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1. Virtual Telepresence for the Future of ROV Teleoperations: Opportunities and Challenges

   https://doi.org/10.5957/TOS-2022-015  

   Xia, Pengxiang ; McSweeney, Kevin ; Wen, Feng ; Song, Zhuoyuan ; Krieg, Michael ; Li, Shuai ; Yu, Xiao ; Crippen, Kent ; Adams, Jonathan ; Du, Eric Jing ( February 2022 , The SNAME 27th Offshore Symposium)

   Underwater robots, including Remote Operating Vehicles (ROV) and Autonomous Underwater Vehicles (AUV), are currently used to support underwater missions that are either impossible or too risky to be performed by manned systems. In recent years the academia and robotic industry have paved paths for tackling technical challenges for ROV/AUV operations. The level of intelligence of ROV/AUV has increased dramatically because of the recent advances in low-power-consumption embedded computing devices and machine intelligence (e.g., AI). Nonetheless, operating precisely underwater is still extremely challenging to minimize human intervention due to the inherent challenges and uncertainties associated with the underwater environments. Proximity operations, especially those requiring precise manipulation, are still carried out by ROV systems that are fully controlled by a human pilot. A workplace-ready and worker-friendly ROV interface that properly simplifies operator control and increases remote operation confidence is the central challenge for the wide adaptation of ROVs.

   This paper examines the recent advances of virtual telepresence technologies as a solution for lowering the barriers to the human-in-the-loop ROV teleoperation. Virtual telepresence refers to Virtual Reality (VR) related technologies that help a user to feel that they were in a hazardous situation without being present at the actual location. We present a pilot system of using a VR-based sensory simulator to convert ROV sensor data into human-perceivable sensations (e.g., haptics). Building on a cloud server for real-time rendering in VR, a less trained operator could possibly operate a remote ROV thousand miles away without losing the minimum situational awareness. The system is expected to enable an intensive human engagement on ROV teleoperation, augmenting abilities for maneuvering and navigating ROV in unknown and less explored subsea regions and works. This paper also discusses the opportunities and challenges of this technology for ad hoc training, workforce preparation, and safety in the future maritime industry. We expect that lessons learned from our work can help democratize human presence in future subsea engineering works, by accommodating human needs and limitations to lower the entrance barrier.

    

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2. Automatic flower detection and phenology monitoring using time‐lapse cameras and deep learning

   https://doi.org/10.1002/rse2.275  

   Mann, Hjalte M. R. ; Iosifidis, Alexandros ; Jepsen, Jane U. ; Welker, Jeffrey M. ; Loonen, Maarten J. J. E. ; Høye, Toke T. ; Rowcliffe, ed., Marcus ; Liu, ed., Xuehua ( June 2022 , Remote Sensing in Ecology and Conservation)

   The advancement of spring is a widespread biological response to climate change observed across taxa and biomes. However, the species level responses to warming are complex and the underlying mechanisms are difficult to disentangle. This is partly due to a lack of data, which are typically collected by direct observations, and thus very time‐consuming to obtain. Data deficiency is especially pronounced in the Arctic where the warming is particularly severe. We present a method for automated monitoring of flowering phenology of specific plant species at very high temporal resolution through full growing seasons and across geographical regions. The method consists of image‐based monitoring of field plots using near‐surface time‐lapse cameras and subsequent automated detection and counting of flowers in the images using a convolutional neural network. We demonstrate the feasibility of collecting flower phenology data using automatic time‐lapse cameras and show that the temporal resolution of the results surpasses what can be collected by traditional observation methods. We focus on two Arctic species, the mountain avensDryas octopetalaandDryas integrifoliain 20 image series from four sites. Our flower detection model proved capable of detecting flowers of the two species with a remarkable precision of 0.918 (adjusted to 0.966) and a recall of 0.907. Thus, the method can automatically quantify the seasonal dynamics of flower abundance at fine scale and return reliable estimates of traditional phenological variables such as the timing of onset, peak, and end of flowering. We describe the system and compare manual and automatic extraction of flowering phenology data from the images. Our method can be directly applied on sites containing mountain avens using our trained model, or the model could be fine‐tuned to other species. We discuss the potential of automatic image‐based monitoring of flower phenology and how the method can be improved and expanded for future studies.

