Bearing-based hunting protocols commonly adopt a leaderless consensus method,which requests an entire state of the target for each agent and ignores the necessity of collision avoidance.We investigate a hunting proble...Bearing-based hunting protocols commonly adopt a leaderless consensus method,which requests an entire state of the target for each agent and ignores the necessity of collision avoidance.We investigate a hunting problem of multi-quadrotor systems with hybrid bearing protocols,where the quadrotor systems are divided into master and slave groups for reducing the onboard loads and collision avoidance.The masters obtain the entire state of the target,whose hybrid protocols are based on the displacement and bearing constraints to maintain formation and to avoid the collision in the hunting process.However,the slaves’protocols merely depend on the part state of the masters to reduce loads of data transmission.We also investigate the feasibility of receiving the bearing state from machine vision.The simulation results are given to illustrate the effectiveness of the proposed hybrid bearing protocols.展开更多
Challenges in motion planning for multiple quadrotors in complex environments lie in overall°ight e±ciency and the avoidance of obstacles,deadlock,and collisions among themselves.In this paper,we present a g...Challenges in motion planning for multiple quadrotors in complex environments lie in overall°ight e±ciency and the avoidance of obstacles,deadlock,and collisions among themselves.In this paper,we present a gradient-free trajectory generation method for multiple quadrotors in dynamic obstacle-dense environments with the consideration of time consumption.A model predictive control(MPC)-based approach for each quadrotor is proposed to achieve distributed and asynchronous cooperative motion planning.First,the motion primitives of each quadrotor are formulated as the boundary state constrained primitives(BSCPs)which are constructed with jerk limited trajectory(JLT)generation method,a boundary value problem(BVP)solver,to obtain time-optimal trajectories.They are then approximated with a neural network(NN),pre-trained using this solver to reduce the computational burden.The NN is used for fast evaluation with the guidance of a navigation function during optimization to guarantee°ight safety without deadlock.Finally,the reference trajectories are generated using the same BVP solver.Our simulation and experimental results demonstrate the superior performance of the proposed method.展开更多
In this paper, we design consensus algorithms for multiple unmanned aerial vehicles （UAV）. We mainly focus on the control design in the face of measurement noise and propose a position consensus controller based on ...In this paper, we design consensus algorithms for multiple unmanned aerial vehicles （UAV）. We mainly focus on the control design in the face of measurement noise and propose a position consensus controller based on the sliding mode control by using the distributed UAV information. Within the framework of Lyapunov theory, it is shown that all signals in the closed-loop multi- UAV systems are stabilized by the proposed algorithm, while consensus errors are uniformly ultimately bounded. Moreover, for each local UAV, we propose a mechanism to define the trustworthiness, based on which the edge weights are tuned to eliminate negative influence from stubborn agents or agents exposed to extremely noisy measurement. Finally, we develop software for a nano UAV platform, based on which we implement our algorithms to address measurement noises in UAV flight tests. The experimental results validate the effectiveness of the proposed algorithms.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61673217 and 61673214)the National Defense Basic Scientific Research Program of China(Grant No.JCKY2019606D001)the China Scholarship Council.
文摘Bearing-based hunting protocols commonly adopt a leaderless consensus method,which requests an entire state of the target for each agent and ignores the necessity of collision avoidance.We investigate a hunting problem of multi-quadrotor systems with hybrid bearing protocols,where the quadrotor systems are divided into master and slave groups for reducing the onboard loads and collision avoidance.The masters obtain the entire state of the target,whose hybrid protocols are based on the displacement and bearing constraints to maintain formation and to avoid the collision in the hunting process.However,the slaves’protocols merely depend on the part state of the masters to reduce loads of data transmission.We also investigate the feasibility of receiving the bearing state from machine vision.The simulation results are given to illustrate the effectiveness of the proposed hybrid bearing protocols.
基金supported in part by the Research Grants Council of Hong Kong SAR(Grant No.14209020)and in part by the Peng Cheng Laboratory.
文摘Challenges in motion planning for multiple quadrotors in complex environments lie in overall°ight e±ciency and the avoidance of obstacles,deadlock,and collisions among themselves.In this paper,we present a gradient-free trajectory generation method for multiple quadrotors in dynamic obstacle-dense environments with the consideration of time consumption.A model predictive control(MPC)-based approach for each quadrotor is proposed to achieve distributed and asynchronous cooperative motion planning.First,the motion primitives of each quadrotor are formulated as the boundary state constrained primitives(BSCPs)which are constructed with jerk limited trajectory(JLT)generation method,a boundary value problem(BVP)solver,to obtain time-optimal trajectories.They are then approximated with a neural network(NN),pre-trained using this solver to reduce the computational burden.The NN is used for fast evaluation with the guidance of a navigation function during optimization to guarantee°ight safety without deadlock.Finally,the reference trajectories are generated using the same BVP solver.Our simulation and experimental results demonstrate the superior performance of the proposed method.
基金This work was supported in part by the National Natural Science Foundation of China （No. 61633007, 61703112）, in part by the China Postdoctoral Science Foundation （No. 2016M600643） and the special fund （No. 2017T100618）, and in part by the Office of Naval Research （No. N00014-17-1-2239, NO0014-18-1-2221 ）.
文摘In this paper, we design consensus algorithms for multiple unmanned aerial vehicles （UAV）. We mainly focus on the control design in the face of measurement noise and propose a position consensus controller based on the sliding mode control by using the distributed UAV information. Within the framework of Lyapunov theory, it is shown that all signals in the closed-loop multi- UAV systems are stabilized by the proposed algorithm, while consensus errors are uniformly ultimately bounded. Moreover, for each local UAV, we propose a mechanism to define the trustworthiness, based on which the edge weights are tuned to eliminate negative influence from stubborn agents or agents exposed to extremely noisy measurement. Finally, we develop software for a nano UAV platform, based on which we implement our algorithms to address measurement noises in UAV flight tests. The experimental results validate the effectiveness of the proposed algorithms.
