(Convention on the International Regulations for Preventing Collisions At Sea,1972,COLREGs)的实时避障路径规划方法。运用STM32嵌入式平台搭建包括超声波、红外激光、陀螺仪和GPS传感器的小型USV水面环境感知硬件架构,将多传感器输出的动态环境信息通过栅格地图映射到二维神经网络中。USV根据神经网络活性势图自动规划通向目标点的无碰撞路径。通过多种船舶航行交汇局面的实验,证明该方法既安全又符合COLREGs的要求。展开更多
基金supported in part by the National Natural Science Foundation of China under Grants 62071189,62201220 and 62171189by the Key Research and Development Program of Hubei Province under Grant 2021BAA026 and 2020BAB120。
文摘As the problem of surface garbage pollution becomes more serious,it is necessary to improve the efficiency of garbage inspection and picking rather than traditional manual methods.Due to lightness,Unmanned Aerial Vehicles(UAVs)can traverse the entire water surface in a short time through their flight field of view.In addition,Unmanned Surface Vessels(USVs)can provide battery replacement and pick up garbage.In this paper,we innovatively establish a system framework for the collaboration between UAV and USVs,and develop an automatic water cleaning strategy.First,on the basis of the partition principle,we propose a collaborative coverage path algorithm based on UAV off-site takeoff and landing to achieve global inspection.Second,we design a task scheduling and assignment algorithm for USVs to balance the garbage loads based on the particle swarm optimization algorithm.Finally,based on the swarm intelligence algorithm,we also design an autonomous obstacle avoidance path planning algorithm for USVs to realize autonomous navigation and collaborative cleaning.The system can simultaneously perform inspection and clearance tasks under certain constraints.The simulation results show that the proposed algorithms have higher generality and flexibility while effectively improving computational efficiency and reducing actual cleaning costs compared with other schemes.
基金the National Natural Science Foundation of China(No.61625304)。
文摘Modern defense systems are developing towards systematization.intellectualization and automation,which include the collaborative defense system on the sea between multiple unmanned surface vehicles(USVs)and unmanned aerial vehicles(UAVs).UAVs can fly in high altitude and collect marine environment information on patrolling.Furthermore,UAVs can plan defense paths for USVs to intercept intruders with full-assignment or reassignment strategies aiming at maximum overall benefits.Thus,we propose dynamic overlay reconnaissance algor计hm based on genetic idea(GI-DORA)to solve the problem of multi-UAV multi-station reconnaissance.Moreover,we develop continuous particle swarm optimization based on obstaele dimension(OD-CPSO)to optimize defense path of USVs to intercept intruders.In addition,under the designed defense constraints,we propose dispersed particle swarm optimization based on mutation and crossover(MC-DPSO)and real-time batch assignment algorithm(RTBA)in emergency for formulating combat defense mission assignment strategy in different scenarios.Finally,we illus trate the feasibility and effectiveness of the proposed met hods.
文摘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.
基金partially supported by the National Natural Science Foundation of China under Grant Nos.62033003,62003098,61973091the Local Innovative and Research Teams Project of Guangdong Special Support Program under Grant No.2019BT02X353the China Postdoctoral Science Foundation under Grant Nos.2019M662813 and 2020T130124。
文摘In this paper,the formation control problem is investigated for a team of uncertain underactuated surface vessels(USVs)based on a directed graph.Considering the risk of collision and the limited communication range of USVs,the prescribed performance control(PPC)methodology is employed to ensure collision avoidance and connectivity maintenance.An event-triggered mechanism is designed to reasonably use the limited communication resources.Moreover,neural networks(NNs)and an auxiliary variable are constructed to deal with the problems of uncertain nonlinearities and underactuation,respectively.Then,an event-triggered formation control scheme is proposed to ensure that all signals of the closed-loop system are uniformly ultimately bounded(UUB).Finally,simulation results are presented to demonstrate the effectiveness of the proposed control scheme.
