Area-Optimized UAV Swarm Network for Search and Rescue Operations

As a quite interesting subject, there are an increasing number of researches about UAV (Unmanned Aerial Vehicle) in the literature.These robots are quite suitable to dull, dirty and dangerous missions (e.g., over flights at low altitude, especially at night, and longrunning operations, which exposes the crew to extreme fatigue).Thus, an important application of these vehicles is the search operations involving multiple UAVs.However, despite the huge potential use of the UAVs, cooperative search operations with this kind of flying robots are not yet occurring.This research topic is a multidisciplinary area of study in its beginning.Several issues can be studied related to cooperative UAV for search operations, such as centralized versus decentralized control, path planning for cooperative flies, agent reasoning for UAV tactical planning, safety assessments, reliability in automatic target reconnaissance by cameras, agent coordination mechanisms applied for UAV cooperation and the application itself (e.g., patrolling, remote sensing, search and rescue operations supporting, etc.).This paper proposes a model that combines path planning algorithms, search patterns and multiagent coordination techniques to obtain a cooperative UAV model.Different path planning algorithms were studied aiming to get the most suitable to these kinds of operations, and the conclusions are presented.In addition, official documents of Search and Rescue operations are also studied in order to know the best practices already established for this kind of operations, and, finally, an overview of the coordination multi-agent theory is presented and evaluated to achieve the UAV coordination.The great goal for cooperative UAV is to achieve such performance that the performance of the group overcomes the sum of the individual performances isolatedly.Comparing two cooperative UAVs with two non-cooperative UAVs, simulations of previous work showed an average increase in efficiency by about 100%.Besides, another aspect was simulated and evaluated: the impact of the density of the initial sweep.The computational simulations show a high potential of reducing even more the time of search.Furthermore, it is worth noting that the usefulness of cooperative UAVs is not only the increase in efficiency, but also the reduction of costs and risks for the crew.

Unmanned Aerial Vehicles (UAVs) can be an important resource when performing Search and Rescue (SAR) operations at sea, as this technology is fairly inexpensive when compared to traditional SAR approaches that use significant human resources and expensive air and naval assets, thus enabling the deployment of several UAVs simultaneously in these missions to perform rescue targets search in maritime environments.In order to maximize the usefulness of these UAVs in such operations, we propose a method which utilizes a state-of-the-art object detection network to perform real-time rescue target detection on-board the UAV, using standard RGB cameras, with minimal human intervention, thus enabling an increased vehicle autonomy and search range. Additionally, since the UAVs only relay the candidate images and locations that contain possible rescue targets, given by the onboard detector, it is possible to have several UAVs working in parallel that report back to a single human operator.We have selected the YOLOv4-tiny detection network, pretrained in the COCO dataset, and retrained it to detect rescue targets at sea. For this purpose some datasets were recorded and annotated to simulate the presence of maritime rescue targets. The proposed approach has been validated on an independent test dataset, showing that it has good detection capabilities and thus providing convincing results regarding the use of UAVs with automatic target detection capabilities in SAR missions.

Unmanned Aerial Vehicles (UAVs) are used in various applications, including crowd management, crime prevention, accident detection, and rescue operations. However, since UAVs perform their tasks independently, some UAV applications are dynamic and geographically distributed, which may require extensive real-time processing capabilities. Thus, processing UAV data locally can be challenging due to their limited computing capabilities. To overcome such limitations, fog and cloud computing can facilitate UAV application development by providing additional resource capacities when needed. Despite this, designing sophisticated and efficient UAV task offloading strategies that collaborate with fog and cloud technologies considering their service latency and energy consumption, is rarely addressed in the literature. Therefore, a collaborative offloading strategy for UAV applications is presented in this work, leveraging fog and cloud computing advantages and capabilities. This approach aims to minimize UAVs’ service latency and energy consumption, as well as provide the required resources and services in real time. In addition, task offloading decisions are formulated using the Mixed-Integer Linear Programming (MILP) model to reduce the energy consumption of the entire UAV-fog-cloud system by optimizing the allocation of computation resources and communication requested by each UAV. The simulation results demonstrate that the proposed strategy can significantly reduce UAV service latency by 15.38%, 35.29%, and 59.26%, as well as decrease overall energy consumption (including processing and networking) by 3.3%, 7.37%, and 12% when compared to alternative standalone strategies (namely UAV, fog, and cloud).

