As an emerging unmanned combat force and indispensable civilian equipment

unmanned aerial vehicles (UAVs) have gradually been integrated into all aspects of national security and social development. Path planning is the core link to ensure that UAVs successfully complete the established task. In order to solve the problem of real-time path planning with many static and dynamic threats in the planning space

a method of autonomous obstacle avoidance path planning with receding horizon is proposed. Firstly

the path planning model was constructed as a single objective function optimization problem. According to the simplified kinematic model and constraints of the UAV

the receding horizon optimization strategy was used to generate the optimal path sequence. Then

the receding horizon optimization strategy was also used to generate sub-sequences for the trajectories between the optimal path sequences. Considering the threat and flight constraints

the negative gradient descent method was used to search the waypoint

and the genetic algorithm was used to plan the sub-sequences. Finally

the approximate global optimal path was obtained by repeated receding iterative optimization

and the trajectory was processed by bezier curve to represent the actual flight path. The experimental simulation results show that the model is reasonable and the method is effective. Meanwhile

it also has good threat avoidance ability and can plan a smooth path. Compared with the global planning method

the proposed method reduces the convergence time

has stronger real-time performance

and can converge to the approximate global optimal solution quickly and robustly.