Swarm robotics in maritime engineering is a promising approach characterized by large numbers of relatively small and inexpensive autonomous aquatic crafts (AACs) to monitor marine environments. Compared with a single...Swarm robotics in maritime engineering is a promising approach characterized by large numbers of relatively small and inexpensive autonomous aquatic crafts (AACs) to monitor marine environments. Compared with a single, large aquatic manned or unmanned surface vehicle, a highly distributed aquatic swarm system with several AACs features advantages in numerous real-world maritime missions, and its natural potential is qualified for new classes of tasks that uniformly feature low cost and high efficiency through time. This article develops an inexpensive AAC based on an embedded-systemcompanion computer and open-source autopilot, providing a verification platform for education and research on swarm algorithm on water surfaces. A topology communication network, including an inner communication network to exchange information among AACs and an external communication network for monitoring the state of the AAC Swarm System (AACSS), was designed based on the topology built into the Xbee units for the AACSS. In the emergence control network, the transmitter and receiver were coupled to distribute or recover the AAC. The swarm motion behaviors in AAC were resolved into the capabilities of go-to-waypoint and path following, which can be accomplished by two uncoupled controllers: speed controller and heading controller. The good performance of velocity and heading controllers in go-to-waypoint was proven in a series of simulations. Path following was achieved by tracking a set of ordered waypoints in the go-to-waypoint. Finally, a sea trial conducted at the China National Deep Sea Center successfully demonstrated the motion capability of the AAC. The sea trial results showed that the AAC is suited to carry out environmental monitoring tasks by efficiently covering the desired path, allowing for redundancy in the data collection process and tolerating the individual AACs’ path-following offset caused by winds and waves.展开更多
The high mobility of unmanned aerial vehicles(UAVs)could bring abundant degrees of freedom for the design of wireless communication systems,which results in that UAVs,especially UAV swarm,have attracted considerable a...The high mobility of unmanned aerial vehicles(UAVs)could bring abundant degrees of freedom for the design of wireless communication systems,which results in that UAVs,especially UAV swarm,have attracted considerable attention.This paper considers a UAV Swarm enabled relaying communication system,where multiple UAV relays are organized via coordinated multiple points(CoMP)as a UAV swarm to enhance physical layer security of the system in the presence of an eavesdropper.In order to maximize achievable secrecy rate of downlink,we jointly optimize the beamforming vector of the virtual array shaped by the UAV swarm and bandwidth allocation on it for receiving and forwarding,and both amplify-and-forward(AF)and decode-andforward(DF)protocols are considered on the UAV swarm.Due to the non-convexity of the joint optimization problem,we propose an alternating optimization(AO)algorithm to decompose it into two subproblems utilizing block coordinate descent technique,then each subproblem is solved by successive convex optimization method.Simulation results demonstrate that DF has competitive performance advantage compared with AF and the superiority of the proposed secure transmission strategy with optimal beamforming and bandwidth allocation compared with benchmark strategies.展开更多
This paper proposes a novel quantum-behaved particle swarm optimization （NQPSO） for the estimation of chaos＇ unknown parameters by transforming them into nonlinear functions＇ optimization. By means of the techniqu...This paper proposes a novel quantum-behaved particle swarm optimization （NQPSO） for the estimation of chaos＇ unknown parameters by transforming them into nonlinear functions＇ optimization. By means of the techniques in the following three aspects： contracting the searching space self-adaptively; boundaries restriction strategy; substituting the particles＇ convex combination for their centre of mass, this paper achieves a quite effective search mechanism with fine equilibrium between exploitation and exploration. Details of applying the proposed method and other methods into Lorenz systems are given, and experiments done show that NQPSO has better adaptability, dependability and robustness. It is a successful approach in unknown parameter estimation online especially in the cases with white noises.展开更多
Inspired by the fact that in most existing swarm models of multi-agent systems the velocity of an agent can be infinite, which is not in accordance with the real applications, we propose a novel swarm model of multi-a...Inspired by the fact that in most existing swarm models of multi-agent systems the velocity of an agent can be infinite, which is not in accordance with the real applications, we propose a novel swarm model of multi-agent systems where the velocity of an agent is finite. The Lyapunov function method and LaSalle＇s invariance principle are employed to show that by using the proposed model all of the agents eventually enter into a bounded region around the swarm center and finally tend to a stationary state. Numerical simulations are provided to demonstrate the effectiveness of the theoretical results.展开更多
