Determining position of target subjects in Maritime Search and Rescue (MSAR) operations using rotary wing Unmanned Aerial Vehicles (UAVs)

Ghazali, S.; Anuar, Hardy Azmir; Alsagoff, Syed Nasir; Yusoff, Zaharin

doi:10.1109/icictm.2016.7890765

Cited by 20 publications

(8 citation statements)

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“…There are four types of SAR problem, according to their application: maritime, combat, urban, and wilderness [33].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Their mixed integer linear program (MILP) model addressed these three challenges and worked efficiently on different numerical examples. Ghazali et al [33], employed UAVs with rotary wings for maritime SAR operations, hovering around disaster areas to locate survivor locations. Moreover, the authors provided an algorithm for this purpose whereby photographs were taken of areas that may contain survivors, then the photographs were divided into four quarters, and process was repeated until a survivor was found.…”

Section: Literature Reviewmentioning

confidence: 99%

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LSAR: Multi-UAV Collaboration for Search and Rescue Missions

2019

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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.INDEX TERMS Autonomous agents, drones, search and rescue, unmanned aerial vehicles.

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“…There are four types of SAR problem, according to their application: maritime, combat, urban, and wilderness [33].…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

LSAR: Multi-UAV Collaboration for Search and Rescue Missions

2019

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“…This leads to increasing requirements for the operation of these unmanned systems. It is required that their operation be more reliable and resistant to harsh environmental conditions (Ghazali et al, 2016; Wang et al, 2018). In many applications, it is insufficient to conduct air operations from a stationary take‐off and landing point.…”

Section: Introductionmentioning

confidence: 99%

Experimental tests of hybrid VTOL unmanned aerial vehicle designed for surveillance missions and operations in maritime conditions from ship‐based helipads

Ambroziak

Ciężkowski

Wolniakowski

et al. 2021

Journal of Field Robotics

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This article presents research on a hybrid vertical take‐off and landing (VTOL) unmanned aerial vehicle (UAV) supported by a relative positioning system, enabling the deployment of autonomous missions from ship‐based helipads in maritime conditions. A crucial issue to be solved is ensuring precise positioning of the UAV relative to the landing pad in the take‐off and landing phases. To achieve this, an extended Kalman filter (EKF) is implemented on the UAV's onboard computer, which integrates the positioning data from the UAV's global navigation satellite system receiver and the positioning data of the landing pad broadcast by the landing pad navigation station (LNS). The EKF estimates both the absolute and relative position of the UAV, which are required for autonomous take‐off and landing on the moving landing pad. In unfavorable weather conditions, EKF also uses data from an optional local positioning system to keep the accuracy within the range 1–3 m. The research was concluded by experimental verification during a ferry cruise over the Baltic Sea. During the research, the VTOL UAV performed two fully autonomous flight missions at the range about 1 km from the moving ferry in international waters. Each of them ended with a successful landing back on the helipad with an accuracy matching its required level, which was already achieved in the previous research carried out in inland conditions. Data recorded during real flights confirm that the developed system, consisting of a hybrid VTOL UAV and a LNS, is ready to be utilized in autonomous missions at sea. Factors having a critical impact on the safety of use of the VTOL UAV in marine conditions were also identified, which were not observed during the research in inland conditions, and are related directly to turbulence around the landing pad.

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“…Recent studies have been based on search-and-rescue (SAR) with unmanned aerial vehicles (UAVs) [3][4][5]. Prior to rescue, victim searching is achieved by using photo/video data obtained from UAV camera and radio signal data, i.e., WiFi from the smartphone of a victim.…”

Section: Introductionmentioning

confidence: 99%

AI-Aided Individual Emergency Detection System in Edge-Internet of Things Environments

Lee

Park

2021

Electronics

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Recently, many disasters have occurred in indoor places. In order to rescue or detect victims within disaster scenes, vital information regarding their existence and location is needed. To provide such information, some studies simply employ indoor positioning systems to identify each mobile device of victims. However, their schemes may be unreliable, since people sometimes drop their mobile devices or put them on a desk. In other words, their methods may find a mobile device, not a victim. To solve this problem, this paper proposes a novel individual monitoring system based on edge intelligence. The proposed system monitors coexisting states with a user and a smart mobile device through a user state detection mechanism, which could allow tracking through the monitoring of continuous user state switching. Then, a fine-grained localization scheme is employed to perceive the precise location of a user who is with a mobile device. Hence, the proposed system is developed as a proof-of-concept relying on off-the-shelf WiFi devices and reusing pervasive signals. The smart mobile devices of users interact with hierarchical edge computing resources to quickly and safely collect and manage sensing data of user behaviors with encryption by cipher-block chaining, and user behaviors are analyzed via the ensemble paradigm of three machine learning technologies. The proposed system shows 98.82% prevision for user activity recognition, and 96.5% accuracy for user localization accuracy is achieved.

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Determining position of target subjects in Maritime Search and Rescue (MSAR) operations using rotary wing Unmanned Aerial Vehicles (UAVs)

Cited by 20 publications

References 0 publications

LSAR: Multi-UAV Collaboration for Search and Rescue Missions

LSAR: Multi-UAV Collaboration for Search and Rescue Missions

Experimental tests of hybrid VTOL unmanned aerial vehicle designed for surveillance missions and operations in maritime conditions from ship‐based helipads

AI-Aided Individual Emergency Detection System in Edge-Internet of Things Environments

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