Rescue Tasks Research Articles

Immersive security personnel training module for active shooter events

There is a need to prepare for emergencies such as active shooter events. Emergency response training drills and exercises are necessary to train for such events as we are unable to predict when emergencies do occur. There has been progress in understanding human behavior, unpredictability, human motion synthesis, crowd dynamics, and their relationships with active shooter events, but challenges remain. This paper presents an immersive security personnel training module for active shooter events in an indoor building. We have created an experimental platform for conducting active shooter drills for training that gives a fully immersive feel of the situation and allow one to perform virtual evacuation drills. The security personnel training module also incorporates four sub-modules namely 1) Situational assessment module, 2) Individual officer intervention module, 3) Team Response Module, and 4) Rescue Task Force module. We have developed an immersive virtual reality training module for active shooter events using an Oculus for course of action, visualization, and situational awareness for active shooter events as shown in Fig.1. The immersive security personnel training module aims to get information about the emergency situation inside the building. The dispatched officer will verify the active shooter situation in the building. The security personnel should find a safe zone in the building and secure the people in that area. The security personnel should also find the number and location of persons in possible jeopardy. Upon completion of the initial assessment, the first security personnel shall advise communications and request resources as deemed necessary. This will allow determining whether to take immediate action alone or with another officer or wait until additional resources are available. After successfully gathering the information, the personnel needs to update the info to their officer through a communication device.

Immersive security personnel training module for active shooter events

There is a need to prepare for emergencies such as active shooter events. Emergency response training drills and exercises are necessary to train for such events as we are unable to predict when emergencies do occur. There has been progress in understanding human behavior, unpredictability, human motion synthesis, crowd dynamics, and their relationships with active shooter events, but challenges remain. This paper presents an immersive security personnel training module for active shooter events in an indoor building. We have created an experimental platform for conducting active shooter drills for training that gives a fully immersive feel of the situation and allow one to perform virtual evacuation drills. The security personnel training module also incorporates four sub-modules namely 1) Situational assessment module, 2) Individual officer intervention module, 3) Team Response Module, and 4) Rescue Task Force module. We have developed an immersive virtual reality training module for active shooter events using an Oculus for course of action, visualization, and situational awareness for active shooter events as shown in Fig.1. The immersive security personnel training module aims to get information about the emergency situation inside the building. The dispatched officer will verify the active shooter situation in the building. The security personnel should find a safe zone in the building and secure the people in that area. The security personnel should also find the number and location of persons in possible jeopardy. Upon completion of the initial assessment, the first security personnel shall advise communications and request resources as deemed necessary. This will allow determining whether to take immediate action alone or with another officer or wait until additional resources are available. After successfully gathering the information, the personnel needs to update the info to their officer through a communication device.

A Model for the Assignment of Emergency Rescuers Considering Collaborative Information

Emergency rescue is a critical decision for emergency response, and the assignment of rescuers is crucial to the sustainable development of emergency rescue. Therefore, how to effectively assign rescuers to carry out rescue tasks, so as to achieve the best rescue effect, is a research problem with practical value. In this paper, a model for the assignment of emergency rescuers considering collaborative information is proposed. Firstly, the synergy degrees of rescuers are calculated based on the synergy effect between rescuers and the synergy ability of rescuers. Secondly, according to the evaluation values of the skill level of rescuers, the competence degrees of rescuers are calculated and the overall ability of each rescuer is obtained. Then, the satisfaction degrees of rescuers are calculated according to the subjective preferences of rescuers. Furthermore, the task fitness degrees are obtained, and the satisfaction of rescue time is calculated. Afterwards, a model for assignment of emergency rescuers is constructed with the satisfaction of rescue time and the task fitness degrees maximization as the objectives, and the optimal assignment scheme can be obtained through solving the model. Finally, an illustrative example on the rescuer assignment under public health emergencies is given to illustrate the use of the proposed model.

