Rescue Scenarios Research Articles

An efficient two-stage evolutionary algorithm for multi-robot task allocation in nuclear accident rescue scenario

With the growing maturity of multi-robot system technology, its applications have expanded across various domains. This paper addresses the critical issue of task allocation in nuclear accident rescue scenario, which plays a pivotal role in the success of such operations. The problem is formulated as a multi-objective optimization problem, taking into account three key indicators: execution time, radiation accumulation, and waiting cost. To effectively tackle this problem, an two-stage evolutionary algorithm is proposed. Firstly, a solution encoding method and a crossover mutation method is devised tailored to the problem’s characteristics. Secondly, a two-stage search strategy is designed. In the first stage, a fixed population size and shift-based density estimation method are used to quickly converge the solution set to the Pareto front. The latter stage uses an infinite size population to find as many Pareto solutions as possible. Finally, a local search strategy is introduced to improve the quality of solution set. In the experimental section, our proposed method is compared with five state-of-the-art algorithms on nine instances of varying scales. Across five evaluation metrics, the proposed algorithm demonstrates competitive performance on all instances. These results underscore the efficacy and competitiveness of our approach in tackling the task allocation problem in multi-robot systems within nuclear accident rescue.

Traffic offloading for postdisaster rescue in UAV‐assisted networks: A coalition formation game approach

SummaryIn postdisaster rescue scenario, unmanned aerial vehicles (UAVs) are effective tools to help ground users in disaster areas to transmit rescue‐critical data to the relief center in time due to their flexible mobilities and fast deployment. However, how UAVs choose disaster areas to take part in traffic offloading with limited bandwidth resources is challenging. This article investigates UAVs' access selection and disaster areas' bandwidth allocation scheme by jointly optimizing network throughput, bandwidth and UAVs' energy cost. The UAVs form coalitions to participate in traffic offloading cooperatively, where each UAV selects a disaster area independently. Specifically, the bandwidth resources are dynamically allocated to all disaster areas when a new coalition partition forms. The UAVs adjust their access choices for disaster areas when the bandwidth allocation changes. When no UAVs migrate to other disaster areas, the network achieves a stable state. The UAVs fly to the final selected disaster area and provide traffic offloading services. To resolve traffic offloading competitions yet enable cooperations among UAVs, we address the UAVs' access selection issue by coalition formation game. A gradient projection method is then proposed to allocate bandwidth resources which maximizes the benefit of the network. We demonstrate that the UAVs' access selection and disaster areas' bandwidth allocation algorithms are convergent. The simulation results demonstrate that our proposed cooperation order is better than the pareto and selfish orders, thereby increasing the benefit of the network.

Design and Implementation of a Camera-Based Tracking System for MAV Using Deep Learning Algorithms

In recent years, the advancement of micro-aerial vehicles has been rapid, leading to their widespread utilization across various domains due to their adaptability and efficiency. This research paper focuses on the development of a camera-based tracking system specifically designed for low-cost drones. The primary objective of this study is to build up a system capable of detecting objects and locating them on a map in real time. Detection and positioning are achieved solely through the utilization of the drone’s camera and sensors. To accomplish this goal, several deep learning algorithms are assessed and adopted because of their suitability with the system. Object detection is based upon a single-shot detector architecture chosen for maximum computation speed, and the tracking is based upon the combination of deep neural-network-based features combined with an efficient sorting strategy. Subsequently, the developed system is evaluated using diverse metrics to determine its performance for detection and tracking. To further validate the approach, the system is employed in the real world to show its possible deployment. For this, two distinct scenarios were chosen to adjust the algorithms and system setup: a search and rescue scenario with user interaction and precise geolocalization of missing objects, and a livestock control scenario, showing the capability of surveying individual members and keeping track of number and area. The results demonstrate that the system is capable of operating in real time, and the evaluation verifies that the implemented system enables precise and reliable determination of detected object positions. The ablation studies prove that object identification through small variations in phenotypes is feasible with our approach.