    

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3. A Dexterous, Glove-Based Teleoperable Low-Power Soft Robotic Arm for Delicate Deep-Sea Biological Exploration

   https://doi.org/10.1038/s41598-018-33138-y  

   Phillips, Brennan T. ; Becker, Kaitlyn P. ; Kurumaya, Shunichi ; Galloway, Kevin C. ; Whittredge, Griffin ; Vogt, Daniel M. ; Teeple, Clark B. ; Rosen, Michelle H. ; Pieribone, Vincent A. ; Gruber, David F. ; et al ( October 2018 , Scientific Reports)

   Modern marine biologists seeking to study or interact with deep-sea organisms are confronted with few options beyond industrial robotic arms, claws, and suction samplers. This limits biological interactions to a subset of “rugged” and mostly immotile fauna. As the deep sea is one of the most biologically diverse and least studied ecosystems on the planet, there is much room for innovation in facilitating delicate interactions with a multitude of organisms. The biodiversity and physiology of shallow marine systems, such as coral reefs, are common study targets due to the easier nature of access; SCUBA diving allows forin situdelicate human interactions. Beyond the range of technical SCUBA (~150 m), the ability to achieve the same level of human dexterity using robotic systems becomes critically important. The deep ocean is navigated primarily by manned submersibles or remotely operated vehicles, which currently offer few options for delicate manipulation. Here we present results in developing a soft robotic manipulator for deep-sea biological sampling. This low-power glove-controlled soft robot was designed with the future marine biologist in mind, where science can be conducted at a comparable or better means than via a human diver and at depths well beyond the limits of SCUBA. The technology relies on compliant materials that are matched with the soft and fragile nature of marine organisms, and uses seawater as the working fluid. Actuators are driven by a custom proportional-control hydraulic engine that requires less than 50 W of electrical power, making it suitable for battery-powered operation. A wearable glove master allows for intuitive control of the arm. The manipulator system has been successfully operated in depths exceeding 2300 m (3500 psi) and has been field-tested onboard a manned submersible and unmanned remotely operated vehicles. The design, development, testing, and field trials of the soft manipulator is placed in context with existing systems and we offer suggestions for future work based on these findings.

    

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4. UNDERSTANDING SPEAKER ROLES ON A NATURALISTIC CORPUS: FEARLESS STEPS APOLLO 11

   Chandra-Shekar, M.M. ; Hansen, J.H.L. ( February 2023 , NASA Human Research Program Investigators Conference)

