The pedestrians can only avoid collisions passively under the action of forces during simulations using the social force model, which may lead to unnatural behaviors. This paper proposes an optimization-based model fo...The pedestrians can only avoid collisions passively under the action of forces during simulations using the social force model, which may lead to unnatural behaviors. This paper proposes an optimization-based model for the avoidance of collisions, where the social repulsive force is removed in favor of a search for the quickest path to destination in the pedestrian＇s vision field. In this way, the behaviors of pedestrians are governed by changing their desired walking direction and desired speed. By combining the critical factors of pedestrian movement, such as positions of the exit and obstacles and velocities of the neighbors, the choice of desired velocity has been rendered to a discrete optimization problem. Therefore,it is the self-driven force that leads pedestrians to a free path rather than the repulsive force, which means the pedestrians can actively avoid collisions. The new model is verified by comparing with the fundamental diagram and actual data. The simulation results of individual avoidance trajectories and crowd avoidance behaviors demonstrate the reasonability of the proposed model.展开更多
文中针对多船会遇避碰决策中过渡依赖单一寻优决策的问题采用了加入自适应权重的樽海鞘群优化算法(weight salp swarm algorithm, WSSA),在算法中融入国际海上避碰规则(convention on the international regulations for presenting col...文中针对多船会遇避碰决策中过渡依赖单一寻优决策的问题采用了加入自适应权重的樽海鞘群优化算法(weight salp swarm algorithm, WSSA),在算法中融入国际海上避碰规则(convention on the international regulations for presenting collisions at sea, COLREGs)和良好船艺的要求.使用速度障碍法判断船舶的碰撞危险度并将多船会遇避让的过程中避让的安全性、经济性以及船舶领域侵入程度作为建立避碰决策的目标函数.算法测试的结果中,WSSA与原始樽海鞘群算法(SSA)以及经典粒子群算法(partide swam optimization, PSO)相比较,WSSA算法在收敛的精度和速度方面都明显优于SSA和PSO算法.结果表明:WSSA在寻找最优碰撞路线的过程中迭代的次数更少,精度更高.展开更多
The introduction ofproportional-integral-dorivative （PID） controllers into cooperative collision avoidance systems （CCASs） has been hindered by difficulties in their optimization and by a lack of study of their ef...The introduction ofproportional-integral-dorivative （PID） controllers into cooperative collision avoidance systems （CCASs） has been hindered by difficulties in their optimization and by a lack of study of their effects on vehicle driving stability, comfort, and fuel economy. In this paper, we propose a method to optimize PID controllers using an improved particle swarm optimization （PSO） algorithm, and to bettor manipulate cooperative collision avoidance with other vehicles. First, we use PRESCAN and MATLAB/Simulink to conduct a united simulation, which constructs a CCAS composed of a PID controller, maneuver strategy judging modules, and a path planning module. Then we apply the improved PSO algorithm to optimize the PID controller based on the dynamic vehicle data obtained. Finally, we perform a simulation test of performance before and after the optimization of the PID controller, in which vehicles equipped with a CCAS undertake deceleration driving and steering under the two states of low speed （≤50 km/h） and high speed （≥100 km/h） cruising. The results show that the PID controller optimized using the proposed method can achieve not only the basic functions of a CCAS, but also improvements in vehicle dynamic stability, riding comfort, and fuel economy.展开更多
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
Considering that the inevitable disturbances and coupled constraints pose an ongoing challenge to distributed control algorithms,this paper proposes a distributed robust model predictive control(MPC)algorithm for a mu...Considering that the inevitable disturbances and coupled constraints pose an ongoing challenge to distributed control algorithms,this paper proposes a distributed robust model predictive control(MPC)algorithm for a multi-agent system with additive external disturbances and obstacle and collision avoidance constraints.In particular,all the agents are allowed to solve optimization problems simultaneously at each time step to obtain their control inputs,and the obstacle and collision avoidance are accomplished in the context of full-dimensional controlled objects and obstacles.To achieve the collision avoidance between agents in the distributed framework,an assumed state trajectory is introduced for each agent which is transmitted to its neighbors to construct the polyhedral over-approximations of it.Then the polyhedral over-approximations of the agent and the obstacles are used to smoothly reformulate the original nonconvex obstacle and collision avoidance constraints.And a compatibility constraint is designed to restrict the deviation between the predicted and assumed trajectories.Moreover,recursive feasibility of each local MPC optimization problem with all these constraints derived and input-to-state stability of the closed-loop system can be ensured through a sufficient condition on controller parameters.Finally,simulations with four agents and two obstacles demonstrate the efficiency of the proposed algorithm.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.61233001 and 61322307)the Fundamental Research Funds for Central Universities of China(Grant No.2013JBZ007)
文摘The pedestrians can only avoid collisions passively under the action of forces during simulations using the social force model, which may lead to unnatural behaviors. This paper proposes an optimization-based model for the avoidance of collisions, where the social repulsive force is removed in favor of a search for the quickest path to destination in the pedestrian＇s vision field. In this way, the behaviors of pedestrians are governed by changing their desired walking direction and desired speed. By combining the critical factors of pedestrian movement, such as positions of the exit and obstacles and velocities of the neighbors, the choice of desired velocity has been rendered to a discrete optimization problem. Therefore,it is the self-driven force that leads pedestrians to a free path rather than the repulsive force, which means the pedestrians can actively avoid collisions. The new model is verified by comparing with the fundamental diagram and actual data. The simulation results of individual avoidance trajectories and crowd avoidance behaviors demonstrate the reasonability of the proposed model.
