AUV 3D Path Planning Based on the Improved Hierarchical Deep Q Network

Sun, Yougang; Ran, Xiangrui; Zhang, Guocheng; Xu, Hao; Wang, Xiang-Bin

doi:10.3390/jmse8020145

Cited by 63 publications

(29 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2020, Sun et al used the hierarchical deep Q network in 3D path planning. e algorithm divided the task of path planning into three layers to solve the problem of dimension disaster and modified the reward function according to the different requirements of the task [114]. However, there is relatively little research in this area.…”

Section: Direction C: Intelligent Path Planning Algorithmsmentioning

confidence: 99%

Research Progress of Path Planning Methods for Autonomous Underwater Vehicle

Guo

Liu

Fan

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

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.

show abstract

Section: Direction C: Intelligent Path Planning Algorithmsmentioning

confidence: 99%

Research Progress of Path Planning Methods for Autonomous Underwater Vehicle

Guo

Liu

Fan

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…As shown in Figure 4, the obstacle avoidance in the water conveyance tunnel mainly refers to: First, avoiding the vessel meeting holes in the tunnel to prevent the AUV from entering the meeting hole, affecting the relative positioning of the AUV and even causing damage to the AUV. The second is to avoid non-outlet branch.es Since there are many branch exits in the water conveyance tunnel and the AUV needs to be recovered from the designated branch exits [13], it is necessary to identify the branch exits based on the positioning data [14] to avoid driving out from the wrong branch exit and affecting the completion of the work task and the AUV recycling. In order to prevent the AUV from missing the exit and to ensure that the AUV can be successfully recovered, a light source with a specified frequency is placed at the exit.…”

Section: Architecture Designmentioning

confidence: 99%

Design and Reliability Analysis of a Tunnel-Detection AUV Based on a Heterogeneous Dual CPU Hot Redundancy System

et al. 2020

Self Cite

View full text Add to dashboard Cite

A water conveyance tunnel is narrow and enclosed with a complex distribution of flow field. The performance of sensors such as Doppler log, magnetic compass, sonar, and depth gauge used by conventional underwater vehicles in the tunnel is greatly affected and can even fail. Aiming at the special operating environment and operational requirements of water conveyance tunnels, this paper designed an architecture suitable for pressurized water conveyance tunnel-detection autonomous underwater vehicles (AUVs). The tunnel-detection AUV (called AUV-T in this paper) with the architecture proposed in this paper could easily and smoothly complete inspection tasks in water conveyance tunnels, and field tests have verified the effectiveness of the architecture. Since an AUV in a water conveyance tunnel cannot go to the surface to rescue itself, in order to ensure its safety we designed the heterogeneous dual-CPU (Central Processing Unit) hot redundancy system based on dual communication lines. The reliability analysis showed that the system can significantly reduce the probability of AUV failure and ensure that the AUV can still be recovered even if it fails in the tunnel.

show abstract

“…When an AUV performs underwater tasks, it is faced with complex environments and situations such as sudden obstacles and unknown water environment. Therefore, it is urgent to improve AUV's motion planning capability under complex dynamic environment [2].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Motion Planning of AUVs in Three Typical Marine Environments

et al. 2021

Self Cite

View full text Add to dashboard Cite

Broad waters, harbor waters, and waterway waters make up more than 90% of autonomous underwater vehicles (AUV) navigation area, and each of them has its typical environmental characteristics. In this paper, a three-layer AUV motion planning architecture was designed to improve the planning logic of an AUV when completing complex underwater tasks. The AUV motion planning ability was trained by the improved deep deterministic policy gradient (DDPG) combined with the experience pool of classification. Compared with the traditional DDPG algorithm, the proposed algorithm is more efficient. Using the strategy obtained from the training and the motion planning architecture proposed in the paper, the tasks of AUVs searching in broad waters, crossing in waterway waters and patrolling in harbor waters were realized in the simulation experiment. The reliability of the planning system was verified in field tests.

show abstract

AUV 3D Path Planning Based on the Improved Hierarchical Deep Q Network

Cited by 63 publications

References 23 publications

Research Progress of Path Planning Methods for Autonomous Underwater Vehicle

Research Progress of Path Planning Methods for Autonomous Underwater Vehicle

Design and Reliability Analysis of a Tunnel-Detection AUV Based on a Heterogeneous Dual CPU Hot Redundancy System

Hierarchical Motion Planning of AUVs in Three Typical Marine Environments

Contact Info

Product

Resources

About