With the advancement of technology,the collaboration of multiple unmanned aerial vehicles(multi-UAVs)is a general trend,both in military and civilian domains.Path planning is a crucial step for multi-UAV mission execu...With the advancement of technology,the collaboration of multiple unmanned aerial vehicles(multi-UAVs)is a general trend,both in military and civilian domains.Path planning is a crucial step for multi-UAV mission execution,it is a nonlinear problem with constraints.Traditional optimization algorithms have difficulty in finding the optimal solution that minimizes the cost function under various constraints.At the same time,robustness should be taken into account to ensure the reliable and safe operation of the UAVs.In this paper,a self-adaptive sparrow search algorithm(SSA),denoted as DRSSA,is presented.During optimization,a dynamic population strategy is used to allocate the searching effort between exploration and exploitation;a t-distribution perturbation coefficient is proposed to adaptively adjust the exploration range;a random learning strategy is used to help the algorithm from falling into the vicinity of the origin and local optimums.The convergence of DRSSA is tested by 29 test functions from the Institute of Electrical and Electronics Engineers(IEEE)Congress on Evolutionary Computation(CEC)2017 benchmark suite.Furthermore,a stochastic optimization strategy is introduced to enhance safety in the path by accounting for potential perturbations.Two sets of simulation experiments on multi-UAV path planning in three-dimensional environments demonstrate that the algorithm exhibits strong optimization capabilities and robustness in dealing with uncertain situations.展开更多
Homo-urbanicus is a planning concept which treats a human being as a rational animal with distinct material,social and intellectual characteristics,and a human settlement as a space in which human beings seek and offe...Homo-urbanicus is a planning concept which treats a human being as a rational animal with distinct material,social and intellectual characteristics,and a human settlement as a space in which human beings seek and offer opportunities for connection.Human-centered planning is the application of classical Natural Law(balance between self-preservation and mutual preservation)to the matching of human needs and human settlements.展开更多
Choosing the best path during unmanned air vehicle （UAV） flying is the target of the UAV mission planning problem. Because of its nearly constant flight height, the UAV mission planning problem can be treated as a 2...Choosing the best path during unmanned air vehicle （UAV） flying is the target of the UAV mission planning problem. Because of its nearly constant flight height, the UAV mission planning problem can be treated as a 2-D （horizontal） path arrangement problem. By modeling the antiaircraft threat, the UAV mission planning can be mapped to the traveling seaman problem （TSP）. A new algorithm is presented to solve the TSP. The algorithm combines the traditional ant colony system （ACS） with particle swarm optimization （PSO）, thus being called the AC-PSO algorithm. It uses one by one tour building strategy like ACS to determine that the target point can be chosen like PSO. Experiments show that AC-PSO synthesizes both ACS and PSO and obtains excellent solution of the UAV mission planning with a higher accuracy.展开更多
The problem of path planning is studied for t he case for a mobile robot moving in a known environment. An aggressive algorith m using a description of the obstacles based on a neural network is proposed. Th e algorit...The problem of path planning is studied for t he case for a mobile robot moving in a known environment. An aggressive algorith m using a description of the obstacles based on a neural network is proposed. Th e algorithm allows to construct an optimal path which is piecewise linear with c hanging directions of the obstacles and the calculation speed for the proposed a lgorithm is comparatively fast. Simulation results and an application to a car_l ike robot 'Khepera' show the effectiveness of the proposed algorithm.展开更多
基金Foundation items:National Natural Science Foundation of China(No.62303108)Fundamental Research Funds for the Central Universities,China(No.CUSF-DH-T-2023065)。
文摘With the advancement of technology,the collaboration of multiple unmanned aerial vehicles(multi-UAVs)is a general trend,both in military and civilian domains.Path planning is a crucial step for multi-UAV mission execution,it is a nonlinear problem with constraints.Traditional optimization algorithms have difficulty in finding the optimal solution that minimizes the cost function under various constraints.At the same time,robustness should be taken into account to ensure the reliable and safe operation of the UAVs.In this paper,a self-adaptive sparrow search algorithm(SSA),denoted as DRSSA,is presented.During optimization,a dynamic population strategy is used to allocate the searching effort between exploration and exploitation;a t-distribution perturbation coefficient is proposed to adaptively adjust the exploration range;a random learning strategy is used to help the algorithm from falling into the vicinity of the origin and local optimums.The convergence of DRSSA is tested by 29 test functions from the Institute of Electrical and Electronics Engineers(IEEE)Congress on Evolutionary Computation(CEC)2017 benchmark suite.Furthermore,a stochastic optimization strategy is introduced to enhance safety in the path by accounting for potential perturbations.Two sets of simulation experiments on multi-UAV path planning in three-dimensional environments demonstrate that the algorithm exhibits strong optimization capabilities and robustness in dealing with uncertain situations.
文摘Homo-urbanicus is a planning concept which treats a human being as a rational animal with distinct material,social and intellectual characteristics,and a human settlement as a space in which human beings seek and offer opportunities for connection.Human-centered planning is the application of classical Natural Law(balance between self-preservation and mutual preservation)to the matching of human needs and human settlements.
文摘Choosing the best path during unmanned air vehicle （UAV） flying is the target of the UAV mission planning problem. Because of its nearly constant flight height, the UAV mission planning problem can be treated as a 2-D （horizontal） path arrangement problem. By modeling the antiaircraft threat, the UAV mission planning can be mapped to the traveling seaman problem （TSP）. A new algorithm is presented to solve the TSP. The algorithm combines the traditional ant colony system （ACS） with particle swarm optimization （PSO）, thus being called the AC-PSO algorithm. It uses one by one tour building strategy like ACS to determine that the target point can be chosen like PSO. Experiments show that AC-PSO synthesizes both ACS and PSO and obtains excellent solution of the UAV mission planning with a higher accuracy.
文摘The problem of path planning is studied for t he case for a mobile robot moving in a known environment. An aggressive algorith m using a description of the obstacles based on a neural network is proposed. Th e algorithm allows to construct an optimal path which is piecewise linear with c hanging directions of the obstacles and the calculation speed for the proposed a lgorithm is comparatively fast. Simulation results and an application to a car_l ike robot 'Khepera' show the effectiveness of the proposed algorithm.