文摘文中针对多船会遇避碰决策中过渡依赖单一寻优决策的问题采用了加入自适应权重的樽海鞘群优化算法(weight salp swarm algorithm, WSSA),在算法中融入国际海上避碰规则(convention on the international regulations for presenting collisions at sea, COLREGs)和良好船艺的要求.使用速度障碍法判断船舶的碰撞危险度并将多船会遇避让的过程中避让的安全性、经济性以及船舶领域侵入程度作为建立避碰决策的目标函数.算法测试的结果中,WSSA与原始樽海鞘群算法(SSA)以及经典粒子群算法(partide swam optimization, PSO)相比较,WSSA算法在收敛的精度和速度方面都明显优于SSA和PSO算法.结果表明:WSSA在寻找最优碰撞路线的过程中迭代的次数更少,精度更高.
基金Project supported by the National Natural Science Foundation o4 China （No. 61300145）
文摘The introduction ofproportional-integral-dorivative （PID） controllers into cooperative collision avoidance systems （CCASs） has been hindered by difficulties in their optimization and by a lack of study of their effects on vehicle driving stability, comfort, and fuel economy. In this paper, we propose a method to optimize PID controllers using an improved particle swarm optimization （PSO） algorithm, and to bettor manipulate cooperative collision avoidance with other vehicles. First, we use PRESCAN and MATLAB/Simulink to conduct a united simulation, which constructs a CCAS composed of a PID controller, maneuver strategy judging modules, and a path planning module. Then we apply the improved PSO algorithm to optimize the PID controller based on the dynamic vehicle data obtained. Finally, we perform a simulation test of performance before and after the optimization of the PID controller, in which vehicles equipped with a CCAS undertake deceleration driving and steering under the two states of low speed （≤50 km/h） and high speed （≥100 km/h） cruising. The results show that the PID controller optimized using the proposed method can achieve not only the basic functions of a CCAS, but also improvements in vehicle dynamic stability, riding comfort, and fuel economy.
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.
基金the National Natural Science Foundation of China(Nos.62173036,62003040,62122014)the Beijing Institute of Technology Research Fund Program for Young Scholars.
文摘Considering that the inevitable disturbances and coupled constraints pose an ongoing challenge to distributed control algorithms,this paper proposes a distributed robust model predictive control(MPC)algorithm for a multi-agent system with additive external disturbances and obstacle and collision avoidance constraints.In particular,all the agents are allowed to solve optimization problems simultaneously at each time step to obtain their control inputs,and the obstacle and collision avoidance are accomplished in the context of full-dimensional controlled objects and obstacles.To achieve the collision avoidance between agents in the distributed framework,an assumed state trajectory is introduced for each agent which is transmitted to its neighbors to construct the polyhedral over-approximations of it.Then the polyhedral over-approximations of the agent and the obstacles are used to smoothly reformulate the original nonconvex obstacle and collision avoidance constraints.And a compatibility constraint is designed to restrict the deviation between the predicted and assumed trajectories.Moreover,recursive feasibility of each local MPC optimization problem with all these constraints derived and input-to-state stability of the closed-loop system can be ensured through a sufficient condition on controller parameters.Finally,simulations with four agents and two obstacles demonstrate the efficiency of the proposed algorithm.