Integrating Local Motion Planning and Robust Decentralized Fault-Tolerant Tracking Control for Search and Rescue Task of Hybrid UAVs and Biped Robots Team System

Integrating Local Motion Planning and Robust Decentralized Fault-Tolerant Tracking Control for Search and Rescue Task of Hybrid UAVs and Biped Robots Team System

A discrete teaching–learning based optimization algorithm with local search for rescue task allocation and scheduling

The allocation and scheduling of the emergency rescue forces is a fundamental task in emergency management. This paper aims to address the allocation and scheduling problem to minimize the average completion time of all rescue teams by using a discrete teaching–learning based optimization algorithm with local search (DTOLS). First, an improved k-means clustering algorithm with constraints is proposed to assign tasks to rescue teams based on the location of rescue tasks. Second, a hybrid discrete optimization algorithm based on a teaching–learning mechanism is designed to generate the task scheduling sequence for each rescue team as an initial solution. Next, an efficient two-phase local search strategy is presented to improve the current solution. For three neighborhood task moves based on problem characteristics, which contains insert task within a team, swap tasks within a team, insert task between teams, the speed-up techniques are introduced to reduce the computational complexity of calculating completion time of a rescue team. Finally, the parameters of DTOLS are calibrated by Taguchi method to determine appropriate values. DTOLS is compared with the state-of-the-art algorithms, and the experimental results demonstrate the effectiveness of DTOLS in solving a set of test instances.

A Distributed Task Scheduling Method Based on Conflict Prediction for Ad Hoc UAV Swarms

UAV swarms have attracted great attention, and are expected to be used in scenarios, such as search and rescue, that require many urgent jobs to be completed in a minimum time by multiple vehicles. For complex missions with tight constraints, careful assigning tasks is inseparable from the scheduling of these tasks, and multi-task distributed scheduling (MTDS) is required. The Performance Impact (PI) algorithm is an excellent solution for MTDS, but it suffers from the suboptimal solution caused by the heuristics for local task selection, and the deadlock problem that it may fall into an infinite cycle of exchanging the same task. In this paper, we improve the PI algorithm by integrating a new task-removal strategy and a conflict prediction mechanism into the task-removal phase and the task-inclusion phase, respectively. Specifically, the task-removal strategy results in better exploration of the inclusion of more tasks than the original PI by freeing up more space in the local scheduler, improving the suboptimal solution caused by the heuristics for local task selection, as done in PI. In addition, we design a conflict prediction mechanism that simulates adjacent vehicles performing inclusion operations as the criteria for local task inclusion. Therefore, it can reduce the deadlock ratio and iteration times of the MTDS algorithm. Furthermore, by combining the protocol stack with the physical transmission model, an ad-hoc network simulation platform is constructed, which is closer to the real-world network, and serves as the supporting environment for testing the MTDS algorithms. Based on the constructed ad-hoc network simulation platform, we demonstrate the advantage of the proposed algorithm over the original PI algorithm through Monte Carlo simulation of search and rescue tasks. The results show that the proposed algorithm can reduce the average time cost, increase the total allocation number under most random distributions of vehicles-tasks, and significantly reduce the deadlock ratio and the number of iteration rounds.

Exploration of the Impact of Interpersonal Communication and Coordination Dynamics on Team Effectiveness in Human-Machine Teams

Teams composed of human and machine members operating in complex task environments must effectively interact in response to information flow while adapting to environmental changes. This study investigates how interpersonal coordination dynamics between team members are associated with team performance and shared situation awareness in a simulated urban search and rescue (USAR) task. More specifically, this study investigates (1) how communication recurrence affected and reflected coordination dynamics between a USAR robot and human operator when they used different communication strategies, and (2) how these dynamic characteristics of the human–robot interpersonal coordination were associated with the team performance and shared situation awareness. The USAR interpersonal coordination dynamics were systematically characterized using discrete recurrence quantification analysis. Results from this study indicate that (1) teams demonstrating more flexibility in their coordination dynamics were more adaptive to changes in the task environment, and (2) while robot explanations help to improve shared situation awareness, revisiting the same communication pattern (i.e., routine coordination) was associated with better team performance, but did not improve shared situation awareness.