An advanced assessment framework for seismic resilience of railway continuous girder bridge with multiple spans considering 72h golden rescue requirements

Given the common sense that people in danger could survive with high probability in the golden rescue time of 72 h after earthquakes, railway bridges that are important transportation hubs and the lifelines for earthquake relief have been required to satisfy the necessities of safe passage and fast traffic for emergency response personnel in this critical period. To ensure the post-earthquake temporary functionality of the railway bridges to a sufficient safety level in golden rescue scenario, an advanced resilience assessment framework, including seismic resilience determinations for individual bridge components based on residual responses with respect to the plastic capacities originated from performance limit state definitions, definitions of three recovery levels considering repair efficiency for different recovery phases, and the series-parallel and parallel-serial system resilience models developed in temporal and spatial domains, was established. A commonly constructed irregular railway multi-span continuous girder bridge was chosen to verify the practicability of the aforementioned framework. The finite-element model of the illustrative bridge was developed taking abutment-girder interaction effects and nonlinear seismic behaviors of girder into account. Average spectral acceleration was taken as the intensity measure (IM) to consider the response variations originated from higher modes and natural period extension during earthquakes, and the site-matched and IM-based scaled ground records were adopted in the multiple stripe analysis to obtain instantaneous peak and residual seismic responses. Based on the framework, post-event resilience characteristics of bridge components were discussed, four recovery strategies were proposed for bridge system, and the seismic resilience of the illustrated bridge was assessed at system level. The research shows that the response contribution of fundamental dominant mode is only 64.37 %, indicating the dynamic response contributions from higher modes are considerable and the case bridge can show a strong irregularity during seismic events. Obvious nonlinear seismic behaviors occur to the superstructure where material strain in girder critical sections can increase to 0.00422 during earthquakes, which illustrates the inaccuracy of girder modeling only taking elastic behavior into consideration. Also, the prestress loss after earthquakes can reach 31.14 % and attention should be paid to the prestress recovery in subsequent repair works. Most components can satisfy the easy recovery resilience level, and the resilience is closely related to ground intensities and repair-waiting time; earthquake-induced functionality reduction can be 46.57 %, and resilience can increase by 25.35 % if the waiting time is shortened by 5 h. System resilience is influenced by the similar factors with component and varies in cases of different repair sequences due to the accumulation effect of repair-waiting time for various subsystems.

Ripple spreading algorithm: a new method for solving multi-objective shortest path problems with mixed time windows

In emergency management, the transportation scheduling of emergency supplies and relief personnel can be regarded as the multi-objective shortest path problem with mixed time window (MOSPPMTW), which has high requirements for timeliness and effectiveness, but the current solution algorithms cannot simultaneously take into account the solution accuracy and computational speed, which is very unfavorable for emergency path decision-making. In this paper, we establish MOSPPMTW matching emergency rescue scenarios, which simultaneously enables the supplies and rescuers to arrive at the emergency scene as soon as possible in the shortest time and at the smallest cost. To solve the complete Pareto optimal surface, we present a ripple spreading algorithm (RSA), which determines the complete Pareto frontier by performing a ripple relay race to obtain the set of Pareto optimal path solutions. The proposed RSA algorithm does not require an initial solution and iterative iterations and only needs to be run once to obtain the solution set. Furthermore, we prove the optimality and time complexity of RSA and conduct multiple sets of example simulation experiments. Compared with other algorithms, RSA performs better in terms of computational speed and solution quality. The advantage is especially more obvious in the computation of large-scale problems. It is applicable to various emergency disaster relief scenarios and can meet the requirements of fast response and timeliness.

Obstacle avoidance USV in multi-static obstacle environments based on a deep reinforcement learning approach

Unmanned surface vehicles (USVs) are intelligent platforms for unmanned surface navigation based on artificial intelligence, motion control, environmental awareness, and other professional technologies. Obstacle avoidance is an important part of its autonomous navigation. Although the USV works in the water environment (e.g. monitoring and tracking, search and rescue scenarios), the dynamic and complex operating environment makes the traditional methods not suitable for solving the obstacle avoidance problem of the USV. In this paper, to address the issue of poor convergence of the Twin Delayed Deep Deterministic policy gradient (TD3) algorithm of Deep Reinforcement Learning (DRL) in an unstructured environment and wave current interference, random walk policy is proposed to deposit the pre-exploration policy of the algorithm into the experience pool to accelerate the convergence of the algorithm and thus achieve USV obstacle avoidance, which can achieve collision-free navigation from any start point to a given end point in a dynamic and complex environment without offline trajectory and track point generation. We design a pre-exploration policy for the environment and a virtual simulation environment for training and testing the algorithm and give the reward function and training method. The simulation results show that our proposed algorithm is more manageable to converge than the original algorithm and can perform better in complex environments in terms of obstacle avoidance behavior, reflecting the algorithm’s feasibility and effectiveness.

