This paper proposes a new three-layer path planning method,where we fused two existing path planning methods(global path and local path)into a single problem for multi-unmanned aerial vehicles(UAVs)path planning for U...This paper proposes a new three-layer path planning method,where we fused two existing path planning methods(global path and local path)into a single problem for multi-unmanned aerial vehicles(UAVs)path planning for UAV.The global-path network layer contains the latest information and algorithms for global planning according to specific applications.The trajectory planning layer represents the kinematics and different motion characteristics,the planningexecution layer implements the local planning algorithm for obstacle avoidance.In the last layer,we propose a new swarm intelligence algorithm called the refraction principle and opposite-based-learning moth flame optimization(ROBL-MFO).In contrast to the classical MFO,the proposed algorithm addresses the shortcoming of the classical MFO algorithm.First,it adapts the moth position update formula to the notion of historical optimal flame average and improves the convergence speed of the algorithm.Second,it utilizes a random inverse learning strategy to narrow down the search space.Finally,the principle of refraction gives the algorithm the ability to jump out of local optima and helps the algorithm avoid premature convergence.The experimental results show that the performance of the proposed algorithm is versatile,robust,and stable.展开更多
Existing solutions for collaborative trajectory planning using multiple UAVs suffer from issues such as low accuracy,instability,and slow convergence.To address the aforementioned issues,this paper introduces a new me...Existing solutions for collaborative trajectory planning using multiple UAVs suffer from issues such as low accuracy,instability,and slow convergence.To address the aforementioned issues,this paper introduces a new method for multiple unmanned aerial vehicle(UAV)3D terrain cooperative trajectory planning based on the cuck0o search golden jackal optimization(CS-GJO)algorithm.A model for single UAV trajectory planning and a model for multi-UAV collaborative trajectory planning have been developed,and the problem of solving the models is restructured into an optimization problem.Building upon the original golden jackal optimization,the use of tent chaotic mapping aids in the generation of the golden jackal's inital population,thereby promoting population diversity.Subsequently,the position update strategy of the cuckoo search algorithm is combined for purpose of update the position information of individual golden jackals,effectively preventing the algorithm from getting stuck in local minima.Finally,the corresponding nonlinear control parameter were developed.The new parameters expedite the decrease in the convergence factor during the pre-exploration stage,resulting in an improved overall search speed of the algorithm.Moreover,they attenuate the decrease in the convergence factor during the post-exploration stage,thereby enhancing the algorithm's global search.The experimental results demonstrate that the CS-GJO algorithm efficiently and accurately accomplishes multi-UAV cooperative trajectory planning in a 3D environment.Compared with other comparative algorithms,the CS-GJO algorithm also has better stability,higher optimization accuracy,and faster convergence speed.展开更多
文摘This paper proposes a new three-layer path planning method,where we fused two existing path planning methods(global path and local path)into a single problem for multi-unmanned aerial vehicles(UAVs)path planning for UAV.The global-path network layer contains the latest information and algorithms for global planning according to specific applications.The trajectory planning layer represents the kinematics and different motion characteristics,the planningexecution layer implements the local planning algorithm for obstacle avoidance.In the last layer,we propose a new swarm intelligence algorithm called the refraction principle and opposite-based-learning moth flame optimization(ROBL-MFO).In contrast to the classical MFO,the proposed algorithm addresses the shortcoming of the classical MFO algorithm.First,it adapts the moth position update formula to the notion of historical optimal flame average and improves the convergence speed of the algorithm.Second,it utilizes a random inverse learning strategy to narrow down the search space.Finally,the principle of refraction gives the algorithm the ability to jump out of local optima and helps the algorithm avoid premature convergence.The experimental results show that the performance of the proposed algorithm is versatile,robust,and stable.
基金supported by the Key Research and Development Program of Henan Province (No.241111222900)Natural Science Foundation of Henan (No.242300421716)+2 种基金Key Science and Technology Program of Henan Province (Nos.242102220044 and 242102210034)National Natural Science Foundation of China (No.62103379)Maker Space Incubation Project (No.2023ZCKJ102).
文摘Existing solutions for collaborative trajectory planning using multiple UAVs suffer from issues such as low accuracy,instability,and slow convergence.To address the aforementioned issues,this paper introduces a new method for multiple unmanned aerial vehicle(UAV)3D terrain cooperative trajectory planning based on the cuck0o search golden jackal optimization(CS-GJO)algorithm.A model for single UAV trajectory planning and a model for multi-UAV collaborative trajectory planning have been developed,and the problem of solving the models is restructured into an optimization problem.Building upon the original golden jackal optimization,the use of tent chaotic mapping aids in the generation of the golden jackal's inital population,thereby promoting population diversity.Subsequently,the position update strategy of the cuckoo search algorithm is combined for purpose of update the position information of individual golden jackals,effectively preventing the algorithm from getting stuck in local minima.Finally,the corresponding nonlinear control parameter were developed.The new parameters expedite the decrease in the convergence factor during the pre-exploration stage,resulting in an improved overall search speed of the algorithm.Moreover,they attenuate the decrease in the convergence factor during the post-exploration stage,thereby enhancing the algorithm's global search.The experimental results demonstrate that the CS-GJO algorithm efficiently and accurately accomplishes multi-UAV cooperative trajectory planning in a 3D environment.Compared with other comparative algorithms,the CS-GJO algorithm also has better stability,higher optimization accuracy,and faster convergence speed.
