An Efficient Formation Control mechanism for Multi-UAV Navigation in Remote Surveillance

Raja, Gunasekaran; Baskar, Yashvandh; Dhanasekaran, Priyanka; Nawaz, Raheel; Yu, Ke

doi:10.1109/gcwkshps52748.2021.9682094

Cited by 15 publications

(9 citation statements)

References 24 publications

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“…Formation Flight Control for Navigation (FFCN) can be performed based on DRL (Raja et al, 2021). The learning strategy is based on the leader‐follower approach and includes fault‐tolerant mechanisms.…”

Section: Path Planning For Uav Inspectionmentioning

confidence: 99%

“…The strategy uses a heuristic guidance algorithm, a partially observable Markov Decision Process (POMDP) model to determine the UAV's flight range and a target-oriented Monte Carlo tree search algorithm pomCP-Go for the POMDP solution.Formation Flight Control for Navigation (FFCN) can be performed based on DRL(Raja et al, 2021). The learning strategy is based on the leader-follower approach and includes fault-tolerant mechanisms.…”

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confidence: 99%

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Unmanned aerial vehicle navigation in underground structure inspection: A review

Zhang,

Hao,

Zhang

et al. 2023

Geological Journal

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Many years after construction, a number of existing old tunnels and underground structures are deteriorating with time as evidenced by cracks, large deformations, water leakage and so forth, which usually require regular site inspections to record their structural deterioration by taking high‐pixel, high‐overlap images along miles of a tunnel network. For complex underground structures (e.g., long tunnels and large caves), unmanned aerial vehicles (UAVs) may be adaptive in acquiring images at multiple heights and angles with low operational costs. So far, UAV underground structural health monitoring has become mature for open‐air surveying with rapid developments in robotic software and hardware. However, the UAV image acquisition for underground working conditions still faces a number of key challenges. This paper aims to provide an overview of UAV navigation techniques in confined dark spaces for geotechnical engineers, geologists, drone developers and other interdisciplinary researchers & professionals in the structural health monitoring field. It specifies the challenges for UAV application in underground space, mainly including lack of Global Navigation Satellite System (GNSS) signals, poor lighting conditions, weak features and obstacle avoidance and then followed by strategic solutions. For example, in light of poor GNSS signals, the fusion of multi‐sensors (e.g., laser imaging, detection and ranging (LiDAR) and multi‐cameras) can enhance localization accuracy in low‐luminance underground conditions. To address obstacle avoidance, computer vision (CV)‐based navigation algorithms (e.g., deep reinforced learning [DRL]) enable effective navigation in complex 3D spaces, but their adaptability is limited by arithmetic power and pre‐training needs. The review of relevant previous studies concludes that further development for UAVs in underground space inspection may focus on operation in large‐scale geometric inspection environments, obstacle avoidance, features and semantic recognition.

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Section: Path Planning For Uav Inspectionmentioning

confidence: 99%

mentioning

confidence: 99%

Unmanned aerial vehicle navigation in underground structure inspection: A review

Zhang,

Hao,

Zhang

et al. 2023

Geological Journal

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“…A fault-tolerant formation control approach for UAVs of leader-follower type is discussed by Yu et al in [19] against the actuator faults of only followers based on finite-time adaptive techniques. Raja et al in [20] present a fault-tolerance module for a leader-follower mechanism to handle the actuator fault of a leader. In this algorithm, a new leader is chosen by using the locations of the follower and failed leader.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Post-Failure Reconfiguration of Swarms of Unmanned Aerial Vehicles

et al. 2023

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In this paper, the reconfiguration of swarms of unmanned aerial vehicles after simultaneous failures of multiple nodes is considered. The objectives of the post-failure reconfiguration are to provide collision avoidance and smooth energy-efficient movement. To incorporate such a mechanism, three different failure recovery algorithms are proposed namely thin plate spline, distance-and time-optimal algorithms. These methods are tested on six swarms, with two variations on failing nodes for each swarm. Simulation results of reconfiguration show that the execution of such algorithms maintains the desired formations with respect to avoiding collisions at run-time. Also, the results show the effectiveness concerning the distance travelled, kinetic energy, and energy efficiency. As expected, the distance-optimal algorithm gives the shortest movements, and the time-optimal algorithm gives the most energy-efficient movements. The thin plate spline is also found to be energy-efficient and has less computational cost than the other two proposed methods. Despite the suggested heuristics, these are combinatorial in nature and might be hard to use in practice. Furthermore, the use of the regularization parameter λ in thin plate spline is also investigated, and it is found that too large values on λ can lead to incorrect locations, including multiple nodes on the same location. In fact, it is found that using λ = 0 worked well in all cases.

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“…For a UAV swarm, position information can be shared, and internal collisions can be avoided by planning the flight path of each UAV within the swarm.Âȃ For instance, the literature [64] proposes a DRL-based formation flight control for navigation for effective UAV swarm construction. The collision rate of successful formation UAVs is reduced to 3.4% without colliding with other UAVs.…”

Section: Ai For Uav Collision Avoidancementioning

confidence: 99%

AI for UAV-Assisted IoT Applications : A Comprehensive Review

Chen¹,

Wu²,

Wang³

et al. 2022

Preprint

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With the rapid development of the Internet of Things (IoT), there are dramatic increasing number of devices in the network, which causes the challenge that only using infrastructure such as based station cannot provide service with all devices with high quality. Therefore, due to their flexibility and economy, unmanned aerial vehicles (UAV) are widely used to increase the performance of IoT networks. UAVs can not only provide communication services for IoT devices in the absence of a network, but they can also perform video surveillance, cargo transportation, pesticide spraying, and other specialized tasks. However, due to the complexity of the scenario and the need for real-time decision making, it is challenging to schedule UAVs in the network using traditional optimization methods, and growing attention has focused on using AI to optimize UAVs in the network. In this paper, we focus on the AI-enabled UAV optimization method in IoT networks and give a comprehensive scope on what and how to use AI-enabled methods to increase the performance of UAV-assisted IoT networks. Moreover, a brief analysis of the challenges of using AI methods in IoT networks and some potential research directions are given.

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An Efficient Formation Control mechanism for Multi-UAV Navigation in Remote Surveillance

Cited by 15 publications

References 24 publications

Unmanned aerial vehicle navigation in underground structure inspection: A review

Unmanned aerial vehicle navigation in underground structure inspection: A review

Energy-Efficient Post-Failure Reconfiguration of Swarms of Unmanned Aerial Vehicles

AI for UAV-Assisted IoT Applications : A Comprehensive Review

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