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

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.

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

“…The authors also do not consider ocean currents and do not emphasize path time and path distance. Reference [84] uses a combination of reinforcement learning and artificial potential field for path planning. It overcomes the problem of under-driven AUVs travelling safely in underwater canyons.…”

USV Path Planning in a Hybrid Map Using a Genetic Algorithm with a Feedback Mechanism

“…Currently, prevalent intelligent optimization methods to address path planning issues include the ant colony algorithm [5][6][7], particle swarm algorithm [8,9], brainstorming algorithm [10,11], bacterial foraging optimization algorithm [12], reinforcement learning [13,14], and various other optimization techniques. The Genetic Algorithm (GA) is commonly used to solve path planning problems for USVs.…”

Path Planning of Deep-Sea Landing Vehicle Based on the Safety Energy-Dynamic Window Approach Algorithm

“…This compromise is mainly due to the degradation of the accuracy of forward sonar ranging caused by deep-sea noise. The detected obstacle volume may be smaller than the actual volume due to the decrease in accuracy [15,16]. (2) The limited energy of DSLV will be depleted more quickly due to the absence of energy consumption-related evaluation sub-functions in the DWA algorithm [3].…”