Quantitative Data Analysis: Small Unmanned Aerial Systems at Hurricane Michael

Murphy, Robin R.; Merrick, David; Adams, Justin; Hart, Laura Kozloski; Broder, Jarrett

doi:10.1109/ssrr.2019.8848935

Cited by 19 publications

(5 citation statements)

References 4 publications

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“…Using robots in urban search and rescue (USAR) has been ongoing for over a decade: at the 9/11 World Trade Center collapse [1]; the 2004 Mid Niigata and 2011 Tohoku earthquakes in Japan [2]; hurricanes Katrina, Wilma, and Rita in 2005 [3], and hurricane Harvey in 2018 [4]. In the wake of these catastrophes, robots were primarily used in reconnaissance missions to assess the disaster environment and search for victims in need of retrieval or rescue [5].…”

Section: Introductionmentioning

confidence: 99%

Towards Human–Robot Teaming: Tradeoffs of Explanation-Based Communication Strategies in a Virtual Search and Rescue Task

Chiou

Demir

Buchanan

et al. 2021

Int J of Soc Robotics

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Autonomous robots have the potential to play a critical role in urban search and rescue (USAR) by allowing human counterparts of a response team to remain in remote, stable locations while the robots execute more dangerous work in the field. However, challenges remain in developing robot capabilities suitable for teaming with humans. Communicating effectively is one of these challenges, especially if plan deviations during field operations require robot explanation. A virtual USAR team task experiment was conducted in Minecraft with a confederate acting as the remote robot. Four explanation-based communication conditions were tested: (1) always explain-the robot automatically provided explanations for any off-plan behaviors, (2) explain if asked-the robot provided an explanation only when the human counterpart requests it, (3) pull prime-the same as (2) but participants also experienced implicit training to pull information from the robot, and (4) never explain-a baseline condition in which the robot acknowledged requests but would not provide an explanation. Results indicate that the training in (3) generated more team communication than (1), but this did not improve team performance or shared situation awareness. Rather, team performance and shared situation awareness was best supported by a moderate level of explanations and the robot pushing information. These findings reinforce the importance of designing robot communication strategies that can reduce human workload, particularly communication overhead, in dynamic and time-constrained tasks.

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Section: Introductionmentioning

confidence: 99%

Towards Human–Robot Teaming: Tradeoffs of Explanation-Based Communication Strategies in a Virtual Search and Rescue Task

Chiou

Demir

Buchanan

et al. 2021

Int J of Soc Robotics

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show abstract

“…During some robot-assisted surgeries, the surgeon controls the robot’s position as it physically probes different locations on an organ ( 69 , 70 ). Similarly, following disasters such as the 2021 landslide in Norway ( 10 ) and Hurricane Harvey ( 7 ), among others ( 8 , 11 , 42 ), the first responders individually piloted the path of each robot, akin to the waypoint control paradigm in our study. By enabling robots to act more autonomously using the strategy proposed here, researchers may be able to improve the cognitive availability and decision-making of surgeons and first responders, whose cognitive performance is critical to human lives.…”

Section: Discussionmentioning

confidence: 99%

“…During waypoint control , the participant provides a path (i.e. a set of waypoints) for each robot to follow ( 8 , 42 ). During user coverage control , the participant can command all three robots at once by providing a region of interest; the robots autonomously coordinate and collectively decide how to explore the user-defined region of interest ( 43 ).…”

Section: Introductionmentioning

confidence: 99%

Collaborative robots can augment human cognition in regret-sensitive tasks

Schlafly,

Prabhakar,

Popovic

et al. 2024

PNAS Nexus

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Despite theoretical benefits of collaborative robots, disappointing outcomes are well-documented by clinical studies, spanning rehabilitation, prostheses, and surgery. Cognitive load theory provides a possible explanation for why humans in the real world are not realizing the benefits of collaborative robots: high cognitive loads may be impeding human performance. Measuring cognitive availability using an electrocardiogram, we ask twenty-five participants to complete a virtual reality task alongside an invisible agent that determines optimal performance by iteratively updating the Bellman equation. Three robots assist by providing environmental information relevant to task performance. By enabling the robots to act more autonomously—managing more of their own behavior with fewer instructions from the human—here we show that robots can augment participants’ cognitive availability and decision-making. The way in which robots describe and achieve their objective can improve the human's cognitive ability to reason about the task and contribute to human-robot collaboration outcomes. Augmenting human cognition provides a path to improve the efficacy of collaborative robots. By demonstrating how robots can improve human cognition, this work paves the way for improving the cognitive capabilities of first responders, manufacturing workers, surgeons, and other future users of collaborative autonomy systems.

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“…Hurricane Michael made landfall near Mexico Beach, Florida, on October 10, 2018, as a category 5 hurricane, one of the powerful and destructive tropical cyclones to strike United States since Andrew in 1992. The dataset was collected on behalf of Florida State Emergency Response Team at Mexico Beach and other directly impacted areas [39] after operating 80 flights conducted between October 11-14, 2018. Unlike typical manned assets which normally fly at 500 feet AGL, the flights were flown at 200 feet AGL.…”

Section: Dataset a Data Collectionmentioning

confidence: 99%

RescueNet: A High Resolution UAV Semantic Segmentation Benchmark Dataset for Natural Disaster Damage Assessment

Chowdhury¹,

Murphy²,

Rahnemoonfar³

2022

Preprint

Self Cite

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Due to climate change, we can observe a recent surge of natural disasters all around the world. These disasters are causing disastrous impact on both nature and human lives. Economic losses are getting greater due to the hurricanes. Quick and prompt response of the rescue teams are crucial in saving human lives and reducing economic cost. Deep learning based computer vision techniques can help in scene understanding, and help rescue teams with precise damage assessment. Semantic segmentation, an active research area in computer vision, can put labels to each pixel of an image, and therefore can be a valuable arsenal in the effort of reducing the impacts of hurricanes. Unfortunately, available datasets for natural disaster damage assessment lack detailed annotation of the affected areas, and therefore do not support the deep learning models in total damage assessment. To this end, we introduce the RescueNet, a high resolution post disaster dataset, for semantic segmentation to assess damages after natural disasters. The RescueNet consists of post disaster images collected after Hurricane Michael. The data is collected using Unmanned Aerial Vehicles (UAVs) from several areas impacted by the hurricane. The uniqueness of the RescueNet comes from the fact that this dataset provides high resolution post-disaster images and comprehensive annotation of each image. While most of the existing dataset offer annotation of only part of the scene, like building, road, or river, RescueNet provides pixel level annotation of all the classes including building, road, pool, tree, debris, and so on. We further analyze the usefulness of the dataset by implementing state-of-the-art segmentation models on the RescueNet. The experiments demonstrate that our dataset can be valuable in further improvement of the existing methodologies for natural disaster damage assessment.

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Quantitative Data Analysis: Small Unmanned Aerial Systems at Hurricane Michael

Cited by 19 publications

References 4 publications

Towards Human–Robot Teaming: Tradeoffs of Explanation-Based Communication Strategies in a Virtual Search and Rescue Task

Towards Human–Robot Teaming: Tradeoffs of Explanation-Based Communication Strategies in a Virtual Search and Rescue Task

Collaborative robots can augment human cognition in regret-sensitive tasks

RescueNet: A High Resolution UAV Semantic Segmentation Benchmark Dataset for Natural Disaster Damage Assessment

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