An official website of the United States government
Search and rescue (SAR) operations are often nearly computer-technology-free due to the fragility and connectivity needs of current information communication technology (ICT). In this design fiction, we envision a world where SAR uses augmented reality (AR) and the surplus labor of volunteers during crisis response efforts. Unmanned aerial vehicles, crowdsourced mapping platforms, and concepts from video game mapping technologies can all be mixed to keep SAR operations complexity-free while incorporating ICTs. Our scenario describes a near-future SAR operation with currently available technology being assembled and deployed without issue. After our scenario, we discuss socio-technical barriers for technology use like technical fragility and overwhelming complexity. We also discuss how to work around those barriers and how to use video games as a testbed for SAR technology. We hope to inspire more resilient ICT design that is accessible without training.
more »
« less
Mapping in the Wild: Toward Designing to Train Search & Rescue Planning
Search and rescue (SAR), performed to locate and save victims in disaster and other scenarios, primarily involves collaborative sensemaking and planning. To become a SAR responder, students learn to search within and navigate the environment, make sense of situations, and collaboratively plan operations. In this study, we synthesize data from four sources: (1) semi-structured interviews with experienced SAR professionals; (2) online surveys of SAR professionals; (3) analysis of documentation and artifacts from SAR operations on the 2017 hurricanes Harvey and Maria; and (4) first-person experience undertaking SAR training. Drawing on activity theory, we develop an understanding of current SAR sensemaking and planning activities, which help explore unforeseen factors that are relevant to the design of training systems. We derive initial design implications for systems that teach SAR responders to deal with mapping in the outdoors, collecting data, sharing information, and collaboratively planning activities.
more »
« less
- PAR ID:
- 10564908
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9781450360180
- Page Range / eLocation ID:
- 137 to 140
- Subject(s) / Keyword(s):
- training disaster response search and rescue maps sensemaking planning fieldwork activity theory
- Format(s):
- Medium: X
- Location:
- Jersey City NJ USA
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)Unmanned aerial vehicles (UAVs), equipped with a variety of sensors, are being used to provide actionable information to augment first responders’ situational awareness in disaster areas for urban search and rescue (SaR) operations. However, existing aerial robots are unable to sense the occluded spaces in collapsed structures, and voids buried in disaster rubble that may contain victims. In this study, we developed a framework, AiRobSim, to simulate an aerial robot to acquire both aboveground and underground information for post-disaster SaR. The integration of UAV, ground-penetrating radar (GPR), and other sensors, such as global navigation satellite system (GNSS), inertial measurement unit (IMU), and cameras, enables the aerial robot to provide a holistic view of the complex urban disaster areas. The robot-collected data can help locate critical spaces under the rubble to save trapped victims. The simulation framework can serve as a virtual training platform for novice users to control and operate the robot before actual deployment. Data streams provided by the platform, which include maneuver commands, robot states and environmental information, have potential to facilitate the understanding of the decision-making process in urban SaR and the training of future intelligent SaR robots.more » « less
-
Abstract Thousands of people are reported lost in the wilderness in the United States every year and locating these missing individuals as rapidly as possible depends on coordinated search and rescue (SAR) operations. As time passes, the search area grows, survival rate decreases, and searchers are faced with an increasingly daunting task of searching large areas in a short amount of time. To optimize the search process, mathematical models of lost person behavior with respect to landscape can be used in conjunction with current SAR practices. In this paper, we introduce an agent-based model of lost person behavior which allows agents to move on known landscapes with behavior defined as independent realizations of a random variable. The behavior random variable selects from a distribution of six known lost person reorientation strategies to simulate the agent’s trajectory. We systematically simulate a range of possible behavior distributions and find a best-fit behavioral profile for a hiker with the International Search and Rescue Incident Database. We validate these results with a leave-one-out analysis. This work represents the first time-discrete model of lost person dynamics validated with data from real SAR incidents and has the potential to improve current methods for wilderness SAR.more » « less
-
Autonomous robotic vehicles (i.e., drones) are potentially transformative for search and rescue (SAR). This paper works toward wearable interfaces, through which humans team with multiple drones. We introduce the Virtual Drone Search Game as a first step in creating a mixed reality simulation for humans to practice drone teaming and SAR techniques. Our goals are to (1) evaluate input modalities for the drones, derived from an iterative narrowing of the design space, (2) improve our mixed reality system for designing input modalities and training operators, and (3) collect data on how participants socially experience the virtual drones with which they work. In our study, 17 participants played the game with two input modalities (Gesture condition, Tap condition) in counterbalanced order. Results indicated that participants performed best with the Gesture condition. Participants found the multiple controls challenging, and future studies might include more training of the devices and game. Participants felt like a team with the drones and found them moderately agentic. In our future work, we will extend this testing to a more externally valid mixed reality game.more » « less
-
Responses in task-oriented dialogue systems often realize multiple propositions whose ultimate form depends on the use of sentence planning and discourse structuring operations. For example a recommendation may consist of an explicitly evaluative utterance e.g. Chanpen Thai is the best option, along with content related by the justification discourse relation, e.g. It has great food and service, that combines multiple propositions into a single phrase. While neural generation methods integrate sentence planning and surface realization in one endto-end learning framework, previous work has not shown that neural generators can: (1) perform common sentence planning and discourse structuring operations; (2) make decisions as to whether to realize content in a single sentence or over multiple sentences; (3) generalize sentence planning and discourse relation operations beyond what was seen in training. We systematically create large training corpora that exhibit particular sentence planning operations and then test neural models to see what they learn. We compare models without explicit latent variables for sentence planning with ones that provide explicit supervision during training. We show that only the models with additional supervision can reproduce sentence planning and discourse operations and generalize to situations unseen in training.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3272973.3274039
