Enhancing unmanned ground vehicle performance in SAR operations: integrated gesture-control and deep learning framework for optimised victim detection.

Since 2014, the number of migrants and refugees crossing the Mediterranean towards Europe has risen significantly due to various reasons. Both state agencies and non-governmental organizations (NGOs) have launched rescue missions in the Central Mediterranean in accordance with international legal obligations for search and rescue (SAR) operations for those under distress at sea. Our aim is to summarise the specific qualifications needed for maritime SAR in the Mediterranean both in terms of the population at risk, the equipment and the medical support required, especially during the coronavirus disease 2019 (COVID-19) pandemic and the operational legal framework. This article aims to summarise the key points of SAR efforts from a medical perspective as depicted in the relevant literature during a specific timeline period (2014-2020) in a specific part of the Mediterranean Sea (Central Mediterranean route). Only papers published in English and whose full text was available were included in this study. The inclusion criteria were: a) articles referring to sea rescue operations between 2014 and 2020, b) research that focused on medical preparedness and assistance during rescue operations in the Central Mediterranean route, c) studies concerning demographic and clinical features of the rescue population, d) guidelines on the rule of conduct of persons and states participating in rescue activities. The exclusion criteria were: a) studies describing SAR operations in different regions of the world and b) studies focusing on routes, demographics and medical support of migrants/refugees on land. Three major themes were identified: a) characteristics of the population in distress at sea: country of origin, age groups, presence of communicable and non-communicable diseases were identified in the relevant literature. Our research shows that dermatological and respiratory issues were the major concerns among sea migrants, coming from different countries of both Africa and Asia, being relatively young and mostly males; b) medical preparedness and equipment needed for rescue: according to current guidelines, revised during the COVID-19 pandemic, infrastructure needed during SAR operations includes both equipment for resuscitation, personal protective equipment, deck adjustments, medical personnel trained to function in an austere setting and able to handle vulnerable patient groups such as children and pregnant women; c) medico-legal implications of SAR operations: knowledge of the legal framework encompassing SAR operations seems necessary, as European Union and state led initiatives seem to withdraw from proactive SAR, while criminalising NGO led rescue efforts. Operating with the imperative to save lives seems to be the only way of respecting international law and human values, thus, a summary of what the law dictates was made in an effort to keep medical workers participating in such operations updated. Investigation aims to shed light on the special clinical features of sea migrants, the skills, equipment and organizational structure needed by medical workers participating in SAR operations as well as the legal framework under which they will be asked to operate. Special consideration will be given to the difficulties that emerged due to the COVD-19 pandemic.

Who is Being Rescued in Canadian National Parks?

Malaysian search and rescue (SAR) operations play a critical role in ensuring the safety and security of citizens, visitors and assets. Due to the extensive coastline and air traffic, effective SAR operations are essential in protecting lives and properties. The current study aims to improve the effectiveness and efficiency of maritime and aeronautical SAR operations in the Malaysian SAR region by enhancing the collaboration between authorities and developing a solid organisational basis. Human resources, SAR resources, training and logistic support are emphasised. This study employed a quantitative methodology and recruited 140 personnel from SAR agencies. Empirical data were collected through a questionnaire with a simple random technique. Resultantly, training possessed the strongest association with organisational performance. The findings could provide insights into improving operational performance during SAR crises and facilitate strategic decision-making, which would also be instrumental to other government agencies conducting similar research.

When a natural or human disaster occurs, time is critical and often of vital importance. Data from the incident area containing the information to guide search and rescue (SAR) operations and improve intervention effectiveness should be collected as quickly as possible and with the highest accuracy possible. Nowadays, rescuers are assisted by different robots able to fly, climb or crawl, and with different sensors and wireless communication means. However, the heterogeneity of devices and data together with the strong low-delay requirements cause these technologies not yet to be used at their highest potential. Cloud and Edge technologies have shown the capability to offer support to the Internet of Things (IoT), complementing it with additional resources and functionalities. Nonetheless, building a continuum from the IoT to the edge and to the cloud is still an open challenge. SAR operations would benefit strongly from such a continuum. Distributed applications and advanced resource orchestration solutions over the continuum in combination with proper software stacks reaching out to the edge of the network may enhance the response time and effective intervention for SAR operation. The challenges for SAR operations, the technologies, and solutions for the cloud-to-edge-to-IoT continuum will be discussed in this paper.

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.

