A 2D Optimal Path Planning Algorithm for Autonomous Underwater Vehicle Driving in Unknown Underwater Canyons

Yushan Sun,Xiangrui Ran,Guocheng Zhang,Xiaokun Luo

doi:10.3390/jmse9030252

Abstract

This research aims to solve the safe navigation problem of autonomous underwater vehicles (AUVs) in deep ocean, which is a complex and changeable environment with various mountains. When an AUV reaches the deep sea navigation, it encounters many underwater canyons, and the hard valley walls threaten its safety seriously. To solve the problem on the safe driving of AUV in underwater canyons and address the potential of AUV autonomous obstacle avoidance in uncertain environments, an improved AUV path planning algorithm based on the deep deterministic policy gradient (DDPG) algorithm is proposed in this work. This method refers to an end-to-end path planning algorithm that optimizes the strategy directly. It takes sensor information as input and driving speed and yaw angle as outputs. The path planning algorithm can reach the predetermined target point while avoiding large-scale static obstacles, such as valley walls in the simulated underwater canyon environment, as well as sudden small-scale dynamic obstacles, such as marine life and other vehicles. In addition, this research aims at the multi-objective structure of the obstacle avoidance of path planning, modularized reward function design, and combined artificial potential field method to set continuous rewards. This research also proposes a new algorithm called deep SumTree-deterministic policy gradient algorithm (SumTree-DDPG), which improves the random storage and extraction strategy of DDPG algorithm experience samples. According to the importance of the experience samples, the samples are classified and stored in combination with the SumTree structure, high-quality samples are extracted continuously, and SumTree-DDPG algorithm finally improves the speed of the convergence model. Finally, this research uses Python language to write an underwater canyon simulation environment and builds a deep reinforcement learning simulation platform on a high-performance computer to conduct simulation learning training for AUV. Data simulation verified that the proposed path planning method can guide the under-actuated underwater robot to navigate to the target without colliding with any obstacles. In comparison with the DDPG algorithm, the stability, training’s total reward, and robustness of the improved Sumtree-DDPG algorithm planner in this study are better.

Highlights

To solve the problem on the safe driving of autonomous underwater vehicles (AUVs) in underwater canyons and tap the potential of AUV autonomous obstacle avoidance in uncertain environments, this paper proposes an improved AUV based on deep deterministic policy gradient (DDPG) path planning method
The path planning method can reach the predetermined target point while avoiding large-scale static obstacles that AUV can only detect a very small part of this obstacle by sonars, such as valley walls in the simulated underwater canyon environment, as well as sudden small-scale dynamic obstacles whose shape and size can be completely detected by the sonars of AUV, such as marine life and other underwater vehicles
This research aims at the multi-objective structure of the obstacle avoidance process of path planning, modularized reward function design, and combined artificial potential field method to set continuous rewards

Summary

Introduction

Autonomous underwater vehicles (AUVs) have elicited wide attention because of revolutionizing the oceanic research with applications on numerous scientific fields, such as marine geoscience, submarine oil exploration, submarine salvage, submarine pipeline repair, and archeology [1,2,3]. Among all of the functions of AUV, autonomous obstacle-avoidance capability is the most important one because the obstacles are usually unknown for AUVs in underwater environment; AUV can run into obstacles, thereby causing them to malfunction or even damage the robot [4]. Several autonomous navigation methods for obstacle-avoidance have been reported in the literature. Lozano et al [5,6] proposed visibility graph algorithm. In this algorithm, AUV is regarded as a bit, and obstacles are considered plane polygons. All attachment fellowships without path obstructions are considered collision-free path

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