RHO-based convex optimization method applied to cooperative trajectory planning for multiple UAVs

This paper presents an optimization algorithm for trajectory planning of multiple unmanned aerial vehicles (UAVs). The goal is to generate three-dimensional trajectories satisfying the initial and terminal state constraints. The framework of cooperative trajectory planning is developed based on receding horizon optimization (RHO). The solution of trajectory optimization is obtained by using the convex optimization method, which serves as a convenient tool for dealing with equality and inequality constraints. The obstacle avoidance is also achieved by transforming the non-convex constraints to convex constraints. The effectiveness of the trajectory planning algorithm is tested by numerical simulations.

[1]  Ping Lu,et al.  Solving Nonconvex Optimal Control Problems by Convex Optimization , 2014 .

[2]  Zhou Rui,et al.  Three-dimensional path planning of UAV based on an improved A* algorithm* , 2016, 2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC).

[3]  Angela P. Schoellig,et al.  Generation of collision-free trajectories for a quadrocopter fleet: A sequential convex programming approach , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Yuan Dong,et al.  A Hybrid PSO Algorithm Based Flight Path Optimization for Multiple Agricultural UAVs , 2016, 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI).

[5]  Xuemei Ren,et al.  Trajectories planning for multiple UAVs by the cooperative and competitive PSO algorithm , 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).

[6]  Dan Wang,et al.  Optimal trajectory planning for a quadrotor via a Gauss Pseudo-spectrum Method , 2013, 2013 Ninth International Conference on Natural Computation (ICNC).