A New Method of Solving UAV Trajectory Planning Under Obstacles and Multi-Constraint

Multi-constraint trajectory planning for unmanned aerial vehicles (UAVs) has been widely used in military and civil fields. The existing path planning methods, such as swarm intelligence algorithm and graph-based algorithm, cannot incorporate the flying time and UAV kinematic model into evolution. To overcome such disadvantage, a method of solving trajectory planning under obstacles and multi-constraint is investigated in this paper. Firstly, the flying time is discretized as a certain number of Chebyshev points which are the optimized moments of control variable, and they can reduce the computational burden. The process of solution is divided into multi-phase, i.e., two points as a phase to generate the trajectory. Then, angular velocity is taken as control variable, and function of angular velocity is solved by cubic spline interpolation. Besides, functions of angle and position are obtained by integration. The results are substituted into the model consisted by particle swarm optimization (PSO) and the UAV kinematic model to optimize. On this basis, the angular velocity, angle and position are calculated according to the allocated moments. Finally, Monte-Carlo simulation and comparison with existed method are carried out in obstacle environment. All the results illustrate that the multi-phase method can calculate the kinematic parameters of UAV accurately and plan smooth trajectories. Meanwhile, the proposed method is easier to meet the complicated constraints than the single-phase method. In addition, the dimensionality of solution is also enriched effectively.