Path planning and obstacle avoidance for auv: a review

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Path planning and obstacle avoidance for AUV: A review

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Ocean Engineering · September 2021

DOI: 10.1016/j.oceaneng.2021.109355

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Ocean University of China

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Ocean University of China

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Ocean Engineering 235 (2021) 109355

Available online 27 July 2021

0029-8018/© 2021 Elsevier Ltd. All rights reserved.

Contents lists available at

ScienceDirect

Ocean Engineering

journal homepage:

www.elsevier.com/locate/oceaneng

Path planning and obstacle avoidance for AUV: A review

Chunxi Cheng, Qixin Sha, Bo He, Guangliang Li

∗

College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China

A R T I C L E

I N F O

Keywords:

Autonomous underwater vehicle

Path planning

Obstacle avoidance

Dynamic obstacles

A B S T R A C T

Autonomous underwater vehicle plays a more and more important role in the exploration of marine resources.

Path planning and obstacle avoidance is the core technology to realize the autonomy of AUV, which will

determine the application prospect of AUV. This paper mainly describes the state-of-the-art methods of path

planning and obstacle avoidance for AUV and aims to become a starting point for researchers who are initiating

their endeavors in this field. Moreover, the objective of this paper is to give a comprehensive overview of

work on recent advances and new breakthroughs, also to discuss some future directions worthy to research

in this area. The focus of this article is put on these path planning algorithms that deal with constraints and

characteristics of AUV and the influence of marine environments. Since most of the time AUV will operate

in the environments full of obstacles, we divide path planning methods of AUV into two categories: global

path planning with known static obstacles, and local path planning with unknown and dynamic obstacles. We

describe the basic principles of each method and survey most related work to them. An in-depth discussion and

comparisons between different path planning algorithms are also provided. Lastly, we propose some potential

future research directions that are worthy to investigate in this field.