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.

- An Unmanned Aerial Vehicle (UAV), commonly known as a drone, is an aircraft that operates without a human pilot onboard and is controlled either remotely or autonomously. UAVs serve various applications, from defence to civilian purposes, and are categorized into fixed-wing, rotary-wing, hybrid, nano, tactical, target, and commercial drones. For this project, we selected a quadcopter due to its versatility, stability, and efficiency, making it ideal for applications like surveillance, aerial photography, emergency response, and environmental monitoring. The prototype is modeled in Fusion 360 for precise design and analyzed using ANSYS to assess structural integrity and durability under varying load conditions. The UAV frame is fabricated using lightweight materials such as carbon fiber and integrates key components like brushless DC motors, LiPo batteries, GPS modules, and a Pixhawk flight controller. Advanced features such as real-time data acquisition, autonomous flight capabilities, and efficient payload handling enhance its functionality. Additionally, 3D printing is used for custom parts, ensuring an optimized weight-to-strength ratio. To validate performance, the UAV undergoes simulation of flight dynamics and control systems using SolidWorks, followed by extensive ground and flight testing. The results demonstrate reliable performance in real-world scenarios such as medical supply delivery, environmental condition monitoring, and disaster relief efforts. By leveraging artificial intelligence, real-time data processing, and robust communication networks, this project contributes to the advancement of UAV technology, providing a cost-effective, efficient, and sustainable solution for public services and community needs. Key Words: Unmanned Aerial Vehicle (UAV), Quadcopter, Prototype Development, Structural Integrity, Environmental Monitoring.

Introduction: Unmanned aerial vehicle (UAV) swarms are increasingly employed in civilian and military applications. Efficient path planning is critical for navigating dynamic environments, yet challenges such as collision avoidance, energy optimization, and multiagent coordination hinder deployment. Methods: A systematic literature review (SLR) was conducted to classify UAV swarm pathplanning algorithms into four categories: sampling-based, graph-based, biologically inspired, and mathematical model-based approaches. The review adhered to established SLR guidelines, employing systematic database searches and predefined inclusion and exclusion criteria. Each algorithm was evaluated in terms of computational efficiency, adaptability, and application suitability. Results: Biologically inspired algorithms demonstrated high adaptability and flexibility, whereas graph-based approaches were effective in structured environments. Sampling-based methods proved scalable in high-dimensional spaces, and mathematical models emphasized optimization. Persistent challenges include real-time adaptability, energy efficiency, and operations in interactive environments. Discussion: The comparative analysis highlights the trade-offs inherent in each approach and underscores the unresolved challenges that limit practical implementation. Addressing these gaps is essential to advance the reliability and applicability of UAV swarms in real-world scenarios. Conclusion: This review systematically evaluates UAV swarm path-planning algorithms, identifying their respective advantages, limitations, and application contexts. The findings provide a reference framework to inform future research directions aimed at enhancing UAV swarm autonomy and operational efficiency.

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

An Unmanned Aerial Vehicle (UAV) is a powered pilotless aircraft, which is controlled remotely or autonomously. UAVs are currently employed in many military roles and a number of civilian applications. Some 32 nations are developing or manufacturing more than 250 models of UAVs and 41 countries operate some 80 types of UAVs (U. S. Department of Defense, 2005). By all accounts utilization of UAVs in military and civilian application is expanding both in the short term and long term. The two basic approaches to implementing unmanned flight, remote control and autonomy, rely predominantly on remote data communication and microprocessor technologies (U. S. Department of Defense, 2005). Advances in these technologies, which have grown exponentially since introduction, have dramatically improved the capabilities of the UAVs to address more complicated tasks. Increasing availability of low-cost computational power will stretch the boundary of what is possible to accomplish with less oversight of human operators, a feature generally called autonomy. In many civil and military flight missions the aircrafts freedom of action and movement is restricted and the path is predefined. Given the path, the control task of the aircraft is to generate the trajectory, i.e. to determine required control manoeuvres to steer the aircraft from one point to another. However, in some flight missions the path is not predefined but should dynamically be determined during the mission, e.g. in military surveillance, search & rescue missions, fire detection, and disaster operations. In this type of scenarios the goal of the UAV is to find the precise location of a searched object in an area of responsibility. Usually some uncertain a priori information about the initial location of the object is available. Since during the search operation this information may be modified due to new reports from other sources or the UAV’s observation, the path of the UAV can not be determined before starting the mission. In this chapter, we try to address this problem and introduce a framework for autonomous and dynamic UAV path planning in search operations. The rest of this chapter is organized as follows: section 2 describes the problem in general and the instance of the problem that we solve, section 3 presents history and related work, section 4 provides an overall description of the proposed solution, section 5 and 6 explains Sequential Monte Carlo (SMC) methods and how we have applied it, in section 7 simulation of a test case scenario and the obtained results are presented, and section 8 summarizes the chapter.

