A Method for Underwater Human–Robot Interaction Based on Gestures Tracking with Fuzzy Control

Jiang, Yu; Zhao, Minghao; Wang, Chong; Wei, Fenglin; Qi, Hong

doi:10.1007/s40815-021-01086-x

Cited by 9 publications

(3 citation statements)

References 30 publications

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“…In 2021, Remmas et al [31] presented a study using data-fusion of various data from inexpensive sensors to improve perception in dynamic and unstructured ocean environments of divers. In 2021, Jiang et al [32] also proposed a gesture tracking method for underwater human-robot interaction based on fuzzy control.…”

Section: Related Workmentioning

confidence: 99%

Towards Multi-AUV Collaboration and Coordination: A Gesture-Based Multi-AUV Hierarchical Language and a Language Framework Comparison System

Chiarella

2023

JMSE

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The underwater environment is a harmful environment, yet one of the richest and least exploited. For these reasons the idea of a robotic companion with the task of supporting and monitoring divers during their activities and operations has been proposed. However, the idea of a platoon of robots at the diver’s disposal has never been fully addressed in these proposals due to the high cost of implementation and the usability, weight and bulk of the robots. Nevertheless, recent advancements in swarm robotics, materials engineering, deep learning, and the decreasing cost of autonomous underwater vehicles (AUVs), have rendered this concept increasingly viable. Therefore, this paper introduces, in the first part, a novel framework that integrates a revised version of a gesture-based language for underwater human–robot interaction (Caddian) based on insights gained from extensive field trials. The newly introduced objective of this framework is to enable the cooperation and coordination of an AUV team by one or more human operators, while allowing a human operator to delegate a robot leader to instruct the other robotic team members. The work, in the second part, provides an evaluation of the new language proposed thanks to a fifty million sentence corpus and describes a comparison framework, which is used to estimate it with respect to other existing underwater human–robot interaction languages.

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Section: Related Workmentioning

confidence: 99%

Towards Multi-AUV Collaboration and Coordination: A Gesture-Based Multi-AUV Hierarchical Language and a Language Framework Comparison System

Chiarella

2023

JMSE

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“…Due to the shortage of land resources, the exploitation of the marine field has been paid more attention to recently. As an essential part of many underwater tasks, underwater object detection technology plays an important role in various fields, including marine resource exploitation, marine biological fishing and breeding, and underwater humanrobot interaction [1,2]. A lightweight and accurate underwater object detection algorithm is also of great significance to the intelligence of unmanned underwater vehicles (UUVs).…”

Section: Introductionmentioning

confidence: 99%

Self-Supervised Pre-Training Joint Framework: Assisting Lightweight Detection Network for Underwater Object Detection

Wang

Chen

Qin

et al. 2023

JMSE

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In the computer vision field, underwater object detection has been a challenging task. Due to the attenuation of light in a medium and the scattering of light by suspended particles in water, underwater optical images often face the problems of color distortion and target feature blurring, which greatly affect the detection accuracy of underwater object detection. Although deep learning-based algorithms have achieved state-of-the-art results in the field of object detection, most of them cannot be applied to practice because of the limited computing capacity of a low-power processor embedded in unmanned underwater vehicles. This paper proposes a lightweight underwater object detection network based on the YOLOX model called LUO-YOLOX. A novel weighted ghost-CSPDarknet and simplified PANet were used in LUO-YOLOX to reduce the parameters of the whole model. Moreover, aiming to solve the problems of color distortion and unclear features of targets in underwater images, this paper proposes an efficient self-supervised pre-training joint framework based on underwater auto-encoder transformation (UAET). After the end-to-end pre-training process with the self-supervised pre-training joint framework, the backbone of the object detection network can extract more essential and robust features from degradation images when retrained on underwater datasets. Numerous experiments on the URPC2021 and detecting underwater objects (DUO) datasets verify the performance of our proposed method. Our work can assist unmanned underwater vehicles to perform underwater object detection tasks more accurately.

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“…The multi-AUV hunting, which refers to the capture of mobile targets (evaders) through collaboration between AUVs (the hunters) in unknown environments, is a typical case of multi-AUV coordination. It involves interdisciplinary knowledge such as navigation, control, communication, and cooperation among multiple intelligent agents [3][4][5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

State Super Sampling Soft Actor–Critic Algorithm for Multi-AUV Hunting in 3D Underwater Environment

Wang

Sui

Qin

et al. 2023

JMSE

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Reinforcement learning (RL) is known for its efficiency and practicality in single-agent planning, but it faces numerous challenges when applied to multi-agent scenarios. In this paper, a Super Sampling Info-GAN (SSIG) algorithm based on Generative Adversarial Networks (GANs) is proposed to address the problem of state instability in Multi-Agent Reinforcement Learning (MARL). The SSIG model allows a pair of GAN networks to analyze the previous state of dynamic system and predict the future state of consecutive state pairs. A multi-agent system (MAS) can deduce the complete state of all collaborating agents through SSIG. The proposed model has the potential to be employed in multi-autonomous underwater vehicle (multi-AUV) planning scenarios by combining it with the Soft Actor–Critic (SAC) algorithm. Hence, this paper presents State Super Sampling Soft Actor–Critic (S4AC), which is a new algorithm that combines the advantages of SSIG and SAC and can be applied to Multi-AUV hunting tasks. The simulation results demonstrate that the proposed algorithm has strong learning ability and adaptability and has a considerable success rate in hunting the evading target in multiple testing scenarios.

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A Method for Underwater Human–Robot Interaction Based on Gestures Tracking with Fuzzy Control

Cited by 9 publications

References 30 publications

Towards Multi-AUV Collaboration and Coordination: A Gesture-Based Multi-AUV Hierarchical Language and a Language Framework Comparison System

Towards Multi-AUV Collaboration and Coordination: A Gesture-Based Multi-AUV Hierarchical Language and a Language Framework Comparison System

Self-Supervised Pre-Training Joint Framework: Assisting Lightweight Detection Network for Underwater Object Detection

State Super Sampling Soft Actor–Critic Algorithm for Multi-AUV Hunting in 3D Underwater Environment

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