Search results

The duration of an urban search and rescue (USAR) operation directly depends on the number of rescue teams involved. The purpose of this paper is to simplify the earthquake environment and determine the initial number of rescuers in earthquake emergencies in USAR operation.

In the proposed methodology, four primary steps were considered: evaluation of buildings damage and the number of injured people by exerting geospatial information system (GIS) analyses; determining service time by means of task allocation; designing the simulation model (queuing theory); and calculation of survival rate and comparison with the time of rescue operations.

The calculation of buildings damage for an earthquake with 6.6 Richter in Tehran’s District One indicated that 18% of buildings are subjected to the high damage risk. The number of injured people calculated was 28,856. According to the calculated survival rate, rescue operations in the region must be completed within 22.33 h to save 75% of the casualties. Finally, the design of the queue model indicated that at least 2,300 rescue teams were required to provide the calculated survival rate.

The originality of this paper is an innovative approach for determining an appropriate number of rescue teams by considering the queuing theory. The results showed that the integration of GIS and the simulation of queuing theory could be a helpful tool in natural disaster management, especially in terms of rapid vulnerability assessment in urban districts, the adequacy and appropriateness of the emergency services.

A search and rescue incident is ultimately all about the location of the missing person; hence, geotechnical tools are critical in providing assistance to search planners. One critical role of Geographic Information Systems (GISs) is to define the boundaries that define the search area. The literature mostly focuses on ring- and area-based methods but lacks a linear/network approach. The purpose of this paper is to present a novel network approach that will benefit search planners by saving time, requires less data layers and provides better results.

The paper compares two existing models (Ring Model, Travel Time Cost Surface Model (TTCSM)) against a new network model (Travel Time Network Model) by using a case study from a mountainous area in Austria. Newest data from the International Search and Rescue Incident Database are used for all three models. Advantages and disadvantages of each model are evaluated.

Network analyses offer a fruitful alternative to the Ring Model and the TTCSM for estimating search areas, especially for regions with comprehensive trail/road networks. Furthermore, only few basic data are needed for quick calculation.

The paper supports GIS network analyses for wildland search and rescue operations to raise the survival chances of missing persons due to optimizing search area estimation.

The paper demonstrates the value of the novel network approach, which requires fewer GIS layers and less time to generate a solution. Furthermore, the paper provides a comparison between all three potential models.

This study aims to investigate locomotion mechanisms of different urban search and rescue (USAR) robots currently being researched or commercially available on the market.

USAR robots are categorized by the type of their mobility. Detailed illustration and analysis have been given for each USAR robot in the paper.

The paper finds that none of current USAR robots can practically and autonomously carry out rescue work in a complex and unstructured environment. Hence, responding to the practical requirements of highly challenging USAR tasks, a team of USAR robots based on different locomotion mechanisms may be a good solution to undertake rescue activities.

The paper provides guidance in the design of future USAR robots.

The paper investigates locomotion mechanisms of different USAR robots in detail.

The purpose of this paper is to propose a model for a two-agent multi-target-searching scenario in a two-dimensional region, where some places of the region have limited resource capacity in terms of the number of agents that can simultaneously pass through those places and few places of the region are unreachable that expand with time. The proposed cooperative search model and Petri net model facilitate the search operation considering the constraints mentioned in the paper. The Petri net model graphically illustrates different scenarios and helps the agents to validate the strategies.

In this paper, the authors have applied an optimization approach to determine the optimal locations of base stations, a cooperative search model, inclusion–exclusion principle, Cartesian product to optimize the search operation and a Petri net model to validate the search technique.

The proposed approach finds the optimal locations of the base stations in the region. The proposed cooperative search model allows various constraints such as resource capacity, time-dependent unreachable places/obstacles, fuel capacities of the agents, two types of targets assigned to two agents and limited sortie lengths. On the other hand, a Petri net model graphically represents whether collisions/deadlocks between the two agents are possible or not for a particular combination of paths as well as effect of time-dependent unreachable places for different combination of paths are also illustrated.

The problem addressed in this paper is similar to various real-time problems such as rescue operations during/after flood, landslide, earthquake, accident, patrolling in urban areas, international borders, forests, etc. Thus, the proposed model can benefit various organizations and departments such as rescue operation authorities, defense organizations, police departments, etc.

To the best of the authors’ knowledge, the problem addressed in this paper has not been completely explored, and the proposed cooperative search model to conduct the search operation considering the above-mentioned constraints is new. To the best of the authors’ knowledge, no paper has modeled time-dependent unreachable places with the help of Petri net.

Mountaineering and related activities are increasingly becoming popular and are accompanied by an increase in medical incidents. Emergency operations in mountainous terrain are time-critical and often pose major logistical challenges for rescuers. Drones are expected to improve the operational performance of mountain rescuers. However, they are not yet widely used in mountain rescue missions. This paper examines the determinants that drive the behavioral intention of mountain rescuers to adopt drones in rescue missions.

