Browse Topic: Disaster and emergency management
Emergency evacuation slides (EVAC slides) are critical safety devices used on aircraft to enable rapid egress during emergencies. While these slides provide a quick and reliable escape route, communication between separated slides during evacuation remains a challenge. Often, during raft deployment over water, slides may drift apart impeding communication among evacuees and rescue personnel potentially compromising safety. Existing aircraft EVAC systems lack integrated wireless communication relying on visual or voice signals that are unreliable in chaotic conditions. This paper explores the integration of wireless IoT technology into EVAC slide systems to facilitate inter-slide communication and monitor critical parameters such as slide air pressure and the floating weight of stranded passengers through embedded sensors. It proposes the adoption of Long Range (LoRa) modulation technology for wireless communication chosen for its low-power, long-range performance and license-free operation in emergency evacuation scenarios. In addition, the usage of this proposed technology can be further extended to locate the aircraft when other existing locating mechanisms fail.
Soft robot systems demonstrate exceptional load-bearing capacity and spatial compliance during operation, with transformative potential in disaster response scenarios requiring adaptive morphology and hazardous material manipulation. By integrating the complementary advantages of soft robotics and particle jamming mechanisms, this study proposes a real-time variable-stiffness soft actuator, while systematically investigating its mathematical modeling framework and stiffness modulation principles. A deformation model for the variable stiffness soft actuator is established, followed by static analysis of the variable-stiffness members using particle jamming theory, with theoretical investigation of their stress distributions. Subsequently, a variable-stiffness driver was fabricated via additive manufacturing (3D printing), resulting in a flexible mechanical digit capable of stiffness tuning, A soft mechanical hand grasping test platform was built, and grasping experiments of objects of different shapes and sizes were conducted. Experimental validation confirms the influence of actuator dimensions, particle characteristics, and granule size distribution on both stress states and bending angles at the soft robotic digit’s distal segment. The obtained results establish theoretical foundations and advance variable-stiffness soft robotics research and associated stiffness regulation methodologies.
The design, testing, and analysis of a Guided Autorotative Delivery System (GADS) for suppression of incipient wildfires is described. The GADS consists of an unpowered 1 m diameter rotor, a control unit, and a payload of 2.2 kg of fire suppressant powder. On release from a fixed-wing UAV, the rotor passively deploys and enters autorotation, decelerating the payload and allowing precise delivery of the suppressant using cyclic pitch control. A numerical model of the system was developed to calculate the trajectory of the GADS during rotor deployment and descent, in the presence of ambient wind and cyclic pitch inputs. A reduced-scale model of the rotor was tested in a wind tunnel, and an uncontrolled full-scale, 1.5 kg prototype of the GADS was fabricated and tested by dropping from a hovering quadcopter as well as a fixed-wing UAV. The full-scale drop experiments validated the deployment and autorotation stability of the system, and demonstrated that the GADS maintains descent velocities suitable for incipient fire suppression (≈ 5 m/s). Numerical predictions indicate that the GADS descent trajectory can be controlled with cyclic pitch in an ambient crosswind of at least 5 m/s (10 kts). Measurements captured during the drop tests using onboard instrumentation show good qualitative agreement with numerical predictions. Future work will include drop tests with remotely controlled cyclic pitch, followed by fully autonomous controlled descent. The study establishes design guidelines for guided autorotative systems and illustrates their potential for scalable UAV-based wildfire suppression or emergency response.
This paper discusses uncrewed aerial vehicles (UAVs) that can have additional applications beyond their respective civilian, industry, or military applications. The increasing popular electric UAVs in advanced air mobility (AAM) and urban air mobility (UAM) networks can be utilized to increase the efficiency and impact of emergency response in both urban and remote settings. The paper will explore the design considerations and requirements for these dual-use vehicles for specific public good missions, while presenting a survey of additional public good missions that could significantly benefit from additional ready-to-go drones. Additionally, this paper aims to explore the logistics required to implement a system for incorporating civilian, industrial, and military drones into a reserve fleet for emergency and disaster relief efforts.
