Path Planning for AUVs Based on Improved APF-AC Algorithm

With the increase in ocean exploration activities and underwater development, the autonomous underwater vehicle (AUV) has been widely used as a type of underwater automation equipment in the detection of underwater environments. However, nowadays AUVs generally have drawbacks such as weak endurance, low intelligence, and poor detection ability. The research and implementation of path-planning methods are the premise of AUVs to achieve actual tasks. To improve the underwater operation ability of the AUV, this paper studies the typical problems of path-planning for the ant colony algorithm and the artificial potential field algorithm. In response to the limitations of a single algorithm, an optimization scheme is proposed to improve the artificial potential field ant colony (APF-AC) algorithm. Compared with traditional ant colony and comparative algorithms, the APF-AC reduced the path length by 1.57% and 0.63% (in the simple environment), 8.92% and 3.46% (in the complex environment). The iteration time has been reduced by approximately 28.48% and 18.05% (in the simple environment), 18.53% and 9.24% (in the complex environment). Finally, the improved APF-AC algorithm has been validated on the AUV platform, and the experiment is consistent with the simulation. Improved APF-AC algorithm can effectively reduce the underwater operation time and overall power consumption of the AUV, and shows a higher safety.