This is a behavioral study that builds upon an extended model of the unified theory of acceptance and use of technology (UTAUT) and investigates the relationship between individual attitudes, perceptions, and intentions for drone adoption. Original survey data of 146 mountain rescuers were analyzed using moderated ordinary least squares (OLS) regression analysis.

Results indicate that the behavioral intention to use drones in mountain rescue missions is driven by the expected performance gains and facilitating conditions. Favorable supporting conditions and experience with drones further moderate the relationship between performance expectancy and behavioral intention. The effects for effort expectancy, social influence, and demonstrations were not significant.

Rescue organizations and stakeholders are recommended to consider the identified determinants in the implementation of drones in emergency logistics. Drone manufacturers targeting mountain rescue organizations are advised to focus on operational performance, provide sufficient support and training, and promote the gathering of practical experience.

A tailored-model that provides first empirical results on the relevance of personal and environmental factors for the acceptance of drones in emergency logistics is presented.

The purpose of this paper is to propose a model for a target searching problem in a two-dimensional region with time constraints. The proposed model facilitates the search operation by minimizing the mission time and fuel usage, and the search operation is performed by a set of agents divided into a number of groups.

The authors have applied optimization techniques, Cartesian product, inclusion–exclusion principle, cooperative strategy, Shapley value, fuzzy Shapley function and Choquet integral to model the problem.

The proposed technique optimizes the placement of base stations that minimizes the sortie length of the agents. The results show that the cooperative strategy outperforms the non-cooperative strategy. The Shapley values quantify the rewards of each group based on their contributions to the search operation, whereas the fuzzy Shapley values determine the rewards of each group based on their contributions and level of cooperation in the search operation.

The proposed model can be applied to model many real-time problems such as patrolling in international borders, urban areas, forests and managing rescue operations after natural calamities, etc. Therefore, defence organizations, police departments and other operation management sectors will be benefitted by applying the proposed approach.

To the best of the authors’ knowledge, determining the optimal locations of base stations in a region is not explored in the existing works on target searching problems with fuel constraints. The proposed approach to cooperatively search the targets in a region is new. Introducing the Shapley function and fuzzy Shapley function is a novel idea to quantify the rewards of each group based on their contributions and level of cooperation in the search operation. This paper addresses these unexplored areas.

This paper seeks to present recent work demonstrating the feasibility of Microbots' mobility in rough terrain. Microbots are a new search and rescue concept based on the deployment of teams of small spherical mobile robots. In this concept, hundreds to thousands of cm‐scale, sub‐kilogram Microbots are released over a search site such as collapsed building rubble or caves. Microbots use hopping, bouncing, and rolling to infiltrate subterranean spaces in search of possible survivors.

The feasibility of the Microbot mobility concept is evaluated through laboratory prototypes and mobility simulations.

Experimental studies have demonstrated the feasibility of using dielectric elastomer actuators (DEAs) to generate autonomous hops. High‐efficiency hydrogen fuel cells were shown to be able to power DEAs. Simulation results show that Microbots of proper diameter and hop height can successfully traverse very rough terrains.

The implication of this research is that small hopping robots are appropriate for certain search and rescue missions. The limitation of the research to date is that issues of control, path planning, and communication have not yet been addressed.

Key technologies of the Microbot mobility, that use high‐energy‐density micro fuel cells combined with low cost and lightweight DEAs, are feasible. These technologies have the potential to make a significant impact on the search and rescue robots.

These results suggest that a team of Microbots‐based DEAs and micro fuel cells can be a useful and effective tool for search and rescue operations.

This paper aims to identify the local information items that are needed by search and rescue (S&R) teams for an effective disaster response following an earthquake. Currently, it is a challenging and time‐consuming task to collect most of this information from a disaster environment. It was envisioned that the local information identified can be stored on distributed databases that are placed in the buildings and will be used to improve S&R operations.

The information items are obtained through a literature review and via interviews conducted with experts from disaster response organizations. The data collected were triangulated to generate a data model, which was then validated internally and externally by expert feedback.

A data model including a detailed list of information items required during S&R operations was generated, along with justification of the information needs. The findings show that not only information related to buildings, but also other information related to the residents and the contents of the buildings are needed, such as residents' health information, and hazardous materials and their specific locations.

The data model presented can be used by researchers to further develop systems that can be used during an earthquake.

Previous studies have only provided a list of some important local information groups to be stored; however, they do not include in‐depth studies on the information needs of S&R teams following an earthquake. In this paper, information needs were fully explored and elaborated, and a data model was developed covering information items required for effective earthquake S&R.