A safe and reliable path planning algorithm is fundamental for unmanned surface vehicles(USVs)to perform autonomous navigation tasks.However,a single global or local planning strategy cannot fully meet the requirement...A safe and reliable path planning algorithm is fundamental for unmanned surface vehicles(USVs)to perform autonomous navigation tasks.However,a single global or local planning strategy cannot fully meet the requirements of complex maritime environments.Global planning alone cannot effectively handle dynamic obstacles,while local planning alone may fall into local optima.To address these issues,this paper proposes a multi-dynamic-obstacle avoidance path planning method that integrates an improved A^(*)algorithm with the dynamic window approach(DWA).The traditional A^(*)algorithm often generates paths that are too close to obstacle boundaries and contain excessive turning points,whereas the traditional DWA tends to skirt densely clustered obstacles,resulting in longer routes and insufficient dynamic obstacle avoidance.To overcome these limitations,improved versions of both algorithms are developed.Key points extracted from the optimized A^(*)path are used as intermediate start-destination pairs for the improved DWA,and the weights of the DWA evaluation function are adjusted to achieve effective fusion.Furthermore,a multi-dynamic-obstacle avoidance strategy is designed for complex navigation scenarios.Simulation results demonstrate that the USV can adaptively switch between dynamic obstacle avoidance and path tracking based on obstacle distribution,validating the effectiveness of the proposed method.展开更多
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
Dear Editor,This letter addresses the formation control problem for unmanned surface vehicles(USVs)under GPS-denied environments.A novel visual servo formation control scheme,utilizing a monocular camera on the follow...Dear Editor,This letter addresses the formation control problem for unmanned surface vehicles(USVs)under GPS-denied environments.A novel visual servo formation control scheme,utilizing a monocular camera on the follower to obtain the leader’s global position,is developed,which is also capable of guaranteeing collision avoidance and visibility maintenance(CA&VM)raised by the requirement of actual formation navigation.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
To enhance the accuracy of path planning of unmanned surface vehicles(USVs),the particle swarm optimization algorithm(PSO)is improved based on species migration strategies observed in ecology.By incorporating the conc...To enhance the accuracy of path planning of unmanned surface vehicles(USVs),the particle swarm optimization algorithm(PSO)is improved based on species migration strategies observed in ecology.By incorporating the concept of particle sight distance,an improved algorithm,called SD-IPSO,is proposed for the real-time autonomous navigation of USVs in marine environments.The algorithm refines the individual behavior pattern of particles in the population,effectively improving both local and global search capabilities while avoiding premature convergence.The effectiveness of the algorithm is validated using standard test functions from CEC-2017 function library,assessing it from multiple dimensions.Sensitivity analysis is conducted on key parameters in the algorithm,including particle sight distance and population size.Results indicate that compared with PSO,SD-IPSO demonstrates significant advantages in optimization accuracy and convergence speed.The application of SD-IPSO in path planning is further investigated through a 14-point traveling salesman problem(TSP)example and navigation autonomous tests of USVs in marine environments.Findings demonstrate that the proposed algorithm exhibits superior optimization capabilities and can effectively address the path planning challenges of USVs.展开更多
During the use of robotics in applications such as antiterrorism or combat,a motion-constrained pursuer vehicle,such as a Dubins unmanned surface vehicle(USV),must get close enough(within a prescribed zero or positive...During the use of robotics in applications such as antiterrorism or combat,a motion-constrained pursuer vehicle,such as a Dubins unmanned surface vehicle(USV),must get close enough(within a prescribed zero or positive distance)to a moving target as quickly as possible,resulting in the extended minimum-time intercept problem(EMTIP).Existing research has primarily focused on the zero-distance intercept problem,MTIP,establishing the necessary or sufficient conditions for MTIP optimality,and utilizing analytic algorithms,such as root-finding algorithms,to calculate the optimal solutions.However,these approaches depend heavily on the properties of the analytic algorithm,making them inapplicable when problem settings change,such as in the case of a positive effective range or complicated target motions outside uniform rectilinear motion.In this study,an approach employing a high-accuracy and quality-guaranteed mixed-integer piecewise-linear program(QG-PWL)is proposed for the EMTIP.This program can accommodate different effective interception ranges and complicated target motions(variable velocity or complicated trajectories).The high accuracy and quality guarantees of QG-PWL originate from elegant strategies such as piecewise linearization and other developed operation strategies.The approximate error in the intercept path length is proved to be bounded to h^(2)/(4√2),where h is the piecewise length.展开更多
