Multiple Uninhabited Aerial Vehicles (multi-UAVs) coordinated trajectory replanning is one of the most complicated global optimum problems in multi-UAVs coordinated control. Based on the construction of the basic mode...Multiple Uninhabited Aerial Vehicles (multi-UAVs) coordinated trajectory replanning is one of the most complicated global optimum problems in multi-UAVs coordinated control. Based on the construction of the basic model of multi-UAVs coordinated trajectory replanning, which includes problem description, threat modeling, constraint conditions, coordinated function and coordination mechanism, a novel Max-Min adaptive Ant Colony Optimization (ACO) approach is presented in detail. In view of the characteristics of multi-UAVs coordinated trajectory replanning in dynamic and uncertain environments, the minimum and maximum pheromone trails in ACO are set to enhance the searching capability, and the point pheromone is adopted to achieve the collision avoidance between UAVs at the trajectory planner layer. Considering the simultaneous arrival and the air-space collision avoidance, an Estimated Time of Arrival (ETA) is decided first. Then the trajectory and flight velocity of each UAV are determined. Simulation experiments are performed under the complicated combating environment containing some static threats and popup threats. The results demonstrate the feasibility and the effectiveness of the proposed approach.展开更多
Field D* algorithm is widely used in mobile robot navigation since it can plan and replan any-angle paths through non-uniform cost grids. However, it still suffers from inefficiency and sub-optimality. In this article...Field D* algorithm is widely used in mobile robot navigation since it can plan and replan any-angle paths through non-uniform cost grids. However, it still suffers from inefficiency and sub-optimality. In this article, a new linear interpolation-based planning and replanning algorithm, Update-Reducing Field D*, is proposed. It employs different approaches during initial planning and replanning respectively in order to reduce the number of updates of the rhs-values of vertices. Experiments have shown that Update-Reducing Field D* runs faster than Field D* and returns smoother and lower-cost paths.展开更多
With the rapid changes of the flight environment and situation,there will be various unexpected situations while multiple missiles are performing the missions.To fast cope with the various situations in mission execut...With the rapid changes of the flight environment and situation,there will be various unexpected situations while multiple missiles are performing the missions.To fast cope with the various situations in mission executions,the conventional sequential convex programming algorithm and the parallel-based sequential convex programming algorithm for multiple missiles fast trajectory replanning are proposed in this paper.The originally non-convex trajectory optimization problem is reformulated into a series of convex optimization subproblems based on the sequential convex programming method.The conventional sequential convex programming algorithm is developed through linearization,successive convexification,and relaxation techniques to solve the convex optimization subproblems iteratively.However,multiple missiles are related through various cooperative constraints.When the trajectory optimization of multiple missiles is formulated as an optimal control problem to solve,the complexity of the problem will increase dramatically as the number of missiles increases.To alleviate the coupled effect caused by multiple aerodynamically controlled missiles,the parallel-based sequential convex programming algorithm is proposed to solve the trajectory optimization problem for multiple missiles in parallel,reducing the complexity of the trajectory optimization problem and significantly shortening the computation time.Numerical simulations are provided to verify the convergence and effectiveness of the conventional sequential convex programming algorithm and the parallel-based sequential convex programming algorithm to cope with the trajectory optimization problem with various constraints.Furthermore,the optimality and the real-time performance of the proposed algorithms are discussed in comparative simulation examples.展开更多