This study proposes a method for uniformly revolving swarm robots to entrap multiple targets,which is based on a gene regulatory network,an adaptive decision mechanism,and an improved Vicsek-model.Using the gene regul...This study proposes a method for uniformly revolving swarm robots to entrap multiple targets,which is based on a gene regulatory network,an adaptive decision mechanism,and an improved Vicsek-model.Using the gene regulatory network method,the robots can generate entrapping patterns according to the environmental input,including the positions of the targets and obstacles.Next,an adaptive decision mechanism is proposed,allowing each robot to choose the most well-adapted capture point on the pattern,based on its environment.The robots employ an improved Vicsek-model to maneuver to the planned capture point smoothly,without colliding with other robots or obstacles.The proposed decision mechanism,combined with the improved Vicsek-model,can form a uniform entrapment shape and create a revolving effect around targets while entrapping them.This study also enables swarm robots,with an adaptive pattern formation,to entrap multiple targets in complex environments.Swarm robots can be deployed in the military field of unmanned aerial vehicles’(UAVs)entrapping multiple targets.Simulation experiments demonstrate the feasibility and superiority of the proposed gene regulatory network method.展开更多
The influence maximization problem aims to select a small set of influential nodes, termed a seed set, to maximize their influence coverage in social networks. Although the methods that are based on a greedy strategy ...The influence maximization problem aims to select a small set of influential nodes, termed a seed set, to maximize their influence coverage in social networks. Although the methods that are based on a greedy strategy can obtain good accuracy, they come at the cost of enormous computational time, and are therefore not applicable to practical scenarios in large-scale networks. In addition, the centrality heuristic algorithms that are based on network topology can be completed in relatively less time. However, they tend to fail to achieve satisfactory results because of drawbacks such as overlapped influence spread. In this work, we propose a discrete two-stage metaheuristic optimization combining quantum-behaved particle swarm optimization with Lévy flight to identify a set of the most influential spreaders. According to the framework,first, the particles in the population are tasked to conduct an exploration in the global solution space to eventually converge to an acceptable solution through the crossover and replacement operations. Second, the Lévy flight mechanism is used to perform a wandering walk on the optimal candidate solution in the population to exploit the potentially unidentified influential nodes in the network. Experiments on six real-world social networks show that the proposed algorithm achieves more satisfactory results when compared to other well-known algorithms.展开更多
Combining the heuristic algorithm （HA） developed based on the specific knowledge of the cooperative multiple target attack （CMTA） tactics and the particle swarm optimization （PSO）, a heuristic particle swarm opt...Combining the heuristic algorithm （HA） developed based on the specific knowledge of the cooperative multiple target attack （CMTA） tactics and the particle swarm optimization （PSO）, a heuristic particle swarm optimization （HPSO） algorithm is proposed to solve the decision-making （DM） problem. HA facilitates to search the local optimum in the neighborhood of a solution, while the PSO algorithm tends to explore the search space for possible solutions. Combining the advantages of HA and PSO, HPSO algorithms can find out the global optimum quickly and efficiently. It obtains the DM solution by seeking for the optimal assignment of missiles of friendly fighter aircrafts （FAs） to hostile FAs. Simulation results show that the proposed algorithm is superior to the general PSO algorithm and two GA based algorithms in searching for the best solution to the DM problem.展开更多
An improved particle swarm optimization （PSO） algorithm is investigated in the optimization of the attitude controller parameters of unmanned aerial vehicle （UAV）. Considering the stagnation phenomenon in the late...An improved particle swarm optimization （PSO） algorithm is investigated in the optimization of the attitude controller parameters of unmanned aerial vehicle （UAV）. Considering the stagnation phenomenon in the later phase of the basic PSO algorithm caused by the diversity scarcity of particles, a modified PSO algorithm is presented. For the basic PSO algorithm, the velocity of each particle is adjusted according to the inertia motion, the swarm previous best position and its own previous best position. However, in the improved PSO algorithm, each particle only learns from another randomly selected particle with higher performance, besides keeping the inertia motion. The inertia weight of the improved PSO algorithm is a random number. The modification decreases the uncertain parameters of the algorithm, simplifies the learning mechanism of the particle, and enhances the diversity of the swarm. Furthermore, a UAV attitude control system is built, and the improved PSO algorithm is applied in the optimized tuning of four controller parameters. Simulation results show that the improved PSO algorithm has stronger global searching ability than the common PSO algorithms, and obtains better UAV attitude control parameters.展开更多