Using Affect as a Communication Modality to Improve Human-Robot Communication in Robot-Assisted Search and Rescue Scenarios

Emotions can provide a natural communication modality to complement the existing multi-modal capabilities of social robots, such as text and speech, in many domains. We conducted three online studies with 112, 223, and 151 participants, respectively, to investigate the benefits of using emotions as a communication modality for Search And Rescue (SAR) robots. In the first experiment, we investigated the feasibility of conveying information related to SAR situations through robots' emotions, resulting in mappings from SAR situations to emotions. The second study used Affect Control Theory as an alternative method for deriving such mappings. This method is more flexible, e.g. allows for such mappings to be adjusted for different emotion sets and different robots. In the third experiment, we created affective expressions for an appearance-constrained outdoor field research robot using LEDs as an expressive channel. Using these affective expressions in a variety of simulated SAR situations, we evaluated the effect of these expressions on participants' (in the role rescue workers) situational awareness. Our results and proposed methodologies (a) provide insights on how emotions could help conveying messages in the context of SAR, and (b) show evidence on the effectiveness of adding emotions as a communication modality in a (simulated) SAR communication context.

Incremental Audio-Visual Fusion for Person Recognition in Earthquake Scene

Earthquakes have a profound impact on social harmony and property, resulting in damage to buildings and infrastructure. Effective earthquake rescue efforts require rapid and accurate determination of whether any survivors are trapped in the rubble of collapsed buildings. While deep learning algorithms can enhance the speed of rescue operations using single-modal data (either visual or audio), they are confronted with two primary challenges: insufficient information provided by single-modal data and catastrophic forgetting. In particular, the complexity of earthquake scenes means that single-modal features may not provide adequate information. Additionally, catastrophic forgetting occurs when the model loses the information learned in a previous task after training on subsequent tasks, due to non-stationary data distributions in changing earthquake scenes. To address these challenges, we propose an innovative approach that utilizes an incremental audio-visual fusion model for person recognition in earthquake rescue scenarios. Firstly, we leverage a cross-modal hybrid attention network to capture discriminative temporal context embedding, which uses self-attention and cross-modal attention mechanisms to combine multi-modality information, enhancing the accuracy and reliability of person recognition. Secondly, an incremental learning model is proposed to overcome catastrophic forgetting, which includes elastic weight consolidation and feature replay modules. Specifically, the elastic weight consolidation module slows down learning on certain weights based on their importance to previously learned tasks. The feature replay module reviews the learned knowledge by reusing the features conserved from the previous task, thus preventing catastrophic forgetting in dynamic environments. To validate the proposed algorithm, we collected the Audio-Visual Earthquake Person Recognition (AVEPR) dataset from earthquake films and real scenes. Furthermore, the proposed method gets 85.41% accuracy while learning the 10th new task, which demonstrates the effectiveness of the proposed method and highlights its potential to significantly improve earthquake rescue efforts.

Auction-Based Task Allocation and Motion Planning for Multi-Robot Systems with Human Supervision

This paper presents a task allocation strategy for a multi-robot system with a human supervisor. The multi-robot system consists of a team of heterogeneous robots with different capabilities that operate in a dynamic scenario that can change in the robots’ capabilities or in the operational requirements. The human supervisor can intervene in the operation scenario by approving the final plan before its execution or forcing a robot to execute a specific task. The proposed task allocation strategy leverages an auction-based method in combination with a sampling-based multi-goal motion planning. The latter is used to evaluate the costs of execution of tasks based on realistic features of paths. The proposed architecture enables the allocation of tasks accounting for priorities and precedence constraints, as well as the quick re-allocation of tasks after a dynamic perturbation occurs –a crucial feature when the human supervisor preempts the outcome of the algorithm and makes manual adjustments. An extensive simulation campaign in a rescue scenario validates our approach in dynamic scenarios comprising a sensor failure of a robot, a total failure of a robot, and a human-driven re-allocation. We highlight the benefits of the proposed multi-goal strategy by comparing it with single-goal motion planning strategies at the state of the art. Finally, we provide evidence for the system efficiency by demonstrating the powerful synergistic combination of the auction-based allocation and the multi-goal motion planning approach.

