AN ONLINE PATH PLANNING METHOD BASED ON HYBRID QUANTUM ANT COLONY OPTIMIZATION FOR AUV

Path planning is one of the important autonomy abilities for autonomous underwater vehicle (AUV), whose main purpose is to plan an optimized and safety path autonomously during long-range navigation in an unknown environment. This paper proposes two path planners based on quantum ant colony optimization (QACO) and hybrid QACO for AUV in real time based on a sensor detection window. When AUV detects unknown static obstacles, the online path planners are scheduled to plan out a new path to avoid obstacles in the optimization window. To limit the yaw angle, a nonlinear fitness function is defined. In the hybrid QACO, an adaptive quantum gate and improved rules of pheromone updating are proposed according to the movement characteristic of AUV in the process of obstacle avoidance. A local search method is combined with QACO to improve the quality of the path planned by QACO and obtain a smoother path. Finally, the simulation experiments compare the performance of the proposed path planning methods with ant colony optimization.