DiverNet &amp;#x2014; A network of inertial sensors for real time diver visualization

Goodfellow, Geraint M.; Neasham, Jeffrey; Rendulic, Ivor; Nađ, Ðula; Mišković, Nikola

doi:10.1109/sas.2015.7133640

Cited by 13 publications

(9 citation statements)

References 13 publications

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“…One version of the system encompasses a MEDUSA USV [222] to determine its localization, the localization of a MEDUSA UUV (the diver buddy) and feedback it to the UUV and the diver [223]. The CADDY project also devised a way to reconstruct and track diver poses using 17 inertial sensors over the diver’s body [224] fused with an analysis of stereo cameras from the UUV using a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) algorithm [225], to increase the understanding of diver behavior and to detect possible risks to the underwater diver. Mišković et al [226] propose a way to track divers using a PlaDyPos USV, equipped with an acoustic positioning device, directly over the diver.…”

Section: Usv Applications For Preventive Maintenance and Disaster mentioning

confidence: 99%

A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions

Jorge

Granada

Maidana

et al. 2019

Sensors

141

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Disaster robotics has become a research area in its own right, with several reported cases of successful robot deployment in actual disaster scenarios. Most of these disaster deployments use aerial, ground, or underwater robotic platforms. However, the research involving autonomous boats or Unmanned Surface Vehicles (USVs) for Disaster Management (DM) is currently spread across several publications, with varying degrees of depth, and focusing on more than one unmanned vehicle—usually under the umbrella of Unmanned Marine Vessels (UMV). Therefore, the current importance of USVs for the DM process in its different phases is not clear. This paper presents the first comprehensive survey about the applications and roles of USVs for DM, as far as we know. This work demonstrates that there are few current deployments in disaster scenarios, with most of the research in the area focusing on the technological aspects of USV hardware and software, such as Guidance Navigation and Control, and not focusing on their actual importance for DM. Finally, to guide future research, this paper also summarizes our own contributions, the lessons learned, guidelines, and research gaps.

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Section: Usv Applications For Preventive Maintenance and Disaster mentioning

confidence: 99%

A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions

Jorge

Granada

Maidana

et al. 2019

Sensors

141

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“…Therefore, in mobile Ad-hoc diver networks (M-ADN), the handover protocol based on visible light communication is proposed to maintain the communication link between the divers in underwater diver network applications [25]. To improve the quality of services in Mobile Ad-hoc diver networks, the existing ad-hoc network technology is used to form a temporary connection in the network without the use of existing infrastructure inside the network [26][27][28]. This mechanism is used in our proposed UHSDM based ADN to support a strong communication link between divers to avoid the risk factors such as rescue delay and link disruption during an emergency.…”

Section: Introductionmentioning

confidence: 99%

Handover Mechanism Based on Underwater Hybrid Software-Defined Modem in Advanced Diver Networks

Raj¹,

Yum²,

Lee³

et al. 2022

Computers, Materials &Amp; Continua

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For the past few decades, the internet of underwater things (IoUT) obtained a lot of attention in mobile aquatic applications such as oceanography, diver network monitoring, unmanned underwater exploration, underwater surveillance, location tracking system, etc. Most of the IoUT applications rely on acoustic medium. The current IoUT applications face difficulty in delivering a reliable communication system due to the various technical limitations of IoUT environment such as low data rate, attenuation, limited bandwidth, limited battery, limited memory, connectivity problem, etc. One of the significant applications of IoUT include monitoring underwater diver networks. In order to perform a reliable and energy-efficient communication system in the underwater diver networks, a smart underwater hybrid softwaredefined modem (UHSDM) for the mobile ad-hoc network was developed that is used for selecting the best channel/medium among acoustic, visible light communication (VLC), and infrared (IR) based on the criteria established within the system. However, due to the mobility of underwater divers, the developed UHSDM meets the challenges such as connectivity errors, frequent link failure, transmission delay caused by re-routing, etc. During emergency, the divers are most at the risk of survival. To deal with diver mobility, connectivity, energy efficiency, and reducing the latency in ADN, a handover mechanism based on pre-built UHSDM is proposed in this paper. This paper focuses on (1) design of UHSDM for ADN (2) propose the channel selection mechanism in UHSDM for selecting the best medium for handover and (3) propose handover protocol in ADN. The implementation result shows that the proposed mechanism can be used to find the new route for divers in advance and the latency can be reduced significantly. Additionally, this paper shows the real field experiment of air tests and underwater tests with various distances. This research will contribute much to the profit of researchers in underwater diver networks and underwater networks, for improving the quality of services (QoS) of underwater applications.

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“…National Research Council of Italy (CNR). 3 Faculty of Electrical Engineering and Computing. University of Zagreb, Croatia.…”

Section: Introductionmentioning

confidence: 99%

CADDY Underwater Stereo-Vision Dataset for Human–Robot Interaction (HRI) in the Context of Diver Activities

Chavez

Ranieri

Chiarella

et al. 2019

JMSE

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In this article we present a novel underwater dataset collected from several field trials within the EU FP7 project "Cognitive autonomous diving buddy (CADDY)", where an Autonomous Underwater Vehicle (AUV) was used to interact with divers and monitor their activities. To our knowledge, this is one of the first efforts to collect a large dataset in underwater environments targeting object classification, segmentation and human pose estimation tasks. The first part of the dataset contains stereo camera recordings (≈ 10K) of divers performing hand gestures to communicate and interact with an AUV in different environmental conditions. These gestures samples serve to test the robustness of object detection and classification algorithms against underwater image distortions i.e., color attenuation and light backscatter. The second part includes stereo footage (≈ 12.7K) of divers free-swimming in front of the AUV, along with synchronized IMUs measurements located throughout the diver's suit (DiverNet) which serve as ground-truth for human pose and tracking methods. In both cases, these rectified images allow investigation of 3D representation and reasoning pipelines from low-texture targets commonly present in underwater scenarios. In this paper we describe our recording platform, sensor calibration procedure plus the data format and the utilities provided to use the dataset.

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DiverNet — A network of inertial sensors for real time diver visualization

Cited by 13 publications

References 13 publications

A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions

A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions

Handover Mechanism Based on Underwater Hybrid Software-Defined Modem in Advanced Diver Networks

CADDY Underwater Stereo-Vision Dataset for Human–Robot Interaction (HRI) in the Context of Diver Activities

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