Application of raycast method for person geolocalization and distance determination using UAV images in Real-World land search and rescue scenarios

People enjoy spending time in the wilderness for numerous reasons. However, they occasionally get lost or injured, and their survival depends on being efficiently found and rescued in the shortest possible time. A search and rescue operation (SAR) is launched after the accident is reported, and all possible resources are activated. The inclusion of drones in SAR operations has enabled the use of computer vision methods to detect persons in aerial imagery automatically. When searching by drone, preference is given to oblique photographs that cover a larger area within a single image, reducing the search time. Unlike vertical photographs, oblique photographs include a significant scale change, making it challenging to locate a person in the real world and determine their distance from the drone. In order to solve this problem, encouraged by our previous successful simulations, we explored the possibility of applying the raycast method for person geolocalization and distance determination for use in real-world scenarios. In this paper, we propose a system able to precisely geolocate persons automatically detected in offline processed images recorded during the SAR mission. After a series of experiments on terrains of different configurations and complexity, using a custom-made 3D terrain generator and raycaster, along with a deep neural network-based person detector trained on our custom dataset, we defined a method for geolocating detected person based on raycast, which allows using low-cost commercial drones with a monocular camera and no Real-Time Kinematic module while enabling laser rangefinder emulation during offline image analysis. Our person geolocating method overcomes the problems faced by previous methods and, using a single flight sequence with only 4 consecutive detections, significantly outperforms the previous best results, with reliability of 42,85% (geolocating error of 0.7 m on recording from a 30 m height). Also, a short time of only 247 s enables offline processing of data recorded during a 21-minute drone flight covering approximately an area of 10 ha, proving that the proposed method can be effectively used in actual SAR missions. We also proposed a new evaluation metric (ErrDist) for person geolocalization and provided recommendations for using the proposed system for person detection and geolocation in real-world scenarios.

Touchless Heart Rate Monitoring from an Unmanned Aerial Vehicle Using Videoplethysmography.

Motivation: The advancement of preventive medicine and, subsequently, telemedicine drives the need for noninvasive and remote measurements in patients' natural environments. Heart rate (HR) measurements are particularly promising and extensively researched due to their quick assessment and comprehensive representation of patients' conditions. However, in scenarios such as endurance training or emergencies, where HR measurement was not anticipated and direct access to victims is limited, no method enables obtaining HR results that are suitable even for triage. Methods: This paper presents the possibility of remotely measuring of human HR from a series of in-flight videos using videoplethysmography (VPG) along with skin detection, human pose estimation and image stabilization methods. An unmanned aerial vehicle (UAV) equipped with a camera captured ten segments of video footage featuring volunteers engaged in free walking and running activities in natural sunlight. The human pose was determined using the OpenPose algorithm, and subsequently, skin areas on the face and forearms were identified and tracked in consecutive frames. Ultimately, HR was estimated using several VPG methods: the green channel (G), green-red difference (GR), excess green (ExG), independent component analysis (ICA), and a plane orthogonal to the skin (POS). Results: When compared to simultaneous readings from a reference ECG-based wearable recorder, the root-mean-squared error ranged from 17.7 (G) to 27.7 (POS), with errors of less than 3.5 bpm achieved for the G and GR methods. Conclusions: These results demonstrate the acceptable accuracy of touchless human pulse measurement with the accompanying UAV-mounted camera. The method bridges the gap between HR-transmitting wearables and emergency HR recorders, and it has the potential to be advantageous in training or rescue scenarios in mountain, water, disaster, or battlefield settings.

Evaluation of live birth rates and perinatal outcomes following two sequential vitrification/warming events at the zygote and blastocyst stages.