An Unmanned Aerial Vehicle (UAV) is an airborne system or pilotless aircraft which is remotely controlled by a human operator on ground or by an onboard computer such that the vehicle moves autonomously. The range of applications in which UAVs are used is very large. This paper describes the application of developing an autonomous surveillance system using an UAV to identify a given target and/or objects of interest in the terrain over which it flies. Such a system can be used in rescue operations, especially in remote areas where physical access is difficult. It can also be used for military operations, farming or any field where surveillance of a given land area is required. The UAV developed in this work is capable of object detection. A mounted camera is used to give visual feedback, and an onboard processing unit runs image recognition software to identify the target in real time. Optimal algorithms are used to search and find the target from the given search area. After recognition of the target, the UAV can either be used to hold its position so as to have a video feed of the target, or return to its base station once the coordinates have been estimated using GPS modules or relay the GPS location to the base station. This paper describes the implementation of the hardware and software components that lead to the realization of the UAV and the application of object detection. The details of a new search algorithm and an example of object detection is presented . The work presented in this paper is the first part in the attempt to develop a cluster of UAVs meant to work in collaboration to be deployed for search and rescue operations.

In recent years, the use of unmanned aerial vehicle (UAV) platforms in civil and military applications has surged, highlighting the critical role of artificial intelligence (AI) embedded UAV systems in the future. This study introduces the Autonomous Drone (Vechür-SIHA), a novel AI-embedded UAV system designed for real-time detection and tracking of other UAVs during flight sequences. Leveraging advanced object detection algorithms and an LSTM-based tracking mechanism, our system achieves an impressive 80% accuracy in drone detection, even in challenging conditions like varying backgrounds and adverse weather. Our system boasts the capability to simultaneously track multiple drones within its field of view, maintaining flight for up to 35 minutes, making it ideal for extended missions that require continuous UAV tracking. Moreover, it can lock onto and track other UAVs in mid-air for durations of 4-10 seconds without losing contact, a feature with significant potential for security applications. This research marks a substantial contribution to the development of AI-embedded UAV systems, with broad implications across diverse domains such as search and rescue operations, border security, and forest fire prevention. These results provide a solid foundation for future research, fostering the creation of similar systems tailored to different applications, ultimately enhancing the efficiency and safety of UAV operations. The novel approach to real-time UAV detection and tracking presented here holds promise for driving innovations in UAV technology and its diverse applications.

In the unmanned aerial vehicle (UAV) based localization of slow-moving radio frequency (RF) sources with unknown transmitted strength of signal, such as a person with a cell phone in a search and rescue mission, the UAV navigation errors are significant sources of localization error. Although the use of a global positioning system (GPS) can reduce the UAV’s localization error significantly resulting in more accurate RF source localization, if the GPS signal is lost temporarily or permanently, the accuracy of the UAV-based localization decreases rapidly. In this paper, a simultaneous localization and mapping (SLAM)-inspired approach for simultaneous localization of UAV and RF sources (SLUS) is proposed. The proposed approach solves these two connected problems, i.e. the UAV localization and RF source localization, simultaneously to decrease the error of the UAV position estimation and the error of the RF source localization. In the proposed approach, beside the UAV position prediction, the RF source position prediction is also performed. Then the predicted states are augmented and the augmented predicted state information is corrected using range-ratio and bearing observations, i.e. RF source features, considering the unknown transmitted power. The proposed approach is simulated and the results show that the normal divergence of a target localization and the divergence of the UAV navigation in latitude and longitude channels have been eliminated using this approach. In other words, simultaneous localization of the UAV and RF sources uses the RF sources, as features in the environment, to aid the navigation system. Although the approach is similar to the mapping of RF sources in the environment, the created map would be useless after finding the RF sources. That is why SLUS has been used instead of SLAM. The main contributions of this work are: 1) performing simultaneous localization of a UAV and targets using RF signals, especially in a non-line of sight (NLOS) condition; 2) using difference of signal strength, i.e. differential received strength signal indicator (DRSSI), to eliminate the impact of unknown target signal power; and 3) simultaneous multi-target localization and tracking.

