Path Planning for AUVs Based on Improved APF-AC Algorithm  

作　　者：Guojun Chen Danguo Cheng Wei Chen Xue Yang Tiezheng Guo 

机构地区：[1]Industrial Center,Nanjing Institute of Technology,Nanjing,211167,China

出　　处：《Computers, Materials & Continua》2024年第3期3721-3741,共21页计算机、材料和连续体（英文）

基　　金：supported by Research Program supported by the National Natural Science Foundation of China(No.62201249);the Jiangsu Agricultural Science and Technology Innovation Fund(No.CX(21)1007);the Open Project of the Zhejiang Provincial Key Laboratory of Crop Harvesting Equipment and Technology(Nos.2021KY03,2021KY04);University-Industry Collaborative Education Program(No.201801166003);the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.SJCX22_1042).

摘　　要：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.

关 键 词：PATH-PLANNING autonomous underwater vehicle ant colony algorithm artificial potential field bio-inspired neural network 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]