This paper presents the design, development, and subscale flight testing of an optionally-autonomous lift-plus-cruise (LPC) eVTOL aircraft for emergency response missions that bridges the gap between existing aerial capabilities and the needs of first responders. A 4+1 LPC configuration consisting of four vertical lift propellers and a single pusher propeller was selected to balance hover performance and cruise efficiency. The vehicle is sized around a 600 lbs gross takeoff weight with a 125 lbs payload capacity. VTOL and Pusher propeller blades were optimized using parametric studies, resulting in a high Figure of Merit and propulsive efficiency. Trim analysis demonstrates efficient hover to cruise transition, lift-to-drag ratios of 10-11 between 70-90 knots, and propulsive efficiency exceeding 0.9 at the cruise speed of 100 knots. The subscale configuration utilized a simulation framework for trim and optimization of flight control laws, which were subsequently implemented on a 1/3-scale subscale demonstrator. Subscale flight tests showed stable hover and robust trajectory tracking under wind disturbances throughout the flight envelope, which demonstrates the feasibility of the proposed LPC architecture.
This paper considers the opportunities and challenges of supporting Disaster Relief and Emergency Response (DRER) missions employing new aerial vehicle and systems concepts. This paper is a broad survey of the possible aerial-vehicle-assisted approaches to aid in DRER missions. The intent of this paper is to elevate this DRER mission application domain as a critical area of investigation for rotorcraft, robotics, intelligent systems, and other research. Current work is primarily focused on assessing air space integration challenges for Commercial Off-The-Shelf (COTS) aerial platforms (typically small multirotor drones and/or small fixed-wing uncrewed aerial vehicles (UAVs)) in disasters such as earthquakes and wildfires. Though this is an important area of investigation, truly efficient and effective DRER systems and response efforts will not be possible without the development of novel aircraft, technologies, and system architectures of COTS DRER drones/UAVs. This paper seeks to address that knowledge gap and encourage government, academia, and industry to pursue DRER mission research.
This study introduces a novel in-cabin health monitoring system leveraging Ultra-Wideband (UWB) radar technology for real-time, contactless detection of occupants' vital signs within automotive environments. By capturing micro-movements associated with cardiac and respiratory activities, the system enables continuous monitoring without physical contact, addressing the need for unobtrusive vehicle health assessment. The system architecture integrates edge computing capabilities within the vehicle's head unit, facilitating immediate data processing and reducing latency. Processed data is securely transmitted via HTTPS to a cloud-based backend through an API Gateway, which orchestrates data validation and routing to a machine learning pipeline. This pipeline employs supervised classifiers, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF) to analyze features such as temporal heartbeat variability, respiration rate stability, and heart rate. Empirical evaluations demonstrate the system's proficiency in classifying occupant states, including normal, distressed, and unconscious conditions, achieving high prediction accuracy with low false positive rates. Notably, the system attains sub-10-second detection latency and facilitates end-to-end response actions within a 5-minute window. Experimental deployment in a Mercedes vehicle demonstrated high accuracy in occupancy detection (97%), vital sign monitoring (94%), and full ERS (Emergency Response System) activation within five minutes, meeting Euro NCAP 2025+ Child Presence Detection (CPD) requirements. Furthermore, the cloud infrastructure supports the accumulation of health data, contributing to personalized driver profiles and informed decision-making for future interventions. This research underscores the potential of UWB radar technology in augmenting automotive safety through real-time health monitoring, paving the way for smarter and more secure vehicular environments.
This study examines the issue of frequent traffic accidents leading to congestion and subsequent accidents. Timely investigation and management of these incidents is essential for effectively addressing this problem. This study aims to utilize Unmanned Aerial Vehicle (UAV) technology to improve the efficiency of assessing and investigating traffic accidents. We propose a bi-objective spatial optimization model based on identifying high-risk accident locations. This model combines coverage and median objectives within a service area, taking into account coverage requirements and optimizing site distribution. We also propose a constraint-based process to generate a Pareto frontier to help identify various alternative UAV station location scenarios. The model was validated using real traffic accident data from Nanning City, resulting in a UAV station configuration solution that reduces accident response time and improves assessment efficiency by considering multi-objective trade-offs. This study demonstrates the potential of UAV technology to improve the management and response to traffic accidents.
