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Cooperative Task Assignment and Trajectory Planning of Unmanned Systems Via HFLC and PSO

UNMANNED SYSTEMS 2019年第2期7卷 65-81页

作者： Hafez, Ahmed T. Kamel, Mohamed A. Mil Tech Coll Dept Elect Engn Cairo 11766 Egypt Mil Tech Coll Dept Mech Engn Cairo 11766 Egypt

This paper investigates the problems of cooperative task assignment and trajectory planning for teams of cooperative unmanned aerial vehicles (UAVs). A novel approach of hierarchical fuzzy logic controller (HFLC) and particle swarm optimization (PSO) is proposed. Initially, teams of UAVs are moving in a pre-defined formation covering a specified area. When one or more targets are detected, the teams send a package of information to the ground station (GS) including the target's degree of threat, degree of importance, and the separating distance between each team and each detected target. Based on the gathered information, the ground station assigns the teams to the targets. HFLC is implemented in the GS to solve the assignment problem ensuring that each team is assigned to a unique target. Next, each team plans its own path by formulating the path planning problem as an optimization problem. The objective in this case is to minimize the time to reach their destination considering the UAVs dynamic constraints and collision avoidance between teams. A hybrid approach of control parametrization and time discretization (CPTD) and PSO is proposed to solve this optimization problem. Finally, numerical simulations demonstrate the effectiveness of the proposed algorithm.

关键词： Unmanned systems hierarchical fuzzy logic controller control parameterization and time discretization particle swarm optimization unmanned aerial vehicles

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Task Assignment/Trajectory Planning for Unmanned Vehicles Via HFLC and PSO

Task Assignment/Trajectory Planning for Unmanned Vehicles Vi...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Hafez, Ahmed Taimour Kamel, Mohamed A. Jardin, Peter T. Givigi, Sidney N. Mil Tech Coll Dept Elect Engn Cairo 11766 Egypt Mil Tech Coll Dept Mech Engn Cairo 11766 Egypt Royal Mil Coll Canada Dept Elect & Comp Engn Kingston ON Canada

ISBN: (纸本)9781509044948

This paper investigates the problems of task assignment and trajectory planning for teams of cooperative unmanned aerial vehicles (UAVs). A novel approach of hierarchical fuzzy logic controller (HFLC) and particle swarm optimization is proposed. Initially, teams of UAVs are moving in a pre-determined formation covering a specified area. When one or more targets are detected, the teams send a package of information to the ground station (GS) including the target's degree of threat, degree of importance, and the separating distance between each team and each detected target. First, the ground station assigns the teams to the targets based on the gathered information. HFLC is implemented in the GS to solve the assignment problem ensuring that each team is assigned to a unique target. Then, each team plans its own path by formulating the path planning problem as an optimization problem, while the objective is to minimize the time to reach their destination considering the UAVs dynamic constraints and the collision avoidance between teams. A hybrid approach of control parametrization and time discretization (CPTD) and PSO is proposed to solve the optimization problem. Finally, numerical simulations demonstrate the effectiveness of the proposed algorithm.

关键词： Unmanned aerial vehicles hierarchical fuzzy logic controller control parameterization and time discretization particle swarm optimization

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Fault-Tolerant Cooperative control of WMRs under Actuator Faults Based on Particle Swarm Optimization 3

Fault-Tolerant Cooperative Control of WMRs under Actuator Fa...

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3rd International Conference on control and Fault-Tolerant Systems (SysTol)

作者： Kamel, Mohamed A. Yu, Xiang Zhang, Youmin Concordia Univ Dept Mech & Ind Engn Montreal PQ H3G 1M8 Canada Hunan Univ Coll Mech & Vehicle Engn Changsha 410082 Hunan Peoples R China

ISBN: (纸本)9781509006588

This paper investigates fault-tolerant cooperative control (FTCC) of multiple wheeled mobile robots (WMRs) in the presence of severe actuator faults. Initially, a team of robots is moving in pre-defined formation configuration. When actuator faults occur in one or more robots, and the faulty robot(s) cannot complete the mission, the rest of robots start reconfiguring the formation to compensate the fault effect on the whole mission. First, the new formation reconfiguration is generated by solving an optimal assignment problem where each healthy robot should be assigned to a unique place. Then, the new formation can be reconfigured by recasting the reconfiguration problem as an optimization problem, while the objective is to minimize the time to achieve the new formation reconfiguration within the constraints of the robots' dynamics and collision avoidance. A hybrid approach of control parametrization and time discretization (CPTD) and particle swarm optimization (PSO) is proposed to solve the optimization problem. The results of the numerical simulations demonstrate the effectiveness of the proposed algorithm.

关键词： Fault-tolerant cooperative control optimal formation reconfiguration control parameterization and time discretization particle swarm optimization

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Optimal Formation Reconfiguration control of Multiple UCAVs Using Improved Particle Swarm Optimization

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Journal of Bionic Engineering 2008年第4期5卷 340-347页

作者： Hai-bin Duan Guan-jun Ma De-lin Luo School of Automation Science and Electrical Engineering Beihang University Beijing 100191 P. R. China Provincial Key Laboratory for Information Processing Technology Suzhou University Suzhou 215006 P. R. China Center for Systems and Control Xiamen University Xiamen 361005 P. R. China

Optimal formation reconfiguration control of multiple Uninhabited Combat Air Vehicles （UCAVs） is a complicated global optimum problem. Particle Swarm Optimization （PSO） is a population based stochastic optimization technique inspired by social behaviour of bird flocking or fish schooling. PSO can achieve better results in a faster, cheaper way compared with other bio-inspired computational methods, and there are few parameters to adjust in PSO. In this paper, we propose an improved PSO model for solving the optimal formation reconfiguration control problem for multiple UCAVs. Firstly, the control parameterization and time Diseretization （CPTD） method is designed in detail. Then, the mutation strategy and a special mutation-escape operator are adopted in the improved PSO model to make particles explore the search space more efficiently. The proposed strategy can produce a large speed value dynamically according to the variation of the speed, which makes the algorithm explore the local and global minima thoroughly at the same time. Series experimental results demonstrate the feasibility and effectiveness of the proposed method in solving the optimal formation reconfiguration control problem for multiple UCAVs.

关键词： uninhabited combat air vehicles particle swarm optimization control parameterization and time discretization optimal formation reeonfiguration

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