The bookmark method to establish minimum performance standards for intensive care flight paramedics performing helicopter winch rescue

We aimed to use the bookmark method to establish minimum standards for selecting intensive care flight paramedics. Two subject matter expert (SME) focus groups reviewed nine videos of decreasing duration showing land and water helicopter rescue tasks. Focus Group 1 (FG1, n = 10) viewed videos individually off-site, whilst Focus Group 2 (FG2, n = 9) attended a face-to-face session. All SMEs selected the video they judged as the appropriate pace for the task then reviewed feedback. For both groups, the process was repeated until 80% agreement was achieved, or three rounds were completed (whichever occurred first). FG1 and FG2 achieved agreement after two rounds for the land task. For the water task, FG1 did not reach agreement. FG2 reached consensus after two rounds. The selected task durations were similar. The bookmark method is valuable to determine performance standards for performing winch rescue, and SMEs are more likely to reach consensus when face-to-face.

Research on modular scheme of aviation emergency rescue for natural disasters

Aviation emergency rescue is the core part of China's emergency rescue system and the main rescue force, which plays an important role in disaster relief, rescue materials transfer, casualty transportation, air rescue, major health and epidemic prevention. Based on H145 helicopter, the modular design concept is adopted to redesign the aviation rescue equipment, and the modular structure layout of the cabin is completed for major natural disasters. Aiming at the path planning problem of rescue helicopter, based on the improved ant colony algorithm, the optimal path planning of helicopter is completed by comprehensively considering the constraints of helicopter flight performance and flight environment. Through the use of medical data interaction technology, a standardized transport information service platform is established, which solves the problem of medical information island between the transfer unit, the transport unit and the evacuation hospital when the helicopter performs the aviation emergency rescue task, realizes the interconnection of medical data, effectively shortens the response time of aviation emergency rescue and improves the rescue efficiency.

A Mixed-Reality Tele-Operation Method for High-Level Control of a Legged-Manipulator Robot.

In recent years, legged (quadruped) robots have been subject of technological study and continuous development. These robots have a leading role in applications that require high mobility skills in complex terrain, as is the case of Search and Rescue (SAR). These robots stand out for their ability to adapt to different terrains, overcome obstacles and move within unstructured environments. Most of the implementations recently developed are focused on data collecting with sensors, such as lidar or cameras. This work seeks to integrate a 6DoF arm manipulator to the quadruped robot ARTU-R (A1 Rescue Tasks UPM Robot) by Unitree to perform manipulation tasks in SAR environments. The main contribution of this work is focused on the High-level control of the robotic set (Legged + Manipulator) using Mixed-Reality (MR). An optimization phase of the robotic set workspace has been previously developed in Matlab for the implementation, as well as a simulation phase in Gazebo to verify the dynamic functionality of the set in reconstructed environments. The first and second generation of Hololens glasses have been used and contrasted with a conventional interface to develop the MR control part of the proposed method. Manipulations of first aid equipment have been carried out to evaluate the proposed method. The main results show that the proposed method allows better control of the robotic set than conventional interfaces, improving the operator efficiency in performing robotic handling tasks and increasing confidence in decision-making. On the other hand, Hololens 2 showed a better user experience concerning graphics and latency time.

Preliminary Evaluation of a System with On-Body and Aerial Sensors for Monitoring Working Dogs.

This paper presents a system for behavioral, environmental, and physiological monitoring of working dogs using on-body and aerial sensors. The proof of concept study presented here includes two trained dogs performing nine scent detection tasks in an uncontrolled environment encompassing approximately two acres. The dogs were outfitted with a custom designed wearable harness to monitor their heart rate, activity levels and skin temperature. We utilized a commercially available micro-air vehicle to perform aerial sensing by tracking the terrain and movement of the dog in the outdoor space. The dogs were free to explore the space working at maximal speeds to complete a scent-based search-and-retrieval task. Throughout the experiment, the harness data was transferred to a base station via Wi-Fi in real-time. In this work, we also focused on testing the performance of a custom 3D electrode with application specific ergonomic improvements and adaptive filter processing techniques to recover as much electrocardiography data as possible during high intensity motion activity. We were able to recover and use 84% of the collected data where we observed a trend of heart rate generally increasing immediately after successful target localization. For tracking the dogs in the aerial video footage, we applied a state-of-the-art deep learning algorithm designed for online object tracking. Both qualitative and quantitative tracking results are very promising. This study presents an initial effort towards deployment of on-body and aerial sensors to monitor the working dogs and their environments during scent detection and search and rescue tasks in order to ensure their welfare, enable novel dog-machine interfaces, and allow for higher success rate of remote and automated task performance.

