Three-Dimensional Path Planning Method for Autonomous Underwater Vehicle Based on Modified Firefly Algorithm

Chang Liu,Feng Gao,Yuxin Zhao,Liqiang Liu

doi:10.1155/2015/561394

Abstract

Path planning is a classic optimization problem which can be solved by many optimization algorithms. The complexity of three-dimensional (3D) path planning for autonomous underwater vehicles (AUVs) requires the optimization algorithm to have a quick convergence speed. This work provides a new 3D path planning method for AUV using a modified firefly algorithm. In order to solve the problem of slow convergence of the basic firefly algorithm, an improved method was proposed. In the modified firefly algorithm, the parameters of the algorithm and the random movement steps can be adjusted according to the operating process. At the same time, an autonomous flight strategy is introduced to avoid instances of invalid flight. An excluding operator was used to improve the effect of obstacle avoidance, and a contracting operator was used to enhance the convergence speed and the smoothness of the path. The performance of the modified firefly algorithm and the effectiveness of the 3D path planning method were proved through a varied set of experiments.

Highlights

autonomous underwater vehicles (AUVs) are in strong demand within both military and civil fields
We can conclude that the performance of AMFA is better than that of Firefly algorithm (FA) and the method proposed here is effective in improving the performance of basic FA
We can conclude that the method proposed here is effective in improving the performance of basic FA and AMFA is powerful in solving problems with a wide range

Summary

Introduction

AUVs are in strong demand within both military and civil fields. More and more research institutions are carrying out research into AUVs. Many methods for AUV path planning have been put forward and most of the AUV operating environments were assumed to be two-dimensional. Chen et al brought forward a global path planning method for AUV based on the sparse A∗ search algorithm [3]. Some novel optimization algorithms, such as the genetic algorithm (GA) and ant colony optimization (ACO), were used to solve the problems of AUV path optimization [4, 5]. These methods were proved effective in solving path planning problems, they inevitably face some problems in practical applications because the AUV working environment is a 3D marine space

Methods

Results

Conclusion