Research on autonomous planning method based on improved quantum Particle Swarm Optimization for Autonomous Underwater Vehicle

Underwater unmanned detector has been the crux to the exploration and exploitation of marine resources. With the development of undersea vehicles especially Autonomous Underwater Vehicle, higher and higher petitions of intelligent control and autonomous navigation are asked. The autonomous planning of AUV consists of two parts: one is the global path planning on the basis of the known environmental information; the other is the local planning for obstacle avoidance based on sensor data. This paper presents a QPSO algorithm with particle's taxonomic evolution, and designs AUV autonomous planning simulation software on the basis of QT development platform. For the known environment, we establish the annular space optimization model, adjust the velocity and heading of AUV to avoid obstacle by optimization algorithm. A method of the local planning for obstacle avoidance is proposed based on sensor detection window for unknown obstacles. The effectiveness of the proposed method is demonstrated by simulation.