Extended belief rule-based method for the assessment of matching between rescue tasks and volunteers

PurposeThis paper aims to solve the major assessment problem in matching the satisfaction of psychological gratification and mission accomplishment pertaining to volunteers with the disaster rescue and recovery tasks.Design/methodology/approachAn extended belief rule-based (EBRB) method is applied with the method's input and output parameters classified based on expert knowledge and data from literature. These parameters include volunteer self-satisfaction, experience, peer-recognition, and cooperation. First, the model parameters are set; then, the parameters are optimized through data envelopment analysis (DEA) and differential evolution (DE) algorithm. Finally, a numerical mountain rescue example and comparative analysis between with-DEA and without-DEA are presented to demonstrate the efficiency of the proposed method. The proposed model is suitable for a two-way matching evaluation between rescue tasks and volunteers.FindingsDisasters are unexpected events in which emergency rescue is crucial to human survival. When a disaster occurs, volunteers provide crucial assistance to official rescue teams. This paper finds that decision-makers have a better understanding of two-sided match objects through bilateral feedback over time. With the changing of the matching preference information between rescue tasks and volunteers, the satisfaction of volunteer's psychological gratification and mission accomplishment are also constantly changing. Therefore, considering matching preference information and satisfaction at two-sided match objects simultaneously is necessary to get reasonable target values of matching results for rescue tasks and volunteers.Originality/valueBased on the authors' novel EBRB method, a matching assessment model is constructed, with two-sided matching of volunteers to rescue tasks. This method will provide matching suggestions in the field of emergency dispatch and contribute to the assessment of emergency plans around the world.

Use of unmanned aviation by State Fire Service (PSP)

This article describes its author’s State Fire Service Officer questionnaire results. The research topic is unmanned aerial vehicle (UAV) usage during service in this formation. The State Fire Service’s statutory tasks are threats reconnaissance and rescue tasks during natural disasters. Some State Fire Service units are equipped with UAVs and use them to their full capabilities. Unmanned systems are increasingly employed by many institutions and the state. Unmanned aerial vehicles can be used in search and rescue operations, waste control, or environmental monitoring. The current possibilities of unmanned aviation are very extensive and often save lives. The numerous impacts of unmanned aviation during the COVID-19 pandemic began with their enormous potential and wide scope of operation. Recognition, effective monitoring and further development of UAVs have a significant impact on improving state security. Research results prove undoubtedly that UAV’s role in State Fire Service is beneficial, especially during missing person search and firefighting actions. Because of the cyclical character of crises in Poland, it is necessary to equip State Fire Service units with tools like UAVs to fight various threats.

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

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

A multi-stage heuristic algorithm based on task grouping for vehicle routing problem with energy constraint in disasters