To study the outcome of sequential cryopreservation-thawing of zygotes followed by the cryopreservation-thawing of blastocysts in the course of an IVF treatment on live birth rate and neonatal parameters. Single center, retrospective chart review for the time period of 2015-2020. Clinical and perinatal outcomes were compared between frozen embryo transfer cycles utilizing twice-cryopreserved (n = 182) vs. once-cryopreserved (n = 282) embryos. Univariate and multivariable analyses were used to adjust for relevant confounders. After adjustment for maternal age, gravidity, parity, body mass index (BMI), paternal age, fertilization method used, the number of oocytes retrieved in the fresh cycle, fertilization rate, and transfer medium, the transfer of twice-cryopreserved embryos resulted in a reduced probability of live birth (OR, 0.52; 95% CI 0.27-0.97; p=0.041) compared to once-cryopreserved embryos. No differences in the sex ratio, the mean gestational age, the mean length at birth, or the mean birth weight were found between the two groups. The circumstantial use of sequential double vitrification-warming in course of treatment is associated with a reduced (but still reasonable) live birth rate compared to once-cryopreserved embryos. As the neonatal outcomes of twice-cryopreserved embryos are similar to once-cryopreserved embryos, this treatment option appears still valid as a rescue scenario in selected cases.

The Counterfactual Argument Against Abortion

AbstractIn this article, I present a novel argument against abortion. In short, what makes it wrong to kill someone is that they are a counterfactual person; counterfactual persons are individuals such that, were they not killed, they would have been persons. My view accommodates two intuitions which many views concerning the wrongness of killing fail to account for: embryo rescue cases and the impermissibility of infanticide. The view avoids embryo rescue cases because embryos in the rescue scenarios are not counterfactual people: they are not counterfactual people because it is false to say that, were they not killed, they would have been persons. As a result, it does not follow from my account that there is a prohibition against allowing embryos to die. On the other hand, infants are counterfactual people: an infant is an individual such that, were she not killed, she would have been a person.

REF: A Rapid Exploration Framework for Deploying Autonomous MAVs in Unknown Environments

Exploration and mapping of unknown environments is a fundamental task in applications for autonomous robots. In this article, we present a complete framework for deploying Micro Aerial Vehicles (MAVs) in autonomous exploration missions in unknown subterranean areas. The main motive of exploration algorithms is to depict the next best frontier for the MAV such that new ground can be covered in a fast, safe yet efficient manner. The proposed framework uses a novel frontier selection method that also contributes to the safe navigation of autonomous MAVs in obstructed areas such as subterranean caves, mines, and urban areas. The framework presented in this work bifurcates the exploration problem in local and global exploration. The proposed exploration framework is also adaptable according to computational resources available onboard the MAV which means the trade-off between the speed of exploration and the quality of the map can be made. Such capability allows the proposed framework to be deployed in subterranean exploration and mapping as well as in fast search and rescue scenarios. The performance of the proposed framework is evaluated in detailed simulation studies with comparisons made against a high-level exploration-planning framework developed for the DARPA Sub-T challenge as it will be presented in this article.

An efficient intelligent energy management strategy based on deep reinforcement learning for hybrid electric flying car

Hybrid electric flying cars hold clear potential to support high mobility and environmentally friendly transportation. For hybrid electric flying cars, overall performance and efficiency highly depend on the coordination of the electrical and fuel systems under ground and air dual-mode. However, the huge differences in the scale and fluctuation characteristics of energy demand between ground driving and air flight modes make the efficient control of energy flow more complex. Thus, designing a power coordinated control strategy for hybrid electric flying cars is a challenging technical problem. This paper proposed a deep reinforcement learning-based energy management strategy (EMS) for a series hybrid electric flying car. A mathematical model of the series hybrid electric flying car driven by the distributed hybrid electric propulsion system (HEPS) which mainly consists of battery packs, twin turboshaft engine and generator sets (TGSs), 16 rotor-motors, and 4 wheel-motors is established. Subsequently, a Double Deep Q Network (DDQN)-based EMS considering ground and air dual driving mode is proposed. A simplified method for the number of control variables is designed to improve exploration efficiency and accelerate the convergence speed. In addition, the frequent engine on/off problem is also taken into account. Finally, DDQN-based and dynamic programming (DP)-based EMSs are applied to investigate the power flow distribution for two completely different hypothetical driving scenarios, namely search and rescue (SAR) scenarios and urban air mobility (UAM) scenarios. The results demonstrate the effectiveness of the DDQN-based EMS and its capacity of reducing the computation time.

