Area-Optimized UAV Swarm Network for Search and Rescue Operations

Ruetten, Laik; Regis, Paulo Alexandre; Feil-Seifer, David; Sengupta, Shamik

doi:10.1109/ccwc47524.2020.9031197

Cited by 35 publications

(9 citation statements)

References 15 publications

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“…Liu et al [10] presented an operational concept for UAV usage in SAR missions over rivers, in which they predicted the drift using Monte-Carlo simulations to delimit the search area. For the faster coverage of larger areas, authors like Ruetten et al [11] proposed swarm networks consisting of many UAVs that organize themselves to reach optimal search coverage with minimal overlapping. While this approach ensures fast detection over large areas, it poses an additional external risk to persons and significantly increases the required infrastructure and equipment.…”

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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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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“…The use of UAV swarms is not limited to military applications. They enable search and rescue missions over big areas [85]. Some agriculture duties such as watering or identifying sick plants are time-consuming, and using UAV swarms with minimal operator intervention would increase the efficiency of precision agriculture [86].…”

Section: A Swarming Advantagesmentioning

confidence: 99%

A Review of AI-enabled Routing Protocols for UAV Networks: Trends, Challenges, and Future Outlook

Rovira-Sugranes¹,

Razi²,

Afghah³

et al. 2021

Preprint

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Unmanned Aerial Vehicles (UAVs), as a recently emerging technology, enabled a new breed of unprecedented applications in different domains. This technology's ongoing trend is departing from large remotely-controlled drones to networks of small autonomous drones to collectively complete intricate tasks time and cost-effectively. An important challenge is developing efficient sensing, communication, and control algorithms that can accommodate the requirements of highly dynamic UAV networks with heterogeneous mobility levels. Recently, the use of Artificial Intelligence (AI) in learning-based networking has gained momentum to harness the learning power of cognizant nodes to make more intelligent networking decisions. An important example of this trend is developing learning-powered routing protocols, where machine learning methods are used to model and predict topology evolution, channel status, traffic mobility, and environmental factors for enhanced routing.This paper reviews AI-enabled routing protocols designed primarily for aerial networks, with an emphasis on accommodating highly-dynamic network topology. To this end, we review the basics of UAV technology, different approaches to swarm formation, and commonly-used mobility models, along with their impact on networking paradigms. We proceed with reviewing conventional and AI-enabled routing protocols, including topology-predictive and self-adaptive learning-based routing algorithms. We also discuss tools, simulation environments, remote experimentation platforms, and public datasets that can be used for developing and testing AI-enabled networking protocols for UAV networks. We conclude by presenting future trends, and the remaining challenges in AI-based UAV Networking, for different aspects of routing, connectivity, topology control, security and privacy, energy efficiency, and spectrum sharing.

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“…Quadrotor unmanned aerial vehicles (UAVs) have gotten a lot of attention in recent years because it is widely used in agricultural production (Chen et al, 2021a;Zhou et al, 2021;Zhang et al, 2021;Chen et al, 2021b), unique period communication technology (Li et al, 2018), air transportation (Qian and Liu, 2019) and rescue information collection duties in risky and limited space (Ruetten et al, 2020;Feraru et al, 2020). High-precision trajectory tracking was required to complete the above tasks successfully (Zhang et al, 2020d;Gu and Wang, 2022;Yu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Adaptive dynamic surface output feedback control for a class of quadrotor aircraft with actuator faults

Zhu

Zhang

et al. 2022

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Purpose The purpose of this study is to propose an adaptive fault-tolerant control approach based on output feedback for a class of quadrotor unmanned aerial vehicles system. In the event of a controlled actuator failure, a stable flying of the aircraft can be achieved by selecting an appropriate sliding mode surface. Design/methodology/approach Aiming at the actuator failure of quadrotor aircraft during flight in the controllable range, a dynamic surface sliding mode passive fault-tolerant controller based on output feedback is designed based on the strong robustness of sliding mode method. Due to the unknown nonlinearity dynamics and parameter uncertainties in the system, a nonlinear observer is used to estimate them online. Findings The stability of the suggested algorithm is established using appropriate Lyapunov functions, and the performance of the proposed control approach is demonstrated using hardware-in-the-loop simulation. Originality/value An error performance function is introduced into the controller to ensure the convergence speed and accuracy of errors are within the predetermined range. By using the norm estimation method, there is only one parameter that needs to be updated in each step of the control process, which considerably minimizes the calculation burden. Finally, the validity of the proposed control scheme is verified on the hardware-in-the-loop simulation, and the results show that the proposed control method has achieved the desired results.

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Area-Optimized UAV Swarm Network for Search and Rescue Operations

Cited by 35 publications

References 15 publications

Optimal UAV Hangar Locations for Emergency Services Considering Restricted Areas

Optimal UAV Hangar Locations for Emergency Services Considering Restricted Areas

A Review of AI-enabled Routing Protocols for UAV Networks: Trends, Challenges, and Future Outlook

Adaptive dynamic surface output feedback control for a class of quadrotor aircraft with actuator faults

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