Development of a Lifeguard assist Drone for coastal search and rescue

Ajgaonkar, Kaustubh; Khanolkar, Shailesh; Rodrigues, Janslon; Shilker, Eshani; Borkar, Prajita; Braz, Enrich

doi:10.1109/ieeeconf38699.2020.9389382

Cited by 12 publications

(6 citation statements)

References 30 publications

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“…The establishment of emergency beacon stations, whereby beachgoers can alert emergency services about an incident immediately and, in some cases, access a flotation device to assist in a bystander rescue, may save not only the life of the individual in distress, but also the bystander rescuer. Technology may also play a future role with the potential use of drones, which have primarily been used in beach safety in relation to shark surveillance (Claesson et al, 2017;Butcher et al, 2019;Ajgaonkar et al, 2020), but also have the potential to be used to identify rip current locations. However, in the case of unpatrolled beaches, there is a logistic constraint of requiring a trained and certified operator to be present.…”

Section: Implications For Beach Safety Managementmentioning

confidence: 99%

Characteristics and beach safety knowledge of beachgoers on unpatrolled surf beaches in Australia

Uebelhoer

Koon

Harley

et al. 2022

Nat. Hazards Earth Syst. Sci.

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Abstract. The majority of drowning deaths on Australian beaches occur significant distances away from lifeguard services. This study uses results of 459 surveys of beachgoers at five beaches unpatrolled by lifeguards in New South Wales, Australia, to improve understanding of who visits these beaches and why, and to identify risk factors associated with their beach safety knowledge and behaviour. Many unpatrolled beach users were infrequent beachgoers (64.9 %) with poor rip current hazard identification skills, who did not observe safety signage that was present, and yet intended to enter the water to swim (85.6 %) despite being aware that no lifeguards were present. The survey found that the main reasons why beachgoers visited unpatrolled beaches were because they were conveniently close to their holiday accommodation, or they represented a quieter location away from crowds. Future beach safety interventions in Australia need to extend beyond the standard “swim between the flags” message in recognition that many Australian beaches will remain unpatrolled, yet still frequented, for the foreseeable future. Future beach safety interventions for unpatrolled beaches should be tailored towards the varied demographic groups of beach users.

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Section: Implications For Beach Safety Managementmentioning

confidence: 99%

Characteristics and beach safety knowledge of beachgoers on unpatrolled surf beaches in Australia

Uebelhoer

Koon

Harley

et al. 2022

Nat. Hazards Earth Syst. Sci.

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“…Various SAR concepts have been studied using UAS as a component in the rescue chain. Ajgaonkar et al [7] developed a UAV to assist lifeguards at coastal beaches. They assumed that the lifeguard provides the initial identification of the person in distress to a UAV operator, who then searches the area to drop a flotation device.…”

Section: Sar Concepts With Uasmentioning

confidence: 99%

Optimal UAV Hangar Locations for Emergency Services Considering Restricted Areas

Braßel¹,

Zeh²,

Fricke³

et al. 2023

Preprint

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With Unmanned Aerial Vehicles (UAV), a swift response to urgent needs like search \& rescue missions or medical deliveries can be realized. Simultaneously, the legislator is establishing so-called geographical zones, which restrict UAV operations to mitigate the air and ground risk to third parties. These geographical zones serve a particular safety interest, but they may also hinder the efficient usage of UAVs on time-critical missions with a range-limiting battery capacity. In this study, we address a facility location problem for up to two UAV hangars with a robust optimization model considering demand hotspots, geographical zones as restricted areas, a standard mission to satisfy battery capacity constraints, and the impact of wind scenarios. To this end, water rescue missions are used exemplary, for which positive and negative location factors for UAV hangars and areas of increased drowning risk as demand points are derived from open-source georeferenced data. Optimal UAV mission trajectories are computed with an A* algorithm considering five different restriction scenarios. As this pathfinding is very time-consuming, binary occupancy grids and image processing algorithms accelerate the computation by identifying either entirely inaccessible or restriction-free connections beforehand. For the optimal UAV hangar locations, we maximize accessibility while minimizing the service time to the hotspots, resulting in a decrease from the average service time of 570.4 s for all facility candidates to 351.1 s for one and 287.2 s for two optimal UAV hangar locations.

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“…Eid et al found that the pressure distribution was greatest on the outer surface of the lower chassis, where the wind blows, and then smallest upper chassis. In addition, due to the lower width from the edge of the hole to the edge of the frame [12], the pressure around the hole created in the design will have a high value. As a result, the center chassis is reinforced with additional support to offset the higher pressures.…”

Section: Uav On Chassismentioning

confidence: 99%

Exploring The Use of Convolutional Neural Networks in UAV Based Rescue Missions

Shen

2023

ACE

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Multi-rotor based flight platforms, especially quadcopters and unmanned aerial vehicles (UAVs), and their embedded avionics have become emerging technologies in rescue operations. Due to the simplicity of mass production, as well as air superiority, rapid deployment and vertical take-off and landing (VTOL) capabilities, autonomous driving will be the next step in rescue operations. This project aims to explore the application of UAV combined with deep learning algorithm in drowning scenario. With the help of Betaflight, create an autonomous flight program that implements specific drowning rescue functions. Deep learning algorithms are critical to developing neural recognition networks for drowning individuals, with pre-programmed flight parameters eliminating the need for human interaction, human error to be precise. The core goal of the project is to improve the success rate of lifesaving solutions through the availability of this technology, a longer operating range than lifeguards, and shorter response times.

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Development of a Lifeguard assist Drone for coastal search and rescue

Cited by 12 publications

References 30 publications

Characteristics and beach safety knowledge of beachgoers on unpatrolled surf beaches in Australia

Characteristics and beach safety knowledge of beachgoers on unpatrolled surf beaches in Australia

Optimal UAV Hangar Locations for Emergency Services Considering Restricted Areas

Exploring The Use of Convolutional Neural Networks in UAV Based Rescue Missions

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