The application of multiple UAVs in complicated tasks has been widely explored in recent years.Due to the advantages of flexibility,cheapness and consistence,the performance of heterogeneous multi-UAVs with proper coo...The application of multiple UAVs in complicated tasks has been widely explored in recent years.Due to the advantages of flexibility,cheapness and consistence,the performance of heterogeneous multi-UAVs with proper cooperative task allocation is superior to over the single UAV.Accordingly,several constraints should be satisfied to realize the efficient cooperation,such as special time-window,variant equipment,specified execution sequence.Hence,a proper task allocation in UAVs is the crucial point for the final success.The task allocation problem of the heterogeneous UAVs can be formulated as a multi-objective optimization problem coupled with the UAV dynamics.To this end,a multi-layer encoding strategy and a constraint scheduling method are designed to handle the critical logical and physical constraints.In addition,four optimization objectives:completion time,target reward,UAV damage,and total range,are introduced to evaluate various allocation plans.Subsequently,to efficiently solve the multi-objective optimization problem,an improved multi-objective quantum-behaved particle swarm optimization(IMOQPSO)algorithm is proposed.During this algorithm,a modified solution evaluation method is designed to guide algorithmic evolution;both the convergence and distribution of particles are considered comprehensively;and boundary solutions which may produce some special allocation plans are preserved.Moreover,adaptive parameter control and mixed update mechanism are also introduced in this algorithm.Finally,both the proposed model and algorithm are verified by simulation experiments.展开更多
Dynamic soaring is a flight maneuver to exploit gradient wind field to extend endurance and traveling distance.Optimal trajectories for permissible wind conditions are generated for loitering dynamic soaring as well a...Dynamic soaring is a flight maneuver to exploit gradient wind field to extend endurance and traveling distance.Optimal trajectories for permissible wind conditions are generated for loitering dynamic soaring as well as for traveling patterns with a small unmanned aerial vehicle.The efficient direct collection approach based on the Runge-Kutta integrator is used to solve the optimization problem.The fast convergence of the optimization process leads to the potential for real-time applications.Based on the results of trajectory optimizations,the general permissible wind conditions which involve the allowable power law exponents and feasible reference wind strengths supporting dynamic soaring are proposed.Increasing the smallest allowable wingtip clearance to trade for robustness and safety of the vehicle system and improving the maximum traveling speed results in shrunken permissible domain of wind conditions for loitering and traveling dynamic soaring respectively.Sensitivity analyses of vehicle model parameters show that properly reducing the wingspan and increasing the maximum lift-to-drag ratio and the wing loading can enlarge the permissible domain.Permissible domains for different traveling directions show that the downwind dynamic soaring benefitting from the drift is more efficient than the upwind traveling pattern in terms of permissible domain size and net traveling speed.展开更多
基金Project(61801495)supported by the National Natural Science Foundation of China
文摘The application of multiple UAVs in complicated tasks has been widely explored in recent years.Due to the advantages of flexibility,cheapness and consistence,the performance of heterogeneous multi-UAVs with proper cooperative task allocation is superior to over the single UAV.Accordingly,several constraints should be satisfied to realize the efficient cooperation,such as special time-window,variant equipment,specified execution sequence.Hence,a proper task allocation in UAVs is the crucial point for the final success.The task allocation problem of the heterogeneous UAVs can be formulated as a multi-objective optimization problem coupled with the UAV dynamics.To this end,a multi-layer encoding strategy and a constraint scheduling method are designed to handle the critical logical and physical constraints.In addition,four optimization objectives:completion time,target reward,UAV damage,and total range,are introduced to evaluate various allocation plans.Subsequently,to efficiently solve the multi-objective optimization problem,an improved multi-objective quantum-behaved particle swarm optimization(IMOQPSO)algorithm is proposed.During this algorithm,a modified solution evaluation method is designed to guide algorithmic evolution;both the convergence and distribution of particles are considered comprehensively;and boundary solutions which may produce some special allocation plans are preserved.Moreover,adaptive parameter control and mixed update mechanism are also introduced in this algorithm.Finally,both the proposed model and algorithm are verified by simulation experiments.
文摘Dynamic soaring is a flight maneuver to exploit gradient wind field to extend endurance and traveling distance.Optimal trajectories for permissible wind conditions are generated for loitering dynamic soaring as well as for traveling patterns with a small unmanned aerial vehicle.The efficient direct collection approach based on the Runge-Kutta integrator is used to solve the optimization problem.The fast convergence of the optimization process leads to the potential for real-time applications.Based on the results of trajectory optimizations,the general permissible wind conditions which involve the allowable power law exponents and feasible reference wind strengths supporting dynamic soaring are proposed.Increasing the smallest allowable wingtip clearance to trade for robustness and safety of the vehicle system and improving the maximum traveling speed results in shrunken permissible domain of wind conditions for loitering and traveling dynamic soaring respectively.Sensitivity analyses of vehicle model parameters show that properly reducing the wingspan and increasing the maximum lift-to-drag ratio and the wing loading can enlarge the permissible domain.Permissible domains for different traveling directions show that the downwind dynamic soaring benefitting from the drift is more efficient than the upwind traveling pattern in terms of permissible domain size and net traveling speed.