   INTRODUCTION: Apollo-11 (A-11) was the first manned space mission to successfully bring astronauts to the moon and return them safely. Effective team based communications is required for mission specialists to work collaboratively to learn, engage, and solve complex problems. As part of NASA’s goal in assessing team and mission success, all vital speech communications between these personnel were recorded using the multi-track SoundScriber system onto analog tapes, preserving their contribution in the success of one of the greatest achievements in human history. More than +400 personnel served as mission specialists/support who communicated across 30 audio loops, resulting in +9k hours of data for A-11. To ensure success of this mission, it was necessary for teams to communicate, learn, and address problems in a timely manner. Previous research has found that compatibility of individual personalities within teams is important for effective team collaboration of those individuals. Hence, it is essential to identify each speaker’s role during an Apollo mission and analyze group communications for knowledge exchange and problem solving to achieve a common goal. Assessing and analyzing speaker roles during the mission can allow for exploring engagement analysis for multi-party speaker situations. METHOD: The UTDallas Fearless steps Apollo data is comprised of 19,000 hours (A-11,A-13,A-1) possessing unique and multiple challenges as it is characterized by severe noise and degradation as well as overlap instances over the 30 channels. For our study, we have selected a subset of 100 hours manually transcribed by professional annotators for speaker labels. The 100 hours are obtained from three mission critical events: 1. Lift-Off (25 hours) 2. Lunar-Landing (50 hours) 3. Lunar-Walking (25 hours). Five channels of interest, out of 30 channels were selected with the most speech activity, the primary speakers operating these five channels are command/owners of these channels. For our analysis, we select five speaker roles: Flight Director (FD), Capsule Communicator (CAPCOM), Guidance, Navigation and, Control (GNC), Electrical, environmental, and consumables manager (EECOM), and Network (NTWK). To track and tag individual speakers across our Fearless Steps audio dataset, we use the concept of ‘where’s Waldo’ to identify all instances of our speakers-of-interest across a cluster of other speakers. Also, to understand speaker roles of our speaker-of-interests, we use speaker duration of primary speaker vs secondary speaker and speaker turns as our metrics to determine the role of the speaker and to understand their responsibility during the three critical phases of the mission. This enables a content linking capability as well as provide a pathway to analyzing group engagement, group dynamics of people working together in an enclosed space, psychological effects, and cognitive analysis in such individuals. IMPACT: NASA’s Apollo Program stands as one of the most significant contributions to humankind. This collection opens new research options for recognizing team communication, group dynamics, and human engagement/psychology for future deep space missions. Analyzing team communications to achieve such goals would allow for the formulation of educational and training technologies for assessment of STEM knowledge, task learning, and educational feedback. Also, identifying these personnel can help pay tribute and yield personal recognition to the hundreds of notable engineers and scientist who made this feat possible. ILLUSTRATION: In this work, we propose to illustrate how a pre-trained speech/language network can be used to obtain powerful speaker embeddings needed for speaker diarization. This framework is used to build these learned embeddings to label unique speakers over sustained audio streams. To train and test our system, we will make use of Fearless Steps Apollo corpus, allowing us to effectively leverage a limited label information resource (100 hours of labeled data out of +9000 hours). Furthermore, we use the concept of 'Finding Waldo' to identify key speakers of interest (SOI) throughout the Apollo-11 mission audio across multiple channel audio streams. 

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5. Learning from Earthquakes Using the Automatic Reconnaissance Image Organizer (ARIO)

   S. J. Dyke, X. Liu ( September 2021 , Proceeding of the 17th World Conference on Earthquake Engineering)

   null (Ed.)

   In the aftermath of earthquake events, reconnaissance teams are deployed to gather vast amounts of images, moving quickly to capture perishable data to document the performance of infrastructure before they are destroyed. Learning from such data enables engineers to gain new knowledge about the real-world performance of structures. This new knowledge, extracted from such visual data, is critical to mitigate the risks (e.g., damage and loss of life) associated with our built environment in future events. Currently, this learning process is entirely manual, requiring considerable time and expense. Thus, unfortunately, only a tiny portion of these images are shared, curated, and actually utilized. The power of computers and artificial intelligence enables a new approach to organize and catalog such visual data with minimal manual effort. Here we discuss the development and deployment of an organizational system to automate the analysis of large volumes of post-disaster visual data, images. Our application, named the Automated Reconnaissance Image Organizer (ARIO), allows a field engineer to rapidly and automatically categorize their reconnaissance images. ARIO exploits deep convolutional neural networks and trained classifiers, and yields a structured report combined with useful metadata. Classifiers are trained using our ground-truth visual database that includes over 140,000 images from past earthquake reconnaissance missions to study post-disaster buildings in the field. Here we discuss the novel deployment of the ARIO application within a cloud-based system that we named VISER (Visual Structural Expertise Replicator), a comprehensive cloud-based visual data analytics system with a novel Netflix-inspired technical search capability. Field engineers can exploit this research and our application to search an image repository for visual content. We anticipate that these tools will empower engineers to more rapidly learn new lessons from earthquakes using reconnaissance data. 

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