Maritime Search and Rescue (SAR) operation is a critical process which aims to minimize the loss of life, injury, material damage by rendering aid to persons in distress or imminent danger at sea. Response time to incidents is the most crucial performance metric in SAR operations. Fatalities, injuries and damage can be reduced if incidents are responded as quickly as possible. However, uncertainty of location, time and severity of an incident make these operations difficult to conduct and complicated to plan. Furthermore, quick response relates to rational planning and allocation as well as the use of modern and fast SAR resources. In this paper, we combine optimization and simulation methodologies and study the problem of allocating SAR helicopters. Our methodology works in two stages; we first determine an optimal allocation scheme of helicopters with the objective of minimizing the average response time to incidents in the responsibility region. Next, we use a discrete event simulation model to test the performance of our analytical solution under stochastic demand. Using 2014 incident data of the Aegean Sea, we demonstrate a case study in the western sea region of Turkey.

Multi-UAV cooperative system for search and rescue based on YOLOv5

The need for search and rescue (SAR) operations arises in various situations: natural disasters such as earthquakes, hurricanes, floods or avalanches, accidents, etc. It is known that the large volume and diversity of the identified area, the limited view, dangerous conditions and the presence of victims in remote or hard-to-reach places complicate such work. The use of advanced technologies such as robotics in improving the efficiency of search and rescue operations to solve problems in these cases allows us to obtain rational solutions. A robotic assistant for search and rescue operations has been developed, a 3D model has been developed. The robot is controlled autonomously or remotely, overcome difficult terrain, collect data and perform tasks that contribute to the search and rescue process. Internet of Things devices and sensors interact over the network, allowing you to collect, analyze and share data in real time. The Arduino board was chosen to create the project. The device circuit was assembled and the operation of the sensors was combined. The device used an ultrasonic sensor, a camera, LED lamps and a speaker for the sound of a siren. The robot assistant works in both offline and manual modes. The device is controlled via a web interface, and the video received by the camera is provided in real time. The wheels of the device can move over any terrain, and the device is designed so that it can move on both sides (under and over).

Rescue services in Norway are based on collaboration between private, public, and volunteer sectors. Digital mapping platforms serve as a support tool for those involved in a search and rescue (SAR) operation. However, they lack interoperability and pose practical challenges to the responders.This research is situated in New Materialism, which focuses on interactions between heterogenous material-semiotic actors. Therefore, I analyze digital maps as assemblages constituted through practices. I deconstruct the “black box” of maps: investigate how maps are assembled for SAR operations, zooming in on what these assemblages can offer for efficiency of collaboration during an effort, especially when (dis-)connecting different localities. By doing so, I trace the interactions contributing to or disrupting cross-organizational capacity for collaboration during a SAR effort. The database for this study is formed from 15 semi-structured interviews with 13 people who are related to volunteer services, police, JRCC, and map modeling in Northern Norway, and supplemented by informal conversations, observations, and complementary documents analysis.Findings reveal that digital maps can function as assembling or disassembling platforms for a coordinated rescue action while contributing to information sharing, decision making and situational awareness. This article demonstrates that maps are intrinsic to cross-organizational collaboration, and are interlinked with available infrastructure, training procedures, funding, regulations, and other socio-material aspects. Change in one of these nodes can have inadvertent consequences for the operational capacity of rescue services. Having mapped-out constellations can help trace how they are affected when implementing a change in the use of maps during SAR operations.

Due to their detection capabilities and low cost, unmanned aerial vehicles (UAVs) are commonly used in search and rescue (SAR) operations. In a SAR mission, the UAV's height may change, causing objects to shrink or increase thus affecting generalization. This paper proposes a hybrid object detection (OD) technique that combines altitude-dependent local deep learning (DL) models, each one designed for a given flight altitude range. Seven cutting-edge OD models, including YOLOv4 and v5, EfficientDet, Detectron2, MobileNet, and Faster R-CNN, were trained locally with YOLOv5 and Scaled YOLOv4 being the best performers in low and high-altitude images, respectively. The suggested hybrid strategy, which uses the best OD performers, outperformed well-known DL algorithms with 86.2% mAP on a public dataset. Computing efficiency and accuracy with images of varying resolutions were also explored. Dividing the fundamental detection issue into local subproblems that are treated separately by powerful OD networks might increase SAR capabilities.