In recent years, serious disasters have happened frequently, causing significant loss of lives and substantial economic consequences. Considering that rescue vehicles are mainly powered by electricity, they cannot compete with the conventional ones in terms of the cruising range. Therefore, it is of great significance to study how rescue vehicles can execute multiple rescue tasks quickly and efficiently with energy constraint. In this paper, a multi-stage vehicle routing algorithm based on task grouping (MSVR-TG) is proposed for the rescue vehicle routing problem with energy constraint (VRPEC) in disasters. In the task grouping stage, a novel K-means algorithm based on angular density (K-means-ad) is suggested to address the instability of the result of K-means caused by the random selection of initial cluster centers. It can balance the travel distance of each vehicle as much as possible, which is critical when vehicles are energy constrained. In the sequence planning stage, a problem-specific genetic algorithm (PSGA) with multiple population initialization rules and the route improvement strategy is introduced to balance the convergence speed and population diversity, achieving a promising planning result. In the route adjusting stage, various removal and insertion heuristics in the large neighborhood search (LNS) are designed to adjust the routes, thereby further improving the planning result. The routes participating in adjustment are obtained via the developed route selection rule. The validity of each stage in MSVR-TG has been verified in a series of scenarios. A comparison about the proposed algorithm against several advanced algorithms is constructed, and results reveal that MSVR-TG is superior over compared algorithms for VRPEC.

The influence of interdependence and a transparent or explainable communication style on human-robot teamwork.

Humans and robots are increasingly working together in human-robot teams. Teamwork requires communication, especially when interdependence between team members is high. In previous work, we identified a conceptual difference between sharing what you are doing (i.e., being transparent) and why you are doing it (i.e., being explainable). Although the second might sound better, it is important to avoid information overload. Therefore, an online experiment (n = 72) was conducted to study the effect of communication style of a robot (silent, transparent, explainable, or adaptive based on time pressure and relevancy) on human-robot teamwork. We examined the effects of these communication styles on trust in the robot, workload during the task, situation awareness, reliance on the robot, human contribution during the task, human communication frequency, and team performance. Moreover, we included two levels of interdependence between human and robot (high vs. low), since mutual dependency might influence which communication style is best. Participants collaborated with a virtual robot during two simulated search and rescue tasks varying in their level of interdependence. Results confirm that in general robot communication results in more trust in and understanding of the robot, while showing no evidence of a higher workload when the robot communicates or adds explanations to being transparent. Providing explanations, however, did result in more reliance on RescueBot. Furthermore, compared to being silent, only being explainable results a higher situation awareness when interdependence is high. Results further show that being highly interdependent decreases trust, reliance, and team performance while increasing workload and situation awareness. High interdependence also increases human communication if the robot is not silent, human rescue contribution if the robot does not provide explanations, and the strength of the positive association between situation awareness and team performance. From these results, we can conclude that robot communication is crucial for human-robot teamwork, and that important differences exist between being transparent, explainable, or adaptive. Our findings also highlight the fundamental importance of interdependence in studies on explainability in robots.

Volunteer rescue dispatch during the Coronavirus Disease 2019 epidemic: Using the recruitment of volunteers for pneumonia epidemic prevention and control in Chun'an County as an example

To solve the problem of volunteer dispatch during the Coronavirus Disease 2019 (COVID-19) epidemic, a many-to-many two-sided matching volunteer dispatch method based on an improved predator-search algorithm is proposed. First, different evaluation index sets for volunteers and rescue tasks were developed, and weightings were determined using the analytic hierarchy process. Subsequently, the actual and expected values of the different indicators of the two parties were determined, and the triangular fuzzy number was used to calculate the satisfaction of the two parties. Based on this number, we used a linear weighting method to calculate the combined satisfaction and build a many-to-many two-sided matching model according to the demands of both parties. Subsequently, an improved predator-search algorithm was used to solve the model. Finally, taking the recruitment of volunteers for pneumonia epidemic prevention and control in Chun'an County as an example, the method proposed in our study was verified. A comparison and analysis of the results further demonstrated the feasibility and advantages of this method.

An optimization model for rescuer assignments under an uncertain environment by using Dempster–Shafer theory