The wing-in-ground effect (WIG) vehicle represents a significant advancement in aerodynamics and vehicle design, leveraging the ground effect phenomenon to enhance lift and reduce drag when flying close to the surface. This unique capability allows WIG vehicles to achieve higher payloads, longer range, and greater fuel efficiency compared to traditional aircraft, making them an attractive option for modern military and global disaster response applications. Wing-in-Ground Effect Vehicles: From Modern Military and Commercial Development to Global Disaster Response discusses future disaster response, logistics, and military applications for WIG vehicles, including the ongoing development of aerospace and transportation technology. Relavant advancements in materials and propulsion systems holds promise for further enhancing WIG performance and operational range. Additionally, cost-effective and powerful flight computers with various types of mission-enabling sensor suites from the advanced air mobility and drone industry further enable the development of modern, safe, and practical WIG platforms. Click here to access the full SAE EDGETM Research Report portfolio.
Elbit Systems Haifa, Isreal
This study focuses on analyzing the impact of the Francis Scott Key Bridge collapse on traffic flow and the traffic network in Baltimore City. By employing the processing of publicly available datasets, the construction of a traffic network model and a comprehensive scoring method and an improved K-means clustering algorithm based on the idea of the rotational method, the following conclusions have been drawn in this study. First, the bridge collapse significantly changed the distribution of traffic flow. According to the AADT data of 17 key traffic nodes, after the bridge collapse, the AADT of all nodes generally increased except for the nodes closest to the tunnel and bridge. For example, traffic nodes around the city center (e.g., nodes with OSMID numbers 37831627 and 602433660) experienced an increase in AADT, suggesting that traffic flows we Second, the 17 key nodes selected represent the major nodes of the Baltimore City traffic system and provide accurate data to support subsequent traffic optimization. Finally, the redistribution of traffic flows after the bridge collapses exacerbated the traffic bottlenecks on the inner and outer ring highways and tunnels and other roads, especially around the collapsed nodes, where the traffic burden increased significantly, reflecting the tendency of shifting traffic flows to the peripheral areas. The results of the study provide a theoretical basis and data support for the optimization of the traffic system in Baltimore City and emergency response to similar events in the future.
As weather-related catastrophes and urban vulnerabilities intensify, there is a growing interest in AI-driven tools for predicting weather patterns and disaster response. Engineers at Texas A&M University have developed CLARKE (Computer vision and Learning for Analysis of Roads and Key Edifices) — a system that uses drone imagery and artificial intelligence to rapidly assess damage after hurricanes and floods.
The German Aerospace Center's (DLR) solar-powered high altitude platform (HAP) has completed ground vibration testing, in preparation for low altitude flight testing planned for 2026. German Aerospace Center (DLR), Cologne, Germany High-altitude uncrewed aircraft can remain in the lower stratosphere for extended periods, performing a wide range of Earth observation and communications tasks - from monitoring shipping lanes and supporting disaster response to providing internet access. The German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt; DLR) has now taken an important step in the development of its own high-flying solar aircraft by successfully completing a Ground Vibration Test (GVT) on its innovative HAP-alpha high-altitude platform. Extensive ground trials took place at DLR's National Experimental Test Center for Unmanned Aircraft Systems in Cochstedt, Germany. Further tests will follow and the first low-altitude flight trial is planned for 2026, subject to ideal weather conditions. “With HAP-alpha, DLR is demonstrating its comprehensive systems expertise in the complete design, development and operation of a new and innovative aircraft, incorporating all disciplines,” explains DLR Executive Board Member for Aeronautics, Markus Fischer. “This illustrates our engagement in an important field of innovation, to strengthen Germany as a location for technology and business and to open up new perspectives for our public stakeholders in collaboration and knowledge exchange.”
High-altitude uncrewed aircraft can remain in the lower stratosphere for extended periods, performing a wide range of Earth observation and communications tasks – from monitoring shipping lanes and supporting disaster response to providing internet access. The German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt; DLR) has now taken an important step in the development of its own high-flying solar aircraft by successfully completing a Ground Vibration Test (GVT) on its innovative HAP-alpha high-altitude platform. Extensive ground trials took place at DLR’s National Experimental Test Center for Unmanned Aircraft Systems in Cochstedt, Germany. Further tests will follow and the first low-altitude flight trial is planned for 2026, subject to ideal weather conditions.