文摘Swarm robotics in maritime engineering is a promising approach characterized by large numbers of relatively small and inexpensive autonomous aquatic crafts (AACs) to monitor marine environments. Compared with a single, large aquatic manned or unmanned surface vehicle, a highly distributed aquatic swarm system with several AACs features advantages in numerous real-world maritime missions, and its natural potential is qualified for new classes of tasks that uniformly feature low cost and high efficiency through time. This article develops an inexpensive AAC based on an embedded-systemcompanion computer and open-source autopilot, providing a verification platform for education and research on swarm algorithm on water surfaces. A topology communication network, including an inner communication network to exchange information among AACs and an external communication network for monitoring the state of the AAC Swarm System (AACSS), was designed based on the topology built into the Xbee units for the AACSS. In the emergence control network, the transmitter and receiver were coupled to distribute or recover the AAC. The swarm motion behaviors in AAC were resolved into the capabilities of go-to-waypoint and path following, which can be accomplished by two uncoupled controllers: speed controller and heading controller. The good performance of velocity and heading controllers in go-to-waypoint was proven in a series of simulations. Path following was achieved by tracking a set of ordered waypoints in the go-to-waypoint. Finally, a sea trial conducted at the China National Deep Sea Center successfully demonstrated the motion capability of the AAC. The sea trial results showed that the AAC is suited to carry out environmental monitoring tasks by efficiently covering the desired path, allowing for redundancy in the data collection process and tolerating the individual AACs’ path-following offset caused by winds and waves.
文摘The high mobility of unmanned aerial vehicles(UAVs)could bring abundant degrees of freedom for the design of wireless communication systems,which results in that UAVs,especially UAV swarm,have attracted considerable attention.This paper considers a UAV Swarm enabled relaying communication system,where multiple UAV relays are organized via coordinated multiple points(CoMP)as a UAV swarm to enhance physical layer security of the system in the presence of an eavesdropper.In order to maximize achievable secrecy rate of downlink,we jointly optimize the beamforming vector of the virtual array shaped by the UAV swarm and bandwidth allocation on it for receiving and forwarding,and both amplify-and-forward(AF)and decode-andforward(DF)protocols are considered on the UAV swarm.Due to the non-convexity of the joint optimization problem,we propose an alternating optimization(AO)algorithm to decompose it into two subproblems utilizing block coordinate descent technique,then each subproblem is solved by successive convex optimization method.Simulation results demonstrate that DF has competitive performance advantage compared with AF and the superiority of the proposed secure transmission strategy with optimal beamforming and bandwidth allocation compared with benchmark strategies.
基金Project supported by the National Natural Science Foundation of China (Grant No 10647141)
文摘This paper proposes a novel quantum-behaved particle swarm optimization （NQPSO） for the estimation of chaos＇ unknown parameters by transforming them into nonlinear functions＇ optimization. By means of the techniques in the following three aspects： contracting the searching space self-adaptively; boundaries restriction strategy; substituting the particles＇ convex combination for their centre of mass, this paper achieves a quite effective search mechanism with fine equilibrium between exploitation and exploration. Details of applying the proposed method and other methods into Lorenz systems are given, and experiments done show that NQPSO has better adaptability, dependability and robustness. It is a successful approach in unknown parameter estimation online especially in the cases with white noises.
基金supported by the National Natural Science Foundation of China(Grant Nos.61203147 and 61034006)
文摘Inspired by the fact that in most existing swarm models of multi-agent systems the velocity of an agent can be infinite, which is not in accordance with the real applications, we propose a novel swarm model of multi-agent systems where the velocity of an agent is finite. The Lyapunov function method and LaSalle＇s invariance principle are employed to show that by using the proposed model all of the agents eventually enter into a bounded region around the swarm center and finally tend to a stationary state. Numerical simulations are provided to demonstrate the effectiveness of the theoretical results.
