With the rapid growth of the number and flight time of unmanned aerial vehicles(UAVs),safety accidents caused by UAVs flight risk is increasing gradually.Safe air route planning is an effective means to reduce the ope...With the rapid growth of the number and flight time of unmanned aerial vehicles(UAVs),safety accidents caused by UAVs flight risk is increasing gradually.Safe air route planning is an effective means to reduce the operational risk of UAVs at the strategic level.The optimal air route planning model based on ground risk assessment is presented by considering the safety cost of UAV air route.Through the rasterization of the ground surface under the air route,the safety factor of each grid is defined with the probability of fatality on the ground per flight hour as the quantitative index.The air route safety cost function is constructed based on the safety factor of each grid.Then,the total cost function considering both air route safety and flight distance is established.The expected function of the ant colony algorithm is rebuilt and used as the algorithm to plan the air routes.The effectiveness of the new air route planning model is verified through the logistical distribution scenario on urban airspace.The results indicate that the new air route planning model considering safety factor can greatly improve the overall safety of air route under small increase of the total flight time.展开更多
An ant colony optimization with artificial potential field(ACOAPF)algorithm is proposed to solve the cooperative search mission planning problem of unmanned aerial vehicle(UAV)swarm.This algorithm adopts a distributed...An ant colony optimization with artificial potential field(ACOAPF)algorithm is proposed to solve the cooperative search mission planning problem of unmanned aerial vehicle(UAV)swarm.This algorithm adopts a distributed architecture where each UAV is considered as an ant and makes decision autonomously.At each decision step,the ants choose the next gird according to the state transition rule and update its own artificial potential field and pheromone map based on the current search results.Through iterations of this process,the cooperative search of UAV swarm for mission area is realized.The state transition rule is divided into two types.If the artificial potential force is larger than a threshold,the deterministic transition rule is adopted,otherwise a heuristic transition rule is used.The deterministic transition rule can ensure UAVs to avoid the threat or approach the target quickly.And the heuristics transition rule considering the pheromone and heuristic information ensures the continuous search of area with the goal of covering more unknown area and finding more targets.Finally,simulations are carried out to verify the effectiveness of the proposed ACOAPF algorithm for cooperative search mission of UAV swarm.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
Based on ADS-B surveillance data,this paper proposes a multi-unmanned aerial vehicle(multi-UAV)collision detection method based on linear extrapolation for ground-based UAV collision detection and resolution,thus to p...Based on ADS-B surveillance data,this paper proposes a multi-unmanned aerial vehicle(multi-UAV)collision detection method based on linear extrapolation for ground-based UAV collision detection and resolution,thus to provide early warning of possible conflicts.To address the problem of multi-UAV conflict,the basic ant colony algorithm is introduced.The conflict simplification model of the traditional basic ant colony algorithm is optimized by adding a speed regulation strategy.A multi-UAV conflict resolution scheme is presented based on speed regulation and heading strategies.The ant colony algorithm is improved by adding angle information and a queuing system.The results show that the improved ant colony algorithm can provide multi-UAV joint escape routes for a multi-UAV conflict situation in airspace.Unlike the traditional ant colony algorithm,our approach converges to the optimization target.The time required for the calculation is reduced by 43.9%,and the total delay distance caused by conflict resolution is reduced by 58.4%.展开更多
为了提高无人机路径规划中的避障效率,首先针对全局规划,提出一种改进蚁群算法TSACO(turning-sensitive ant colony optimization)。该算法利用A算法进行非均匀分配初始信息素,通过在概率函数中引入转向启发函数,以及采用精英蚂蚁系统...为了提高无人机路径规划中的避障效率,首先针对全局规划,提出一种改进蚁群算法TSACO(turning-sensitive ant colony optimization)。该算法利用A算法进行非均匀分配初始信息素,通过在概率函数中引入转向启发函数,以及采用精英蚂蚁系统等方法,来提高算法的收敛速度,减少路径的转角次数。其次,针对局部规划提出一种改进速度障碍算法,加入了无人机动力学方程,考虑障碍物自适应碰撞半径和紧急碰撞锥,以及最优速度选择法等,来改善无人机局部避障的实时性与安全性。仿真实验表明,该算法相较于其他算法,在路径长度、转向次数及动态避障等方面,均具有更好的有效性。展开更多
基金This work is supported by the Scientific Research Project of Tianjin Education Commission(No.2019KJ128).