Multi-robot mission planning is composed of assignment allocation and mobile-robot route planning in this paper.Multi-robot exploration missions adopts fuzzy c-mean(FCM)algorithm to allocate,and then,heterogeneous int...Multi-robot mission planning is composed of assignment allocation and mobile-robot route planning in this paper.Multi-robot exploration missions adopts fuzzy c-mean(FCM)algorithm to allocate,and then,heterogeneous interactive cultural hybrid algorithm(HICHA)is devised for route planning in order to optimize mobilerobot execution path.Meanwhile,we design multi-robot mission replanning mechanism based on the rules system of greedy algorithm for dynamic stochastic increment missions.Finally,extensive simulation experiments were shown that FCM for assignment allocation and HICHA for route planning were efficacious for mobile-robot exploration mission planning.Furthermore,the improved greedy algorithm based on experience rules met dynamic stochastic increment missions replanning requirement for load balance.展开更多
Recovery is a crucial supporting process for carrier aircraft,where a reasonable landing scheduling is expected to guide the fleet landing safely and quickly.Currently,there is little research on this topic,and most o...Recovery is a crucial supporting process for carrier aircraft,where a reasonable landing scheduling is expected to guide the fleet landing safely and quickly.Currently,there is little research on this topic,and most of it neglects potential influence factors,leaving the corresponding supporting efficiency questionable.In this paper,we study the landing scheduling problem for carrier aircraft considering the effects of bolting and aerial refueling.Based on the analysis of recovery mode involving the above factors,two types of primary constraints(i.e.,fuel constraint and wake interval constraint)are first described.Then,taking the landing sequencing as decision variables,a combinatorial optimization model with a compound objective function is formulated.Aiming at an efficient solution,an improved firefly algorithm is designed by integrating multiple evolutionary operators.In addition,a dynamic replanning mechanism is introduced to deal with special situations(i.e.,the occurrence of bolting and fuel shortage),where the high efficiency of the designed algorithm facilitates the online scheduling adjustment within seconds.Finally,numerical simulations with sufficient and insufficient fuel cases are both carried out,highlighting the necessity to consider bolting and aerial refueling during the planning procedure.Simulation results reveal that a higher bolting probability,as well as extra aerial refueling operations caused by fuel shortage,will lead to longer recovery complete time.Meanwhile,due to the strong optimum-seeking capability and solution efficiency of the improved algorithm,adaptive scheduling can be generated within milliseconds to deal with special situations,significantly improving the safety and efficiency of the recovery process.An animation is accessible at bilibili.com/video/BV1QprKY2EwD.展开更多
基金supported by the Natural Science Foundation of China (Grant no.60604009)Aeronautical Science Foundation of China (Grant no.2006ZC51039,Beijing NOVA Program Foundation of China (Grant no.2007A017)+1 种基金Open Fund of the Provincial Key Laboratory for Information Processing Technology,Suzhou University (Grant no KJS0821)"New Scientific Star in Blue Sky"Talent Program of Beihang University of China
文摘Multiple Uninhabited Aerial Vehicles (multi-UAVs) coordinated trajectory replanning is one of the most complicated global optimum problems in multi-UAVs coordinated control. Based on the construction of the basic model of multi-UAVs coordinated trajectory replanning, which includes problem description, threat modeling, constraint conditions, coordinated function and coordination mechanism, a novel Max-Min adaptive Ant Colony Optimization (ACO) approach is presented in detail. In view of the characteristics of multi-UAVs coordinated trajectory replanning in dynamic and uncertain environments, the minimum and maximum pheromone trails in ACO are set to enhance the searching capability, and the point pheromone is adopted to achieve the collision avoidance between UAVs at the trajectory planner layer. Considering the simultaneous arrival and the air-space collision avoidance, an Estimated Time of Arrival (ETA) is decided first. Then the trajectory and flight velocity of each UAV are determined. Simulation experiments are performed under the complicated combating environment containing some static threats and popup threats. The results demonstrate the feasibility and the effectiveness of the proposed approach.
文摘Field D* algorithm is widely used in mobile robot navigation since it can plan and replan any-angle paths through non-uniform cost grids. However, it still suffers from inefficiency and sub-optimality. In this article, a new linear interpolation-based planning and replanning algorithm, Update-Reducing Field D*, is proposed. It employs different approaches during initial planning and replanning respectively in order to reduce the number of updates of the rhs-values of vertices. Experiments have shown that Update-Reducing Field D* runs faster than Field D* and returns smoother and lower-cost paths.
