Research Progress of Path Planning Methods for Autonomous Underwater Vehicle

Abstract

Path planning is a key technology for autonomous underwater vehicle (AUV) navigation. With the emphasis and research on AUV, AUV path planning technology is continuously developing. Path planning techniques generally include environment modelling methods and path planning algorithms. Based on a brief description of the environment modelling methods, this paper focuses on the path planning algorithms commonly used by AUV. According to the basic principles of the algorithm, the AUV path planning algorithms are divided into four categories: artificial potential field methods, geometric model search methods, random sampling methods, and intelligent bionic methods. In this review, we summarize in detail the development and application of various path planning algorithms in recent years. Meanwhile, we analyse the advantages and disadvantages of various algorithms and their improvement methods. Obstacles, ocean currents, and undersea terrain have an impact on AUV path planning. Therefore, how to deal with the complex underwater environment adds some limits to AUV path planning algorithms. In addition to the external environment, path planning algorithms also need to consider AUV’s physical constraints, such as energy constraints and motion constraints. Then, we analyse the motion constraints in AUV path planning. Finally, we discuss the development direction of AUV path planning algorithm. Time-varying ocean currents, special obstacles, multiobjective constraints, and practicability will be the problems that AUV path planning algorithms need to solve.

Path Planning of Multi-Objective Underwater Robot Based on Improved Sparrow Search Algorithm in Complex Marine Environment

“…According to the GEBCO database, the prior map information is obtained, and the continuous space submarine terrain information is approximately obtained by using the reciprocal difference method of adjacent point distance. The cost function is determined by combining the threat model, navigation height change model, underwater vehicle physical constraint model and energy consumption model under the time-varying ocean current environment, which need to be focused on in AUV path planning [32]. The experimental results show that the average cost of the improved SSA is lower than that of other algorithms in different cases.…”

An Overview of Machine Learning Techniques in Local Path Planning for Autonomous Underwater Vehicles

“…However, the majority of artificial neural network path planning algorithms have drawbacks, including extended learning times, weak generalisation abilities, sluggish processing speeds, and learning delays, making it challenging to ensure real-time path planning performance [54]. Despite their flaws, artificial neural network methods are nevertheless often utilised in AUV path planning because of their strong learning and adaptive capabilities, robustness, and parallelism [2] Spiked Neural Network (SNN).…”

“…The foundation of an AUV's navigation system and essential to its underwater operation is path planning. The importance of path planning for the safe and effective navigation of AUVs cannot be overstated [2].…”

Robust Path Planning Of Obstacle Avoidance For Unmanned delivery robot

“…And commonly used path planning methods include graph-based search, sampling-based methods, interpolating curves, and reaction-based methods, intelligent bionic algorithms, elc. (Zhou et al, 2022;Guo et al, 2021).…”