A Multi-Target Search Cooperative Obstacle Avoidance Algorithm based on Probability Finite State Machine

Quadrotor unmanned aerial vehicle(UAV) are widely used in environmental exploration, target search and other tasks due to their lightness, flexibility and adaptability to diverse environments, but they also have defects such as low efficiency and easy destruction due to their simple design. The swarm intelligence algorithm makes up for the shortcomings of drones. The combination of drone swarms and swarm intelligence algorithms is very suitable for target search tasks. This paper presents a model of the multi-target search problem of UAV swarms, introduces swarm intelligence optimization algorithm, random walk algorithm, formation search strategy, chooses a design method based on probability finite state machine to improve search efficiency, and then refers to gene regulation network power Learn to construct an artificial potential energy field to avoid obstacles, and finally propose a multi-target search cooperative obstacle avoidance algorithm based on probability finite state machine(PFSM). In order to restore the real scene as much as possible, this article conducts drone simulation experiments on the CoppeliaSim simulation platform.