Dear Editor,This letter investigates the problem of multi-dimension formation tracking(MDFT)for the cross-domain unmanned systems,including several interconnected agents,namely,unmanned aerial vehicles(UAVs)and unmann...Dear Editor,This letter investigates the problem of multi-dimension formation tracking(MDFT)for the cross-domain unmanned systems,including several interconnected agents,namely,unmanned aerial vehicles(UAVs)and unmanned surface vehicles(USVs).We assume that each agent suffers from by the mixed constraints on its velocity,control input and Euler angle.Solving the MDFT problem implies that 1)The virtual state of each USV is determined in the earth coordinate by expanding its 2D work space to the 3D space.展开更多
面对多障碍、大尺寸障碍、狭窄通道等特殊环境下的USV路径规划问题,快速扩展随机树算法(rapidly-exploring random trees,RRT)存在采样基数大、规划成功率低、规划路径曲折等缺点。基于双延迟深度确定性策略梯度(twin delayed deep dete...面对多障碍、大尺寸障碍、狭窄通道等特殊环境下的USV路径规划问题,快速扩展随机树算法(rapidly-exploring random trees,RRT)存在采样基数大、规划成功率低、规划路径曲折等缺点。基于双延迟深度确定性策略梯度(twin delayed deep deterministic policy gradient,TD3)提出一种全局路径规划算法(TD3-RRT)。结合RRT算法与深度强化学习建立USV路径搜索模型,利用前视探测感知环境以自适应调整扩展步长,通过策略网络输出路径搜索方向,解决RRT算法扩展盲目的问题;改进后见经验回放策略,通过重选虚拟目标、双经验回放池采样等策略以增强复杂环境下路径搜索能力;通过奖励函数提高规划路径质量,加快路径搜索速度。实验结果表明:不同环境下TD3-RRT相比当前主流算法能够有效提高规划成功率,优化转向角度、路径长度和规划时间,证明了改进算法能有效加快路径搜索速度并提高路径质量,且对不同环境具有良好适应性。展开更多
The integration of Unmanned Aerial Vehicles(UAVs)and Uncrewed Surface Vehicles(USVs)has revolutionized topographic and bathymetricmapping,significantly enhancing the accuracy and efficiency of geospatial data acquisit...The integration of Unmanned Aerial Vehicles(UAVs)and Uncrewed Surface Vehicles(USVs)has revolutionized topographic and bathymetricmapping,significantly enhancing the accuracy and efficiency of geospatial data acquisition processes.This innovative approach synergistically combines terrestrial data collected by UAVs with underwater data obtained through USVs,culminating in the creation of unified high-resolution Digital Elevation Models(DEMs)of the river basin region represents a vital step toward understanding the dynamic interactions between land and water bodies.Hence,the seamless Topo-Bathymetric Elevation Model offers a detailed perspective of the river system,supporting informed decision-making in addressing sediment transport,erosion,and river morphology.This manuscript provides a comprehensive review examines the advanced methodologies for creating seamlessmultisource Topo-Bathymetry ElevationModels(TBEMs)in river basin contexts,emphasising critical factors such as cost-effectiveness,operational efficiency,and data precision.In particular,UAVs deliver high-resolution(1-3 cm)topographic mapping with 5-10 km operational ranges,while USVs provide complementary bathymetric data(1 m resolution)across 3-5 km.This synergy enables seamless land-water surveys,achieving superior precision(±8 cmterrestrial,±3 cmunderwater)and efficiency over traditional methods.By analysing the benefits and limitations inherent in these technologies,this review elucidates the potential of UAV-USV synergy to improve the accuracy and reliability of geospatial data,thereby supporting well-versed decision-making processes in environmental management and conservation efforts.Furthermore,the findings underscore the broader implications of this integrated approach for riverine and coastal studies,advocating for its wider adoption in various applications,including habitat monitoring,flood risk assessment,and sustainable resource management.The synthesis of terrestrial and aquatic data through UAV-USV collaboration not only advances the field of geospatial science but also fosters a deeper understanding of the interdependencies between land and water systems,ultimately contributing to more effective environmental stewardship.展开更多
基金supported by the National Nature Science Foundation of China(62203299,62373246,62388101)the Research Fund of State Key Laboratory of Deep-Sea Manned Vehicles(2024SKLDMV04)+1 种基金the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University(SL2023MS007)the Startup Fund for Young Faculty at SJTU(24X010502929)。