文摘With the rapid growth of the number and flight time of unmanned aerial vehicles(UAVs),safety accidents caused by UAVs flight risk is increasing gradually.Safe air route planning is an effective means to reduce the operational risk of UAVs at the strategic level.The optimal air route planning model based on ground risk assessment is presented by considering the safety cost of UAV air route.Through the rasterization of the ground surface under the air route,the safety factor of each grid is defined with the probability of fatality on the ground per flight hour as the quantitative index.The air route safety cost function is constructed based on the safety factor of each grid.Then,the total cost function considering both air route safety and flight distance is established.The expected function of the ant colony algorithm is rebuilt and used as the algorithm to plan the air routes.The effectiveness of the new air route planning model is verified through the logistical distribution scenario on urban airspace.The results indicate that the new air route planning model considering safety factor can greatly improve the overall safety of air route under small increase of the total flight time.
基金supported by the National Natural Science Foundation of China (Nos.61973158, 61673209)the Aeronautical Science Foundation (No.2016ZA52009)
文摘An ant colony optimization with artificial potential field(ACOAPF)algorithm is proposed to solve the cooperative search mission planning problem of unmanned aerial vehicle(UAV)swarm.This algorithm adopts a distributed architecture where each UAV is considered as an ant and makes decision autonomously.At each decision step,the ants choose the next gird according to the state transition rule and update its own artificial potential field and pheromone map based on the current search results.Through iterations of this process,the cooperative search of UAV swarm for mission area is realized.The state transition rule is divided into two types.If the artificial potential force is larger than a threshold,the deterministic transition rule is adopted,otherwise a heuristic transition rule is used.The deterministic transition rule can ensure UAVs to avoid the threat or approach the target quickly.And the heuristics transition rule considering the pheromone and heuristic information ensures the continuous search of area with the goal of covering more unknown area and finding more targets.Finally,simulations are carried out to verify the effectiveness of the proposed ACOAPF algorithm for cooperative search mission of UAV swarm.
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supported by the National Natural Science Foundation of China (No. 61773202)the National Key Laboratory of Air Traffic Control (No.SKLATM201706)the Sichuan Science and Technology Plan Project(No. 2018JZ0030).
文摘Based on ADS-B surveillance data,this paper proposes a multi-unmanned aerial vehicle(multi-UAV)collision detection method based on linear extrapolation for ground-based UAV collision detection and resolution,thus to provide early warning of possible conflicts.To address the problem of multi-UAV conflict,the basic ant colony algorithm is introduced.The conflict simplification model of the traditional basic ant colony algorithm is optimized by adding a speed regulation strategy.A multi-UAV conflict resolution scheme is presented based on speed regulation and heading strategies.The ant colony algorithm is improved by adding angle information and a queuing system.The results show that the improved ant colony algorithm can provide multi-UAV joint escape routes for a multi-UAV conflict situation in airspace.Unlike the traditional ant colony algorithm,our approach converges to the optimization target.The time required for the calculation is reduced by 43.9%,and the total delay distance caused by conflict resolution is reduced by 58.4%.
文摘为了提高无人机路径规划中的避障效率,首先针对全局规划,提出一种改进蚁群算法TSACO(turning-sensitive ant colony optimization)。该算法利用A算法进行非均匀分配初始信息素,通过在概率函数中引入转向启发函数,以及采用精英蚂蚁系统等方法,来提高算法的收敛速度,减少路径的转角次数。其次,针对局部规划提出一种改进速度障碍算法,加入了无人机动力学方程,考虑障碍物自适应碰撞半径和紧急碰撞锥,以及最优速度选择法等,来改善无人机局部避障的实时性与安全性。仿真实验表明,该算法相较于其他算法,在路径长度、转向次数及动态避障等方面,均具有更好的有效性。