基金funded by the National Natural Science Foundation of China(62176147)the Science and Technology Planning Project of Guangdong Province of China,the State Key Lab of Digital Manufacturing Equipment and Technology(DMETKF2019020)the National Defense Technology Innovation Special Zone Project(193-A14-226-01-01)。
文摘This study proposes a method for uniformly revolving swarm robots to entrap multiple targets,which is based on a gene regulatory network,an adaptive decision mechanism,and an improved Vicsek-model.Using the gene regulatory network method,the robots can generate entrapping patterns according to the environmental input,including the positions of the targets and obstacles.Next,an adaptive decision mechanism is proposed,allowing each robot to choose the most well-adapted capture point on the pattern,based on its environment.The robots employ an improved Vicsek-model to maneuver to the planned capture point smoothly,without colliding with other robots or obstacles.The proposed decision mechanism,combined with the improved Vicsek-model,can form a uniform entrapment shape and create a revolving effect around targets while entrapping them.This study also enables swarm robots,with an adaptive pattern formation,to entrap multiple targets in complex environments.Swarm robots can be deployed in the military field of unmanned aerial vehicles’(UAVs)entrapping multiple targets.Simulation experiments demonstrate the feasibility and superiority of the proposed gene regulatory network method.
基金Project supported by the Zhejiang Provincial Natural Science Foundation (Grant No.LQ20F020011)the Gansu Provincial Foundation for Distinguished Young Scholars (Grant No.23JRRA766)+1 种基金the National Natural Science Foundation of China (Grant No.62162040)the National Key Research and Development Program of China (Grant No.2020YFB1713600)。
文摘The influence maximization problem aims to select a small set of influential nodes, termed a seed set, to maximize their influence coverage in social networks. Although the methods that are based on a greedy strategy can obtain good accuracy, they come at the cost of enormous computational time, and are therefore not applicable to practical scenarios in large-scale networks. In addition, the centrality heuristic algorithms that are based on network topology can be completed in relatively less time. However, they tend to fail to achieve satisfactory results because of drawbacks such as overlapped influence spread. In this work, we propose a discrete two-stage metaheuristic optimization combining quantum-behaved particle swarm optimization with Lévy flight to identify a set of the most influential spreaders. According to the framework,first, the particles in the population are tasked to conduct an exploration in the global solution space to eventually converge to an acceptable solution through the crossover and replacement operations. Second, the Lévy flight mechanism is used to perform a wandering walk on the optimal candidate solution in the population to exploit the potentially unidentified influential nodes in the network. Experiments on six real-world social networks show that the proposed algorithm achieves more satisfactory results when compared to other well-known algorithms.
文摘Combining the heuristic algorithm （HA） developed based on the specific knowledge of the cooperative multiple target attack （CMTA） tactics and the particle swarm optimization （PSO）, a heuristic particle swarm optimization （HPSO） algorithm is proposed to solve the decision-making （DM） problem. HA facilitates to search the local optimum in the neighborhood of a solution, while the PSO algorithm tends to explore the search space for possible solutions. Combining the advantages of HA and PSO, HPSO algorithms can find out the global optimum quickly and efficiently. It obtains the DM solution by seeking for the optimal assignment of missiles of friendly fighter aircrafts （FAs） to hostile FAs. Simulation results show that the proposed algorithm is superior to the general PSO algorithm and two GA based algorithms in searching for the best solution to the DM problem.
基金Supported by the Graduate Student Research Innovation Program of Jiangsu Province(CX08B-091Z)the Innovation and Excellence Foundation of Doctoral Dissertation of Nanjing University of Aeronautics and Astronautics(BCXJ08-06)~~
文摘An improved particle swarm optimization （PSO） algorithm is investigated in the optimization of the attitude controller parameters of unmanned aerial vehicle （UAV）. Considering the stagnation phenomenon in the later phase of the basic PSO algorithm caused by the diversity scarcity of particles, a modified PSO algorithm is presented. For the basic PSO algorithm, the velocity of each particle is adjusted according to the inertia motion, the swarm previous best position and its own previous best position. However, in the improved PSO algorithm, each particle only learns from another randomly selected particle with higher performance, besides keeping the inertia motion. The inertia weight of the improved PSO algorithm is a random number. The modification decreases the uncertain parameters of the algorithm, simplifies the learning mechanism of the particle, and enhances the diversity of the swarm. Furthermore, a UAV attitude control system is built, and the improved PSO algorithm is applied in the optimized tuning of four controller parameters. Simulation results show that the improved PSO algorithm has stronger global searching ability than the common PSO algorithms, and obtains better UAV attitude control parameters.