Emergency decision making and disposal are significant challenges faced by the international community. To minimize the emergency casualties, and reduce probable secondary disasters, it is necessary to immediately dispatch rescuers for emergency rescue in calamity prone areas. The abruptness, destructiveness, and uncertainty of emergencies, the rescue team often faces challenges of pressing time, scattered calamity locations, and diverse tasks. This necessitates the effective organization of rescuers, for their swift dispatch to the areas requiring rescue. A valuable research problem is how to group and dispatch rescuers reasonably and effectively according to the actual needs of the emergency rescue task and the situation to achieve the best rescue effect. This study establishes a dispatch model for rescuers in multiple disaster areas and rescue points. First, this paper combines the Dempster–Shafer theory (DST) and linguistic term set, to propose the concept of an evidential linguistic term set (ELTS), that can flexibly and accurately describe the subjective judgment of emergency decision-makers. It not only lays a theoretical foundation for establishing the rescuers’ dispatch model, but also aids in expressing information in uncertain linguistic environments of decision-making and evaluation. Second, to determine the weight of ability-based rescuer evaluation criteria, this study adopted the evidential best–worst method, combining it with DST to compensate for the limitations of the traditional weightage calculation method in expressing uncertainty. Third, to effectively dispatch rescuers to multiple disaster areas, modeling is carried out based on the above methods to maximize the competence of rescuers and the satisfaction of rescue time, and the best scheme for the allocation of rescuers is determined by solving the model. Finally, the advantages of the constructed model in emergency multitasking group decision-making are demonstrated through an empirical analysis.

Brain-Inspired Affective Empathy Computational Model and Its Application on Altruistic Rescue Task.

Affective empathy is an indispensable ability for humans and other species' harmonious social lives, motivating altruistic behavior, such as consolation and aid-giving. How to build an affective empathy computational model has attracted extensive attention in recent years. Most affective empathy models focus on the recognition and simulation of facial expressions or emotional speech of humans, namely Affective Computing. However, these studies lack the guidance of neural mechanisms of affective empathy. From a neuroscience perspective, affective empathy is formed gradually during the individual development process: experiencing own emotion—forming the corresponding Mirror Neuron System (MNS)—understanding the emotions of others through the mirror mechanism. Inspired by this neural mechanism, we constructed a brain-inspired affective empathy computational model, this model contains two submodels: (1) We designed an Artificial Pain Model inspired by the Free Energy Principle (FEP) to the simulate pain generation process in living organisms. (2) We build an affective empathy spiking neural network (AE-SNN) that simulates the mirror mechanism of MNS and has self-other differentiation ability. We apply the brain-inspired affective empathy computational model to the pain empathy and altruistic rescue task to achieve the rescue of companions by intelligent agents. To the best of our knowledge, our study is the first one to reproduce the emergence process of mirror neurons and anti-mirror neurons in the SNN field. Compared with traditional affective empathy computational models, our model is more biologically plausible, and it provides a new perspective for achieving artificial affective empathy, which has special potential for the social robots field in the future.

Cognitive characteristics in firefighter wayfinding Tasks: An Eye-Tracking analysis

During search and rescue, firefighters need to find paths in an unfamiliar space with minimum time and information available. The effective memorization and retrieval of critical spatial information can help reduce risks and increase mission efficiency. Although evidence has shown that different formats of wayfinding information, including landmarks, routes, and surveys, can impact search and rescue performance in different manners, a deeper understanding of the characteristics of firefighters’ cognitive processes related to the varying wayfinding information formats is less explored. To evaluate firefighters’ performance and cognitive characteristics in search and rescue, a firefighter experiment in Virtual Reality (VR) was conducted. Firefighters (n = 40) were recruited to participate in the simulated rescue task. After reviewing the spatial information in different formats, firefighters were requested to find three victims inside a VR maze as quickly as possible. Task performance was evaluated by the number of victims found and the time spent. Firefighters’ gaze patterns were analyzed to evaluate their cognitive status. The result showed that although the cognitive load under the survey and route conditions was significantly higher than under the landmark condition (p < 0.001), the decision-making involved a more effective cognitive process related to choosing the right path at critical waypoints such as where a turning decision must be made. Thus, the perceived workload and fatigue levels of the two conditions were lower, and the wayfinding performance was better. In contrast, with landmark information, the cognitive load levels were consistently high, along with increased mental fatigue.The findings reveal a series of cognitive features related to a more effective spatial decision-making in search and rescue. In the future, it is expected that these cognitive features can be used to develop real-time monitoring and prediction models for wayfinding performance.