Distributed and Collaborative Localization for Swarming UAVs

Chen, Rui; Yang, Bin; Zhang, Wei

doi:10.1109/jiot.2020.3037192

Cited by 51 publications

(20 citation statements)

References 27 publications

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“…Cooperation between SUAVs should always be guaranteed despite the harsh environmental conditions and failures of their hardware [33]. Besides, collision avoidance and the fulfilment of their tasks and decision-making rely on the flow of information shared with/among the UAVs and their coordination.…”

Section: Cooperative Localization In the Internet Of Everything (Ioe)mentioning

confidence: 99%

Swarm Unmanned Aerial Vehicles (SUAVs): A Comprehensive Analysis of Localization, Recent Aspects, and Future Trends

Khelifi

Bütün

2022

Journal of Sensors

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Within the last decade, Swarm Unmanned Aerial Vehicles (SUAVs) are booming and growing at a surprisingly rapid pace. From military combat, environmental surveillance, and air transport to the blossoming public entertainment sector, there is a wide range of UAV applications. For these above use cases, the accurate location of the target of interest can be requested/queried, which is very important for their mission accomplishment. In the case of GPS-armed SUAVs, this is an easy task. However, the GPS signal can be obscured, affected by environmental conditions, or suppressive jamming. Therefore, location information needs to be improved/assisted by some other localization techniques, which constitutes the main scope of this article. Besides, with the advancements in localization, guidance, and communication technologies; future SUAVs will be operating autonomously by distributing tasks and coordinating the operation of many UAVs. Thus, UAV to UAV communication is presented as it constitutes the SUAV’s autonomous coordination ability. In addition, future research directions and open challenges that need to be addressed, including autonomous SUAVs, are also discussed.

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Section: Cooperative Localization In the Internet Of Everything (Ioe)mentioning

confidence: 99%

Swarm Unmanned Aerial Vehicles (SUAVs): A Comprehensive Analysis of Localization, Recent Aspects, and Future Trends

Khelifi

Bütün

2022

Journal of Sensors

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“…Guo et al [33] proposed a localization technique by the combination of distributed cooperative localization and distance-based formation control for UAV swarms in infrastructure-less GPS-free environments. Chen et al [34] proposed a distributed and collaboration-based localization technique for multi-UAV swarms to address the low-accuracy of GPS signals. In [34], the authors utilized multidimensional scaling technique to get the relative coordinates of UAVs with GPS signals.…”

Section: Review Of Related Studiesmentioning

confidence: 99%

“…Chen et al [34] proposed a distributed and collaboration-based localization technique for multi-UAV swarms to address the low-accuracy of GPS signals. In [34], the authors utilized multidimensional scaling technique to get the relative coordinates of UAVs with GPS signals.…”

Section: Review Of Related Studiesmentioning

confidence: 99%

Bio-Inspired Approaches for Energy-Efficient Localization and Clustering in UAV Networks for Monitoring Wildfires in Remote Areas

Arafat

Moh

2021

IEEE Access

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In dynamic unmanned aerial vehicle (UAV) networks, localization and clustering are fundamental functions for cooperative control. In this article, we propose bio-inspired localization (BIL) and clustering (BIC) schemes in UAV networks for wildfire detection and monitoring. First, we develop a hybrid gray wolf optimization (HGWO) method and propose an energy-efficient three-dimensional BIL algorithm based on the HGWO, which reduces localization errors, avoids flip ambiguity in bounded distance measurement errors, and achieves high localization accuracy. In BIL, the bounding cube method is applied to reduce the initial search space. Second, we propose an energy-efficient BIC algorithm based on the HGWO. The BIC algorithm utilizes the gray wolf leadership hierarchy to improve clustering efficiency. We also develop an analytical model for determining the optimal number of clusters that provide the minimum number of transmissions. Finally, we propose a GWO-based compressive sensing (CS-GWO) algorithm to transmit data from cluster heads (CHs) to the base station (BS). The proposed CS-GWO constructs an efficient routing tree from CHs to the BS, thereby reducing the routing delay and the number of transmissions. Our performance evaluation shows that the proposed BIL and BIC significantly outperform conventional schemes in terms of various performance metrics under different scenarios.INDEX TERMS Bio-inspired algorithm, cluster head, clustering, energy efficiency, gray wolf optimization, localization, network lifetime, routing protocol, unmanned aerial vehicle network.

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“…Autonomous navigation of UAVs in largescale dynamic environments is one of the key components for optimal outcome. Localization and mapping techniques [13], [14], and sensing and avoidance techniques [15] are often used in traditional approaches to achieve autonomous navigation.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Approaches for UAV Navigation: Recent Advances and Future Challenges

Rezwan

Choi

2022

IEEE Access

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Unmanned aerial vehicles (UAVs) applications have increased in popularity in recent years because of their ability to incorporate a wide variety of sensors while retaining cheap operating costs, easy deployment, and excellent mobility. However, controlling UAVs remotely in complex environments limits the capability of the UAVs and decreases the efficiency of the whole system. Therefore, many researchers are working on autonomous UAV navigation where UAVs can move and perform the assigned tasks based on their surroundings. With recent technological advancements, the application of artificial intelligence (AI) has proliferated. Autonomous UAV navigation is an example of an application in which AI plays a critical role in providing fundamental human control characteristics. Thus, many researchers have adopted different AI approaches to make autonomous UAV navigation more efficient. This paper comprehensively surveys and categorizes several AI approaches for autonomous UAV navigation implicated by several researchers. Different AI approaches comprise mathematical-based optimization and model-based learning approaches. The fundamentals, working principles, and main features of the different optimization-based and learningbased approaches are discussed in this paper. In addition, the characteristics, types, navigation models, and applications of UAVs are highlighted to make AI implementation understandable. Finally, the open research directions are discussed to provide researchers with clear and direct insights for further research.

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Distributed and Collaborative Localization for Swarming UAVs

Cited by 51 publications

References 27 publications

Swarm Unmanned Aerial Vehicles (SUAVs): A Comprehensive Analysis of Localization, Recent Aspects, and Future Trends

Swarm Unmanned Aerial Vehicles (SUAVs): A Comprehensive Analysis of Localization, Recent Aspects, and Future Trends

Bio-Inspired Approaches for Energy-Efficient Localization and Clustering in UAV Networks for Monitoring Wildfires in Remote Areas

Artificial Intelligence Approaches for UAV Navigation: Recent Advances and Future Challenges

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