Pixel-to-Action Policy for Underwater Pipeline Following via Deep Reinforcement Learning

Liu, Yanan; Wang, Fang; Lv, Zeyu; Cao, Kaihui; Lin, Yuanshan

doi:10.1109/irce.2018.8492943

Cited by 8 publications

(2 citation statements)

References 5 publications

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“…Learning-based models were used to translate the acquired image into corresponding control commands. [19]. However, the reliabilities of vision-only approaches were dominated by visibility, and the difficulties in acquiring underwater datasets.…”

Section: B Related Workmentioning

confidence: 99%

An Autonomous Underwater Vehicle Simulation With Fuzzy Sensor Fusion for Pipeline Inspection

Sang

Norris

2023

IEEE Sensors J.

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Underwater pipeline inspection is an important topic in off-shore subsea operations. ROVs (Remotely Operated Vehicles) can play an important role in multiple application areas including military, ocean science, aquaculture, shipping, and energy. However, using ROVs for inspection is not cost-effective, and the fixed leak detection sensors mounted along the pipeline have limited precision. Although the cost can be significantly reduced by applying AUVs (Autonomous Underwater Vehicles), the unstable current, low visibility and loss of GPS signal make the navigation of AUVs underwater very challenging. Previous studies have been conducted on coordinate-based, vision-based, and fusion-based navigation algorithms. However, the coordinate-based algorithms suffered from the denial of GPS signals while the vision-based methods typically relied on terrain and landscape knowledge that required collection prior to the mission. As a result of these issues, a navigation system for an AUV (Autonomous Underwater Vehicle) that incorporates vision and sonar sensors is presented in this paper. In a ROS/Gazebo-based simulation environment, the AUV had the ability to find and navigate towards the pipeline and continuously traverse along its length. Additionally, with a chemical concentration sensor mounted on the AUV, the system demonstrated the capability of inspecting the pipeline and reporting the leak point with a resolution of 3 meters along the pipeline.

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Section: B Related Workmentioning

confidence: 99%

An Autonomous Underwater Vehicle Simulation With Fuzzy Sensor Fusion for Pipeline Inspection

Sang

Norris

2023

IEEE Sensors J.

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“…DRL combines the perception ability of deep learning with the decision-making ability of reinforcement learning, as shown in Figure 15(b). It can directly control AUV's motion based on the input image to solve the path planning problem of AUV [103]. Cao et al first used sonar imaging to obtain environmental information to establish a grid map of the AUV search area and then adopted the asynchronous advantage actor-critic (A3C) network structure to enable the AUV to learn from its own experience and generate search strategies for various unknown environments.…”

Section: Human-inspired Algorithmsmentioning

confidence: 99%

Research Progress of Path Planning Methods for Autonomous Underwater Vehicle

Guo

Liu

Fan

et al. 2021

Mathematical Problems in Engineering

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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.

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A control strategy of normal motion and active self-rescue for autonomous underwater vehicle based on deep reinforcement learning

et al. 2022

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The survivability of autonomous underwater vehicles (AUV) in complex missions and dangerous situations is of great significance to ocean resource exploration, hydrological research, maritime rescue, and undersea military. Existing researches on motion control for the AUV mainly focus on its normal operating, but the active self-rescue method in emergency situations is hardly found. As classical control methods are not sufficient enough for complicated self-rescue missions of the AUV, this paper uses the deep reinforcement learning (DRL) algorithm to solve this problem because the DRL algorithm has the advantages in learning and decision making for complex robot control missions. In this paper, the normal motion control of the AUV based on the deep deterministic policy gradient algorithm is explored, including the yaw angle adjustment, yaw angle adjustment extension, trajectory tracking, and normal floating-up control of the AUV. Then, active self-rescue methods are successfully achieved to recover the AUV from emergencies, such as ocean water density decreasing sharply or one fin getting jammed at a random angle. What is more, real environment experiments are successfully conducted on a self-developed platform of the AUV to validate the feasibility of the proposed control methods. The results can effectively improve the survivability of the AUV and can be a reference to submarine survivability technologies.

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Pixel-to-Action Policy for Underwater Pipeline Following via Deep Reinforcement Learning

Cited by 8 publications

References 5 publications

An Autonomous Underwater Vehicle Simulation With Fuzzy Sensor Fusion for Pipeline Inspection

An Autonomous Underwater Vehicle Simulation With Fuzzy Sensor Fusion for Pipeline Inspection

Research Progress of Path Planning Methods for Autonomous Underwater Vehicle

A control strategy of normal motion and active self-rescue for autonomous underwater vehicle based on deep reinforcement learning

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