文摘A safe and reliable path planning algorithm is fundamental for unmanned surface vehicles(USVs)to perform autonomous navigation tasks.However,a single global or local planning strategy cannot fully meet the requirements of complex maritime environments.Global planning alone cannot effectively handle dynamic obstacles,while local planning alone may fall into local optima.To address these issues,this paper proposes a multi-dynamic-obstacle avoidance path planning method that integrates an improved A^(*)algorithm with the dynamic window approach(DWA).The traditional A^(*)algorithm often generates paths that are too close to obstacle boundaries and contain excessive turning points,whereas the traditional DWA tends to skirt densely clustered obstacles,resulting in longer routes and insufficient dynamic obstacle avoidance.To overcome these limitations,improved versions of both algorithms are developed.Key points extracted from the optimized A^(*)path are used as intermediate start-destination pairs for the improved DWA,and the weights of the DWA evaluation function are adjusted to achieve effective fusion.Furthermore,a multi-dynamic-obstacle avoidance strategy is designed for complex navigation scenarios.Simulation results demonstrate that the USV can adaptively switch between dynamic obstacle avoidance and path tracking based on obstacle distribution,validating the effectiveness of the proposed method.
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supported by the National Natural Science Foundation of China(62421004,U24A20279,62473243,62533004)。
文摘Dear Editor,This letter addresses the formation control problem for unmanned surface vehicles(USVs)under GPS-denied environments.A novel visual servo formation control scheme,utilizing a monocular camera on the follower to obtain the leader’s global position,is developed,which is also capable of guaranteeing collision avoidance and visibility maintenance(CA&VM)raised by the requirement of actual formation navigation.
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To enhance the accuracy of path planning of unmanned surface vehicles(USVs),the particle swarm optimization algorithm(PSO)is improved based on species migration strategies observed in ecology.By incorporating the concept of particle sight distance,an improved algorithm,called SD-IPSO,is proposed for the real-time autonomous navigation of USVs in marine environments.The algorithm refines the individual behavior pattern of particles in the population,effectively improving both local and global search capabilities while avoiding premature convergence.The effectiveness of the algorithm is validated using standard test functions from CEC-2017 function library,assessing it from multiple dimensions.Sensitivity analysis is conducted on key parameters in the algorithm,including particle sight distance and population size.Results indicate that compared with PSO,SD-IPSO demonstrates significant advantages in optimization accuracy and convergence speed.The application of SD-IPSO in path planning is further investigated through a 14-point traveling salesman problem(TSP)example and navigation autonomous tests of USVs in marine environments.Findings demonstrate that the proposed algorithm exhibits superior optimization capabilities and can effectively address the path planning challenges of USVs.
基金supported by the National Natural Sci‐ence Foundation of China(Grant No.62306325)。
文摘During the use of robotics in applications such as antiterrorism or combat,a motion-constrained pursuer vehicle,such as a Dubins unmanned surface vehicle(USV),must get close enough(within a prescribed zero or positive distance)to a moving target as quickly as possible,resulting in the extended minimum-time intercept problem(EMTIP).Existing research has primarily focused on the zero-distance intercept problem,MTIP,establishing the necessary or sufficient conditions for MTIP optimality,and utilizing analytic algorithms,such as root-finding algorithms,to calculate the optimal solutions.However,these approaches depend heavily on the properties of the analytic algorithm,making them inapplicable when problem settings change,such as in the case of a positive effective range or complicated target motions outside uniform rectilinear motion.In this study,an approach employing a high-accuracy and quality-guaranteed mixed-integer piecewise-linear program(QG-PWL)is proposed for the EMTIP.This program can accommodate different effective interception ranges and complicated target motions(variable velocity or complicated trajectories).The high accuracy and quality guarantees of QG-PWL originate from elegant strategies such as piecewise linearization and other developed operation strategies.The approximate error in the intercept path length is proved to be bounded to h^(2)/(4√2),where h is the piecewise length.