基金supported by the National Natural Science Foundation of China(Grant No.12372044).
文摘With the rapid changes of the flight environment and situation,there will be various unexpected situations while multiple missiles are performing the missions.To fast cope with the various situations in mission executions,the conventional sequential convex programming algorithm and the parallel-based sequential convex programming algorithm for multiple missiles fast trajectory replanning are proposed in this paper.The originally non-convex trajectory optimization problem is reformulated into a series of convex optimization subproblems based on the sequential convex programming method.The conventional sequential convex programming algorithm is developed through linearization,successive convexification,and relaxation techniques to solve the convex optimization subproblems iteratively.However,multiple missiles are related through various cooperative constraints.When the trajectory optimization of multiple missiles is formulated as an optimal control problem to solve,the complexity of the problem will increase dramatically as the number of missiles increases.To alleviate the coupled effect caused by multiple aerodynamically controlled missiles,the parallel-based sequential convex programming algorithm is proposed to solve the trajectory optimization problem for multiple missiles in parallel,reducing the complexity of the trajectory optimization problem and significantly shortening the computation time.Numerical simulations are provided to verify the convergence and effectiveness of the conventional sequential convex programming algorithm and the parallel-based sequential convex programming algorithm to cope with the trajectory optimization problem with various constraints.Furthermore,the optimality and the real-time performance of the proposed algorithms are discussed in comparative simulation examples.
基金This work was supported in part by the National Natural Science Foundation of China(Grant No.90820302)the Research Fund for the Doctoral Program of Higher Education(No.200805330005)Hunan S&T Funds(No.06IJY3035).
文摘Multi-robot mission planning is composed of assignment allocation and mobile-robot route planning in this paper.Multi-robot exploration missions adopts fuzzy c-mean(FCM)algorithm to allocate,and then,heterogeneous interactive cultural hybrid algorithm(HICHA)is devised for route planning in order to optimize mobilerobot execution path.Meanwhile,we design multi-robot mission replanning mechanism based on the rules system of greedy algorithm for dynamic stochastic increment missions.Finally,extensive simulation experiments were shown that FCM for assignment allocation and HICHA for route planning were efficacious for mobile-robot exploration mission planning.Furthermore,the improved greedy algorithm based on experience rules met dynamic stochastic increment missions replanning requirement for load balance.
基金the financial support of the National Natural Science Foundation of China(12102077,12161076)the Natural Science and Technology Program of Liaoning Province(2023-BS-061).
文摘Recovery is a crucial supporting process for carrier aircraft,where a reasonable landing scheduling is expected to guide the fleet landing safely and quickly.Currently,there is little research on this topic,and most of it neglects potential influence factors,leaving the corresponding supporting efficiency questionable.In this paper,we study the landing scheduling problem for carrier aircraft considering the effects of bolting and aerial refueling.Based on the analysis of recovery mode involving the above factors,two types of primary constraints(i.e.,fuel constraint and wake interval constraint)are first described.Then,taking the landing sequencing as decision variables,a combinatorial optimization model with a compound objective function is formulated.Aiming at an efficient solution,an improved firefly algorithm is designed by integrating multiple evolutionary operators.In addition,a dynamic replanning mechanism is introduced to deal with special situations(i.e.,the occurrence of bolting and fuel shortage),where the high efficiency of the designed algorithm facilitates the online scheduling adjustment within seconds.Finally,numerical simulations with sufficient and insufficient fuel cases are both carried out,highlighting the necessity to consider bolting and aerial refueling during the planning procedure.Simulation results reveal that a higher bolting probability,as well as extra aerial refueling operations caused by fuel shortage,will lead to longer recovery complete time.Meanwhile,due to the strong optimum-seeking capability and solution efficiency of the improved algorithm,adaptive scheduling can be generated within milliseconds to deal with special situations,significantly improving the safety and efficiency of the recovery process.An animation is accessible at bilibili.com/video/BV1QprKY2EwD.