TaskPOI Priority-Based Energy Balanced Multi-UAVs Cooperative Trajectory Planning Algorithm in 6G Networks

In the emergency rescue scenario where the communication infrastructure is damaged, Unmanned Air Vehicle(UAV) networking communication, as a part of the 6G air-space-ground integrated network system, plays a huge role in the establishment of the emergency communication network. In this paper, we fully consider the distribution of rescue areas, the type of tasks and the characteristics of UAVs, and design the TaskPOI-based multi-UAVs collaborative trajectory planning algorithm(TMCTP) based on regional tasks, aiming to improve the completed efficiency of rescue tasks and the utilization rate of energy consumption in the UAV backbone network. Firstly, we filter the population-intensive points of interest(PiPOI) region based on the location heat map of population density distribution and use the K-means clustering algorithm to generate task points of interest(TaskPOI) and use the neural collaborative filtering algorithm to determine the task priority and different priorities correspond to different task types. Then, we design a single UAV flight model and a multi-UAVs cooperative flight model. At the same time, considering the flight energy consumption, communication energy consumption and data acquisition energy consumption of UAV, the energy consumption equilibrium distribution model is designed to ensure that multi-UAVs have low energy consumption and balance the residual energy distribution while completing various levels of tasks. Finally, based on UAV flight model, energy consumption equilibrium distribution model, and combined with task priority of TaskPOI, TMCTP algorithm for the trajectory planning of the UAV group is designed by using multi-agent reinforcement learning algorithm. The experimental results show that in the TMCTP algorithm, the success rate of the UAV arriving at the goal and the completed rate of the action are converged to 1, the times of collisions is converged to 0, and the average reward, the loss of algorithm are all converged to a good degree, and the PiPOI coverage rate in the area exceeds 60%. In terms of energy consumption, the difference between the energy consumption of each UAV is small, and compared with the average residual energy using GP3, the difference is not large, indicating that the algorithm can improve the PiPOI coverage of the entire rescue area without generating a large energy consumption. In this way, the UAV group after arriving at the goal will provide communication services for the rescue area and improve the connectivity and life cycle of the 6G air network.

Physiological and cognitive responses to hyperoxic exercise in full water submersion

ABSTRACT The positive effects of combined hyperoxia and physical exercise on physiological parameters and cognitive functioning are established for normobaric laboratory contexts. Still, increased practicability exists in hyperbaric settings like underwater activities and SCUBA diving, where environmental and sport-specific factors might moderate effects. Improved cognition, reduced ventilation (V̇E), and lower blood lactate concentrations [Lac-] are highly relevant, especially during high-stress and rescue scenarios. Fifteen participants performed 3 × 8 min of continuous underwater fin-swimming at 25 % (low), 45 % (moderate), and 75 % (vigorous) heart rate reserve (HRR) in each test. Three separate test days differed solely by the inspiratory oxygen partial pressure (PIO2: 29 kPa, 56 kPa, and 140 kPa). V̇E was measured continuously, whereas breathing gas analysis, blood sampling, and Eriksen Flanker tasks for inhibitory control (100 stimuli) were performed post-exercise. Two-way ANOVAs with repeated measures on the factors PIO2 and exercise intensity analyzed physiological outcome variables and reactions times (RT) and accuracy (ACC) of inhibitory control. V̇E was significantly reduced for 140 kPa during moderate and vigorous and for 56 kPa during vigorous compared to 29 kPa. 56 kPa and 140 kPa showed no differences. [Lac-], post-exercise V̇CO2, and velocity were unaffected by PIO2. Faster RTs but lower ACC of inhibitory control were observed following exercise at 75 % HRR compared to rest, 25 %, and 45 % HRR, while PIO2 produced no effects. Underwater performance in hyperoxia presents reduced V̇E, possible by dampened chemoreceptor sensitivity, and effects on cognition that differ from laboratory results and emphasise the moderating role of sport-specific factors.