文摘由于海洋环境复杂多变,为了保障海员安全,全面提高海洋水域治理能力,近年来水面无人艇(unmanned surface vehicle,USV)的话题热度逐渐升高。USV路径规划能力是其关键技术之一,是其智能化的重要体现。随着船用雷达的逐步发展,现已成为保障船舶安全航行必不可少的一环。本文针对船用雷达图中USV的路径规划问题,将快速扩展随机树(Rapidly-exploring Random Trees,RRT)算法,对其进行改进并应用于USV路径规划问题,再与经典RRT算法进行比较。实验结果表明,改进后的RRT算法比经典RRT算法的路径长度更短,更平滑,证明改进后的RRT算法可以更有效的完成USV的路径规划。
基金financially supported by the Foundation of Shanxi Key Laboratory of Machine Vision and Virtual Reality (Grant No.447-110103)the Science and Technology Innovation Plan of Shanghai Science and Technology Commission (Grant No. 22dz1204000)。
文摘Maritime target recognition and image perception enhancement are gradually being promoted and applied in ocean engineering. This paper proposes the attentional multi-pixel fusion(AMF) algorithm for the intelligent navigation of unmanned surface vessels(USVs). The algorithm preprocesses the image pixel matrix in blocks, computes the mapping between regional and full-pixel matrices, and adaptively equalizes the mapping weights via a Gaussian-fuzzy matrix.This approach guarantees the preservation of the target contour and texture information. Compared with five classic enhancement algorithms, the AMF algorithm improves the peak signal-to-noise ratio(PSNR) and structural similarity index(SSIM). Experimental validation via YOLOv8 for maritime target detection demonstrates 2.1% and 2.4%improvements in the evaluation indices over training on 4000 original images, with shorter training times and lower confusion rates. In maritime target ranging, the AMF algorithm, coupled with the ISR method, exhibits the lowest improved stereo ranging mean absolute error and standard deviation values and higher similarity between the regional and full-pixel matrices. In summary, the AMF algorithm excels in target detection and ranging, offering promising applications in ocean engineering, such as marine resource exploitation, path planning, and intelligent collaboration among unmanned vessels.
基金the National Natural Science Foundation of China(Grant No.62101579).
文摘Offboard active decoys(OADs)can effectively jam monopulse radars.However,for missiles approaching from a particular direction and distance,the OAD should be placed at a specific location,posing high requirements for timing and deployment.To improve the response speed and jamming effect,a cluster of OADs based on an unmanned surface vehicle(USV)is proposed.The formation of the cluster determines the effectiveness of jamming.First,based on the mechanism of OAD jamming,critical conditions are identified,and a method for assessing the jamming effect is proposed.Then,for the optimization of the cluster formation,a mathematical model is built,and a multi-tribe adaptive particle swarm optimization algorithm based on mutation strategy and Metropolis criterion(3M-APSO)is designed.Finally,the formation optimization problem is solved and analyzed using the 3M-APSO algorithm under specific scenarios.The results show that the improved algorithm has a faster convergence rate and superior performance as compared to the standard Adaptive-PSO algorithm.Compared with a single OAD,the optimal formation of USV-OAD cluster effectively fills the blind area and maximizes the use of jamming resources.
文摘针对无人水面艇(unmanned surface vehicle,USV)自主航行过程中的避障与遵守海事交通规则之间潜在的冲突问题,设计基于生物启发神经网络并且遵守《1972年国际海上避碰规则》(Convention on the International Regulations for Preventing Collisions At Sea,1972,COLREGs)的实时避障路径规划方法。运用STM32嵌入式平台搭建包括超声波、红外激光、陀螺仪和GPS传感器的小型USV水面环境感知硬件架构,将多传感器输出的动态环境信息通过栅格地图映射到二维神经网络中。USV根据神经网络活性势图自动规划通向目标点的无碰撞路径。通过多种船舶航行交汇局面的实验,证明该方法既安全又符合COLREGs的要求。
