Resilient motion planning and control,without prior knowledge of disturbances,are crucial to ensure the safe and robust flight of quadrotors.The development of a motion planning and control architecture for quadrotors...Resilient motion planning and control,without prior knowledge of disturbances,are crucial to ensure the safe and robust flight of quadrotors.The development of a motion planning and control architecture for quadrotors,considering both internal and external disturbances(i.e.,motor damages and suspended payloads),is addressed.Firstly,the authors introduce the use of exponential functions to formulate trajectory planning.This choice is driven by its ability to predict thrust responses with minimal computational overhead.Additionally,a reachability analysis is incorporated for error dynamics resulting from multiple disturbances.This analysis sits at the interface between the planner and controller,contributing to the generation of more robust and safe spatial–temporal trajectories.Lastly,the authors employ a cascade controller,with the assistance of internal and external loop observers,to further enhance resilience and compensate the disturbances.The authors’benchmark experiments demonstrate the effectiveness of the proposed strategy in enhancing flight safety,particularly when confronted with motor damages and payload disturbances.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
Purpose–Autonomous Underwater Vehicles(AUVs)play a crucial role in marine biology research and oceanic natural resources exploration.Since most AUVs are underactuated they require sophisticated trajectory planning an...Purpose–Autonomous Underwater Vehicles(AUVs)play a crucial role in marine biology research and oceanic natural resources exploration.Since most AUVs are underactuated they require sophisticated trajectory planning and tracking algorithms.The purpose of this paper is to develop a new method that allows an underactuated AUV to track a moving object while constraining the approach to a direction tangent to the path of the target.Furthermore,the distance at which the AUV follows the target is constrained,reducing the probability of detection and unwanted behavior change of the target.Design/methodology/approach–First,a kinematic controller that generates a trajectory tangent to the path of the moving target is designed such that the AUV maintains a prescribed distance and approaches the target from behind.Using a Lyapunov based method the stability of the kinematic controller is proven.Second,a dynamic sliding mode controller is employed to drive the vehicle on the trajectory computed in the first step.Findings–The kinematic and dynamic controllers are shown to be stable and robust against parameter uncertainty in the dynamic model of the vehicle.Results of numerical simulations for equidistant tracking of a target on both smooth and discontinuous derivatives trajectories for a variety of relative initial positions and orientations are shown.Originality/value–The contribution of this research is development of a new method for path planning and tracking of moving targets for underactuated AUVs in the horizontal plane.The method allows control of both the direction of approach and the distance from a moving object.展开更多
基金National Natural Science Foundation of China,Grant/Award Numbers:62303412,62322314China Postdoctoral Science Foundation,Grant/Award Number:2022M722739Natural Science Foundation of Zhejiang Province,Grant/Award Number:2023YZ01。
文摘Resilient motion planning and control,without prior knowledge of disturbances,are crucial to ensure the safe and robust flight of quadrotors.The development of a motion planning and control architecture for quadrotors,considering both internal and external disturbances(i.e.,motor damages and suspended payloads),is addressed.Firstly,the authors introduce the use of exponential functions to formulate trajectory planning.This choice is driven by its ability to predict thrust responses with minimal computational overhead.Additionally,a reachability analysis is incorporated for error dynamics resulting from multiple disturbances.This analysis sits at the interface between the planner and controller,contributing to the generation of more robust and safe spatial–temporal trajectories.Lastly,the authors employ a cascade controller,with the assistance of internal and external loop observers,to further enhance resilience and compensate the disturbances.The authors’benchmark experiments demonstrate the effectiveness of the proposed strategy in enhancing flight safety,particularly when confronted with motor damages and payload disturbances.
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.
文摘Purpose–Autonomous Underwater Vehicles(AUVs)play a crucial role in marine biology research and oceanic natural resources exploration.Since most AUVs are underactuated they require sophisticated trajectory planning and tracking algorithms.The purpose of this paper is to develop a new method that allows an underactuated AUV to track a moving object while constraining the approach to a direction tangent to the path of the target.Furthermore,the distance at which the AUV follows the target is constrained,reducing the probability of detection and unwanted behavior change of the target.Design/methodology/approach–First,a kinematic controller that generates a trajectory tangent to the path of the moving target is designed such that the AUV maintains a prescribed distance and approaches the target from behind.Using a Lyapunov based method the stability of the kinematic controller is proven.Second,a dynamic sliding mode controller is employed to drive the vehicle on the trajectory computed in the first step.Findings–The kinematic and dynamic controllers are shown to be stable and robust against parameter uncertainty in the dynamic model of the vehicle.Results of numerical simulations for equidistant tracking of a target on both smooth and discontinuous derivatives trajectories for a variety of relative initial positions and orientations are shown.Originality/value–The contribution of this research is development of a new method for path planning and tracking of moving targets for underactuated AUVs in the horizontal plane.The method allows control of both the direction of approach and the distance from a moving object.
