Decentralized MPC-Based Trajectory Generation for Multiple Quadrotors in Cluttered Environments  

作　　者：Xinyi Wang Lele Xi Yizhou Chen Shupeng Lai Feng Lin Ben M.Chen 

机构地区：[1]Department of Mechanical and Automation Engineering,The Chinese University of Hong Kong,Shatin,N.T.,Hong Kong [2]School of Automation,Beijing Institute of Technology,Beijing,P.R.China [3]Peng Cheng Laboratory,Nanshan,Shenzhen,Guangdong,P.R.China

出　　处：《Guidance, Navigation and Control》2021年第2期40-59,共20页制导、导航与控制（英文）

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.

摘　　要：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.

关 键 词：Multi-quadrotor systems motion planning motion primitive model predictive control 

分 类 号：O17[理学—数学]