Don't Shoot Me: Potential Consequences of Force-on-Force Training Modulate the Human Stress Response.

Jensen, AE, Bernards, JR, Hamilton, JA, Markwald, RR, Kelly, KR, and Biggs, AT. Do not shoot me: potential consequences of force-on-force training modulate the human stress response. J Strength Cond Res 37(9): 1761-1769, 2023-Close-quarters combat (CQC) engagements trigger the "fight-or-flight" response, activating the sympathetic nervous system and hypothalamic-pituitary-adrenal axis in response to perceived threats. However, it has yet to be shown if a force-on-force (FoF) CQC training environment will lead to adaptations in the physiological stress response or performance. United States Marines and Army infantry personnel participated in a 15-day CQC training program. The CQC program focused heavily on FoF training with the use of nonlethal training ammunition (NLTA). Data collections occurred on training days 1 and 15, during a simulated FoF-hostage rescue (HR) scenario and photorealistic target drill. For the FoF-HR, subjects were instructed to clear the shoot house, rescue the hostage, and only shoot hostile threat(s) with NLTA. The photorealistic target drills were similar, but replaced the role players in the FoF-HR with paper targets. Salivary alpha-amylase (sAA) and salivary cortisol were obtained immediately before entering and exiting the shoot house. Time to completion significantly decreased, between days 1 and 15, for both the FoF-HR and the photorealistic drills by 67.7 and 54.4%, respectively ( p < 0.05). Analyses revealed that the change in sAA, nonsignificantly, doubled from day 1 to 15 during FoF-HR ( p > 0.05), whereas the change in sAA decreased during the photorealistic drills across days ( p < 0.05). Cortisol was significantly higher during the FoF-HR in comparison to the photorealistic drills ( p < 0.05). These data suggest that potential consequences of FoF training heighten the stress response in conjunction with enhanced performance.

Construction of FANETs for user coverage and information transmission in disaster rescue scenarios

In this paper, we consider a flying ad hoc network (FANET) construction problem in a post-disaster scenario where ground users (GUs) lose contact with the outside world. The main objective of the FANET is to provide communication coverage for as many as GUs, and to transmit disaster information back to the rescue center as soon as possible. To solve this FANET construction problem, we first propose a FANET construction algorithm based on particle swarm optimization (PSO) algorithm. To improve the performance of the PSO-based FANET construction algorithm, we further model the FANET construction problem as an ordinary potential game (OPG), and propose a better-response-based FANET construction algorithm. Several simulation experiments are presented to evaluate the effectiveness of these proposed two algorithms. Simulation results demonstrate that both the PSO-based FANET construction algorithm and the better-response-based FANET construction algorithm can be used to maximize the coverage rate of GUs and minimize the average transmission delay from rescue UAVs to the gateway UAV. Moreover, the better-response-based FANET construction algorithm has a fast convergence rate, and performs better than the PSO-based FANET construction algorithm and other baselines in terms of coverage rate and average transmission delay.

Improved Particle Swarm Path Planning Algorithm with Multi-Factor Coupling in Forest Fire Spread Scenarios

In this paper, a solution based on an improved particle swarm algorithm is proposed for the path planning problem without a road network in forest fire rescue scenarios. The algorithm adopts an adaptive inertia weight and a dynamically updated learning factor strategy to enhance the global and local search capabilities of the algorithm. In terms of cost function design, the article considers three factors: path length, terrain slope, and obstacle avoidance ability to ensure the safety and effectiveness of the path. The experimental results show that: (1) the path planning algorithm based on improved particle swarm optimization can effectively avoid spreading wildfire and reach the designated target point with a good “detour” effect; (2) the path planned by the improved PSO algorithm performs better than the original PSO algorithm in terms of fitness evaluation and average slope; and (3) changes in the particle population, dimensions, and learning factors in the particle swarm optimization algorithm can affect the convergence of the final path. Increasing the particle dimensions can bring more reasonable and specific paths; decreasing the learning factor increases the convergence iterations, but also obtains a better path planning solution and higher fitness.