Dufour Aerospace designs and manufactures an automated tilt-wing aircraft for critical cargo delivery missions. Emphasizing operational efficiency, the platform integrates path generation and tracking techniques tailored for the unique dynamics of tilt-wing flight and builds upon the existing lower level control. While there exist a myriad of methods for high-level aircraft automation ranging from PID to MPC, they often require a trade-off between complexity and the capability to handle non-linear dynamics of the system they are controlling. Hence, a lightweight, deterministic geometric path generation approach using clothoid-based transitions between three waypoints and a robust SO(3)- based path tracking controller adapted for tilt-wing dynamics are presented. Additionally, a high-level automation framework is introduced that includes failure mode handling for GNSS loss and communication breakdowns. This system ensures mission continuity and operational safety while supporting flexible mission planning. The methods are validated through extensive flight testing on both small and large-scale aircraft. The latter prove the scalability, safety, and reliability of the presented solution for tilt-wing aircraft automation and enhance the aircraft's capabilities in real-world emergency response and complex operational scenarios.
A novel multirotor concept is proposed for airlifting the emergency medical personnel without the use of a rescue helicopter (designed for patient transport) during the first line emergency services. Based on this concept, two configurations are designed and introduced, comprising a common quadrotor system with single and dual pusher propellers, respectively. An initial flight performance assessment is conducted for the introduced configurations by means of trim calculations in two distinctive flight modes across the entire designated flight speed range, initially without rotor-rotor interactions, and subsequently, with their inclusion. For this purpose, an existing mid-fidelity rotor-rotor interaction method is extended to capture the interactions in all three directions between the rotors that are arbitrarily positioned and oriented to each other. The trim calculations including rotor-rotor interactions show a 10% increase in the vehicle power at the maximum flight speed. The interactions between the pusher propellers and the lifting rotor wakes result in sudden increases up to 100% in the induced inflow of the propellers at low-speed regimes. The paper presents the two conceptual configurations along with their model syntheses and the results of the trim calculations. The influences of the rotor-rotor interactions on the trim results are discussed both at the rotor and the vehicle level.
In late July to October 2022, residents of the Manu’a Islands in American Samoa felt the earth shake several times a day, raising concerns of an imminent volcanic eruption or tsunami.
SAE J3108 Recommended Practice (RP) provides fuel and hazard guidance for first and second responders of incidents associated with alternative fueled vehicles. The intent of SAE J3108-1 is to present responders with a limited number of intuitive letters and colors. The International community is in the process of adopting International Standards Organization (ISO) 17840, which provides first and second responders with a standardized format for emergency information. While the ISO 17840 format in coloring and lettering can be adopted and should be encouraged when possible, it is intended for large and heavy vehicles. SAE J3108-1 provides a means for responders to recognize fuel and vehicle type on North American light duty vehicles due to size constraints preventing use of ISO 17840 labels.1 While encouraged to be adopted or referenced by vehicle manufacturers, this RP has been developed for the use of States and other Governmental bodies. The RP is not intended to replace the standards for SAE J2990 format emergency response guide (ERG) created by automotive manufacturers for use at the scene of an emergency. This coding should be consistent with other vehicle badging with the goal of providing additional clarity. This RP does not include detailed equipment and procedures such as tools, personal protective equipment, or other aspects required for addressing vehicles which have been involved in accidents or incidents. Other documents providing those aspects are contained in the list of references. This RP is independent of existing U.S. Department of Transportation (DOT) hazardous labeling and nomenclature provided by Code of Federal Regulations Title 49. The ten hazard symbols and four levels of severity rating are insufficient to identify the unique aspects of configurations which may be specific to various vehicles designs. Those symbols are typically not used on the passenger types of vehicles this RP is meant for. This RP also does not supersede current DOT requirements, U.S. Federal Regulations, or other regional regulations for identifying vehicle fuel and critical first responder information.
Recent advancements in electric vertical take-off and landing (eVTOL) aircraft and the broader advanced air mobility (AAM) movement have generated significant interest within and beyond the traditional aviation industry. Many new applications have been identified and are under development, with considerable potential for market growth and exciting potential. However, talent resources are the most critical parameters to make or break the AAM vision, and significantly more talent is needed than the traditional aviation industry is able to currently generate. One possible solution—leverage rapid advancements of artificial intelligence (AI) technology and the gaming industry to help attract, identify, educate, and encourage current and future generations to engage in various aspects of the AAM industry. Beyond Aviation: Embedded Gaming, Artificial Intelligence, Training, and Recruitment for the Advanced Air Mobility Industry discusses how the modern gaming population of 3.3 million individuals could be engaged through embedded AAM-based scenarios and AI-enhanced grading systems for concept creation, engineering, manufacturing, air space design and management, piloting, remote operations, infrastructure planning, vehicle operations. Click here to access the full SAE EDGETM Research Report portfolio.