基金supported in part by the National Natural Science Foundation of China(62073301,62373162,62473349,U24A20268,62233007)the Shenzhen Science and Technology Program(JCYJ20240813114007010).
文摘Dear Editor,This letter investigates the problem of multi-dimension formation tracking(MDFT)for the cross-domain unmanned systems,including several interconnected agents,namely,unmanned aerial vehicles(UAVs)and unmanned surface vehicles(USVs).We assume that each agent suffers from by the mixed constraints on its velocity,control input and Euler angle.Solving the MDFT problem implies that 1)The virtual state of each USV is determined in the earth coordinate by expanding its 2D work space to the 3D space.
文摘面对多障碍、大尺寸障碍、狭窄通道等特殊环境下的USV路径规划问题,快速扩展随机树算法(rapidly-exploring random trees,RRT)存在采样基数大、规划成功率低、规划路径曲折等缺点。基于双延迟深度确定性策略梯度(twin delayed deep deterministic policy gradient,TD3)提出一种全局路径规划算法(TD3-RRT)。结合RRT算法与深度强化学习建立USV路径搜索模型,利用前视探测感知环境以自适应调整扩展步长,通过策略网络输出路径搜索方向,解决RRT算法扩展盲目的问题;改进后见经验回放策略,通过重选虚拟目标、双经验回放池采样等策略以增强复杂环境下路径搜索能力;通过奖励函数提高规划路径质量,加快路径搜索速度。实验结果表明:不同环境下TD3-RRT相比当前主流算法能够有效提高规划成功率,优化转向角度、路径长度和规划时间,证明了改进算法能有效加快路径搜索速度并提高路径质量,且对不同环境具有良好适应性。
基金financed by Universiti Teknologi Malaysia Encouragement Research Grant(Vot Q.J130000.3852.42J12)to provide incentives and financial support for UTM academic staff to lead research projects that contribute to the university’s research Key Performance Indicators(KPIs)and foster the development of high-quality,competitive research proposals.
文摘The integration of Unmanned Aerial Vehicles(UAVs)and Uncrewed Surface Vehicles(USVs)has revolutionized topographic and bathymetricmapping,significantly enhancing the accuracy and efficiency of geospatial data acquisition processes.This innovative approach synergistically combines terrestrial data collected by UAVs with underwater data obtained through USVs,culminating in the creation of unified high-resolution Digital Elevation Models(DEMs)of the river basin region represents a vital step toward understanding the dynamic interactions between land and water bodies.Hence,the seamless Topo-Bathymetric Elevation Model offers a detailed perspective of the river system,supporting informed decision-making in addressing sediment transport,erosion,and river morphology.This manuscript provides a comprehensive review examines the advanced methodologies for creating seamlessmultisource Topo-Bathymetry ElevationModels(TBEMs)in river basin contexts,emphasising critical factors such as cost-effectiveness,operational efficiency,and data precision.In particular,UAVs deliver high-resolution(1-3 cm)topographic mapping with 5-10 km operational ranges,while USVs provide complementary bathymetric data(1 m resolution)across 3-5 km.This synergy enables seamless land-water surveys,achieving superior precision(±8 cmterrestrial,±3 cmunderwater)and efficiency over traditional methods.By analysing the benefits and limitations inherent in these technologies,this review elucidates the potential of UAV-USV synergy to improve the accuracy and reliability of geospatial data,thereby supporting well-versed decision-making processes in environmental management and conservation efforts.Furthermore,the findings underscore the broader implications of this integrated approach for riverine and coastal studies,advocating for its wider adoption in various applications,including habitat monitoring,flood risk assessment,and sustainable resource management.The synthesis of terrestrial and aquatic data through UAV-USV collaboration not only advances the field of geospatial science but also fosters a deeper understanding of the interdependencies between land and water systems,ultimately contributing to more effective environmental stewardship.
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。