Frequent natural disasters and man-made catastrophes have threatened the safety of citizens and have attracted much more attention. The rescue mission under disaster environment is very complicated and dangerous for a rescue team. Search and rescue (SAR) robots can not only improve the efficiency of rescue operations but also reduce the casualty of rescuers. Robots can help rescue teams and even replace rescuers to perform dangerous missions. Search and rescue robots will play a more and more important role in the rescue operations. A survey of the research status of search and rescue robots in Japan, USA, China and other countries has been provided. According to current research, experiences and the lessons learned from applications, the five key performances of a search and rescue robot are survivability, mobility, sensing, communicability and operability. Multi-technique fusion and multi-agent intelligent network are considered to be requirements for the future development of the search and rescue robot. Disaster prevention, disaster reduction and disaster rescue are the important parts of national public safety. They are also crucial for the safety of citizens and their estates. Search and rescue robotic technique is an urgent needed, strategic and core technique for national development. It will be important and strategic for national economy and safety.

It has been observed that the use of UAVs in search and rescue (SAR) operations is very advantageous. When, all of a sudden, a crisis strikes, UAV technology is incredibly helpful and works more effectively to identify the entire region of a disaster and identify victims trapped in the region. The deployment of a UAV network with a high battery lifespan and complete coverage of the disaster region is the primary emphasis of this article. For the efficient communication of UAVS, we suggested the intelligent cluster-based multi-unmanned aerial vehicle (ICBM-UAV) protocol. In order to discover victims swiftly and rescue those who are trapped in the afflicted region as soon as possible, ICBM-UAV uses the clustering technique smartly, which helps conserve drone batteries and performs some of the useful computations within the CH and hence helps to lessen workload on network congestion. Dividing the CMBM-UAV into two parts, the information gathering and the user equipment location identification, improves network life and makes the search and rescue operation more efficient and successful. After going to through vigorous result calculation, it is deduced that the proposed scheme has outperformed the existing state-of-the-art protocols such as AODV, OSLR and flocking mechanisms in terms of throughput, PDR, and coverage area probability by considering each scenario with and without the presence of obstacles. Hence, by delivering an exploitable estimate before reaching the victim, the proposed approach could drastically minimize the search and rescue time to save valuable lives.

Despite there being some researches about unmanned aerial vehicle (UAV), operations with this kind of robots are not yet occurring in search and rescue (SAR) operations. Using adaptive concepts, a Cooperative UAV model applied to search and rescue operations applied is proposed. The adaptive concepts are well suited to the dynamism present in search operations in unknown environment. Thus, applying adaptiveness, each UAV takes decision in order to best accomplish the goal. Simulations were carried out and the results observed show that the model reduces until 75% of the search time (comparing with a manned search using the SAR navigation rules). Furthermore, related works, UAV evolution, conclusion and future works suggestions are presented.

The multifaceted potential of using drones for search and rescue (SAR) is worthy of exploration. There are surely benefits in securing the safety of humans and animals, but there are also needs for careful planning and taking inherent risks and other factors into consideration. There are also ethical discussions that should be raised in ensuring appropriate drone and data use. An evaluation of the current status, with a combination of real-world SAR examples and expert insight into security and privacy concerns, are presented as well as an exploration of concerns about integrating drones into SAR operations.

This study aims to analyze public accountability in the Gorontalo Search and Rescue (SAR) Office across five dimensions: performance, financial, legal, moral, and innovation. These dimensions are theoretically anchored in Bovens’ multidimensional accountability framework, which distinguishes procedural, professional, and outcome-based responsibilities; this grounding clarifies the conceptual meaning and analytical basis of each dimension. Accountability is viewed as a central pillar of institutional legitimacy, especially in crisis settings where public expectations are heightened. A descriptive qualitative approach was employed, supplemented by a structured quantitative scoring procedure. Qualitative indicators derived from interviews, observations, and document analysis were converted into numerical values using predefined rubrics aligned with the accountability framework, allowing transparent and non-arbitrary visualization through radar and bar charts. Findings show that moral accountability received the highest score, reflecting strong humanitarian responsibility, emotional support to victims’ families, and dignified victim handling. Performance accountability also scored well, supported by response speed, procedural compliance, and inter-agency coordination, although resource constraints limited operational effectiveness. Financial and legal accountability demonstrated moderate results, indicating persistent tensions between bureaucratic compliance and operational flexibility. Innovation accountability was the weakest dimension due to limited adoption of advanced technologies such as drones, satellite-based communication, and digital dashboards.Overall, the study indicates that the Gorontalo SAR Office has established a credible accountability foundation but requires strengthened innovation capacity and improved regulatory harmonization. By explicitly grounding the five accountability dimensions in a recognized theoretical framework and employing a transparent qualitative-to-quantitative scoring process, this study offers a more analytically rigorous contribution to understanding accountability within crisis-response institutions in developing countries.