3D ONLINE PATH PLANNING OF UAV BASED ON IMPROVED DIFFERENTIAL EVOLUTION AND MODEL PREDICTIVE CONTROL

This paper presents an efficient 3D online path planning algorithm for UAV flying in partially known environment. The algorithm integrates model predictive control (MPC) and differential evolution (DE) as the planning strategy. In the initial stage, the artificial potential field (APF) model is developed to describe the mutual effect between the UAV and the surrounding environments. Afterwards, a novel objective function is proposed to address the optimization problem of multi-objective and multi-constraints, which take into account the path length, the smoothness degree of a path and the safety of a path. In addition, the multiple constraints based on the realistic scenarios are taken into account, including maximum acceleration, maximum velocity, map and threat constraints. Then, the improved differential evolution algorithm based on the theory of MPC, is developed to optimize the objective function to find the optimal path. Finally, to show the high performance of the proposed method, we compare the proposed algorithm with the existing optimization algorithms and several extended algorithms. The results reveal that the proposed algorithm not only produces an optimal plan for UAV in a local known 3D environment, but also has better performances in terms of running time and stability.