In recent years, the utilization of unmanned aerial vehicles (UAVs) has surged in a variety of applications, including weather monitoring, emergency search and rescue operations, and smart agriculture. In these applications, UAVs are instrumental in executing data collection tasks. However, due to the scarcity of resources such as battery capacity, the duration of work is limited, necessitating the optimization of UAV trajectories and resource allocation. In this paper, we introduce a hierarchical task scheduling and resource allocation scheme for UAV data collection tasks, which incorporates the deep reinforcement learning (DRL) technology into a two-layer training framework. The continuous actions, including UAV trajectories, UAV communication power, and UAV CPU main frequency, are first optimized in the lower layer, while the upper one concentrates on the discrete action space for the target allocation. In particular, the well-trained lower-layer network is integrated into the upper layer, facilitating rapid global reward feedback between the two-layer network and accelerating the training process of the upper-layer network. Experimental results demonstrate that the proposed approach achieves superior performance than the baseline methods in terms of the amount of collected data as well as UAV energy consumption.

In the era of big data, unmanned aerial vehicles (UAVs) have become indispensable tools in wilderness search and rescue operations. Unlike traditional scenarios where the costs and rewards of all candidate search locations are known in advance, real-world missions require UAVs to make sequential decisions under strict energy constraints, with the reward and energy consumption at each location revealed only upon arrival. In this paper, we formulate the problem as an online Prize-Collecting Traveling Salesman Problem (PC-TSP) with costs, capturing the inherent uncertainty and dynamic nature of search and rescue tasks. We propose an innovative approach that integrates the nearest-neighbor principle with an online linear programming strategy to guide UAVs in maximizing rescue success or the total value of discovered clues. Extensive experiments on both simulated and real-world datasets demonstrate the effectiveness and superiority of our method over conventional offline planning techniques, highlighting its practical potential for real-time UAV decision-making in complex environments.

In the event of a maritime accident, surveying the maximum area efficiently in the least amount of time is crucial for rescuing survivors. Increasingly, unmanned aerial vehicles (UAVs) are being used in search and rescue operations. This study proposes a method to generate a search path that covers all generated nodes in the shortest amount of time with multiple heterogeneous UAVs. The proposed model, which is a mixed-integer linear programming (MILP) model based on a hexagonal grid-based decomposition method, was verified through a simulation analysis based on the performance of an actual UAV. This study presents both the optimization technique’s calculation time as a function of the search area size and the various UAV routes derived as the search area grows. The results of this study can have wide-ranging applications for emergency search and rescue operations.

In this paper, we consider the use of a team of multiple unmanned aerial vehicles (UAVs) to accomplish a search and rescue (SAR) mission in the minimum time possible while saving the maximum number of people. A novel technique for the SAR problem is proposed and referred to as the layered search and rescue (LSAR) algorithm. The novelty of LSAR involves simulating real disasters to distribute SAR tasks among UAVs. The performance of LSAR is compared, in terms of percentage of rescued survivors and rescue and execution times, with the max-sum, auction-based, and locust-inspired approaches for multi UAV task allocation (LIAM) and opportunistic task allocation (OTA) schemes. The simulation results show that the UAVs running the LSAR algorithm on average rescue approximately 74% of the survivors, which is 8% higher than the next best algorithm (LIAM). Moreover, this percentage increases with the number of UAVs, almost linearly with the least slope, which means more scalability and coverage is obtained in comparison to other algorithms. In addition, the empirical cumulative distribution function of LSAR results shows that the percentages of rescued survivors clustered around the [78%-100%] range under an exponential curve, meaning most results are above 50%. In comparison, all the other algorithms have almost equal distributions of their percentage of rescued survivor results. Furthermore, because the LSAR algorithm focuses on the center of the disaster, it finds more survivors and rescues them faster than the other algorithms, with an average of 55%~77%. Moreover, most registered times to rescue survivors by LSAR are bounded by a time of 04:50:02 with 95% confidence for a one-month mission time.