Artificial Intelligence (AI) has emerged as a transformative force across various industries, revolutionizing processes and enhancing efficiency. In the automotive domain, AI's adaption has ushered in a new era of innovation and driving advancements across manufacturing, safety, and user experience. By leveraging AI technologies, the automotive industry is undergoing a significant transformation that is reshaping the way vehicles are manufactured, operated, and experienced. The benefits of AI-powered vehicles are not limited to their manufacturing, operation, and enhancing the user experience but also by integrating AI-powered vehicles with smart city infrastructure can unlock much more potential of the technology and can offer numerous advantages such as enhanced safety, efficiency, growth, and sustainability. Smart cities aim to create more livable, resilient, and inclusive communities by harnessing innovation through technologies like Internet of Things (IoT), devices, data analytics, and artificial intelligence (AI) and enables data-driven decision-making to meet the evolving needs of urban populations. Integrating AI-powered vehicles with smart city infrastructure can potentially compliment it and can offer numerous advantages like: Traffic Management, Infrastructure Optimization, Parking Optimization, Safety Enhancements, Environmental Sustainability, Emergency Response, various Infrastructure, and business Investment Planning and many more. Moreover, the integration will also have positive impact on environment such as Smart city infrastructure can provide AI-powered vehicles with data on optimal routes and availability of parking slot, resulting in reduced air pollution and energy consumption. In essence, this offers a holistic approach to urban mobility, fostering safer, more efficient, and environmentally sustainable transportation systems. By leveraging advanced technologies and data-driven insights, cities can unlock new opportunities for improving quality of life, enhancing economic competitiveness, and fostering inclusive and resilient communities. In this technical manual, we delve into the futuristic implications of this integration, providing a detailed exploration of the technical aspects and benefits.
Researchers from Stanford and the American University of Beirut have developed a lightweight, portable antenna that can communicate with satellites and devices on the ground, making it easier to coordinate rescue and relief efforts in disaster-prone areas. Stanford University, Stanford, California When an earthquake, flood, or other disaster strikes a region, existing communication infrastructure such as cell phone and radio towers are often damaged or destroyed. Restoring emergency communications as quickly as possible is vital for coordinating rescue and relief efforts. Researchers at Stanford University and the American University of Beirut (AUB) have developed a portable antenna that could be quickly deployed in disaster-prone areas or used to set up communications in underdeveloped regions. The antenna, described recently in Nature Communications, packs down to a small size and can easily shift between two configurations to communicate either with satellites or devices on the ground without using additional power.
When an earthquake, flood, or other disaster strikes a region, existing communication infrastructure such as cell phone and radio towers are often damaged or destroyed. Restoring emergency communications as quickly as possible is vital for coordinating rescue and relief efforts.
After hurricanes and other disasters, it is becoming more common for people to fly drones to record the damage and post videos on social media. Those videos are a resource for rapid damage assessment. By using artificial intelligence, researchers are developing a system that can automatically identify buildings and make an initial determination of whether they are damaged and how serious that damage might be.
The mission of VAERA (VTOL Analysis for Emergency Response Applications) is to enable the design, development, and analysis of emergency response rotorcraft for different disaster scenarios. The project's current focus is on improving crewed and uncrewed rotorcraft for wildfire relief efforts. This paper presents background information on the current state of the art for wildfire-fighting crewed and uncrewed rotorcraft, current wildfire operations, handling and flying qualities considerations of similar vehicles, and the limitations of uncrewed sub-1000 lb commercial off the shelf (COTS) rotorcraft that could be (and sometimes are) used for different wildfire missions. Technology gaps that are currently limiting rotorcraft firefighting capabilities are identified using the background information, and a plan of how to address each of the identified technology gaps is presented. In this paper, the key technology gaps identified for rotorcraft in the wildfire environment include: poor performance and handling/flying qualities, inadequate or nonexistent categorization of handling qualities, unvalidated flight dynamics turbulence modeling approaches, and inadequate subsystems for wildfire missions. While numerous concerns for rotorcraft operating in the wildfire environment exist, this paper focuses on those issues that are either not being addressed by others, or that require more attention. The goals of this paper are to both educate the public on critical technology gaps for wildfirefighting rotorcraft that have not gained significant traction in the public domain, and to explain the work required to address those technology gaps.
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