Reconnaissance Mission Conducted by UAV Swarms Based on Distributed PSO Path Planning Algorithms

Wang, Yubing; Bai, Peng; Liang, Xiaolong; Wang, Weijia; Zhang, Jiaqiang; Fu, Qing

doi:10.1109/access.2019.2932008

Cited by 112 publications

(56 citation statements)

References 29 publications

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“…separation, aggregation and aligment) [6]. Computational swarm intelligence mechanisms, like the PSO and genetic algorithms, have been investigated among others by [8]- [10] while [11], [28] have proposed the application of well-known robotic mobility models on FANETs. At the same time, few works have taken into account the Quality of Service (QoS) of wireless links between the ground nodes and the UAVs, that is a key requirement for applications with strict requirements (e.g.…”

Section: A Motivationsmentioning

confidence: 99%

“…how to increase the service time of an aerial mesh exploiting the presence of charging stations on the ground: the optimization problem is formulated by means of distributed PSO techniques in [8] and of game theory in [21]. UAV group mobility based on particle swarming is investigated also in [6], [9] and [10]. The three components of the flocking behaviour (i.e.…”

Section: Related Workmentioning

confidence: 99%

“…In [9], a PSO-based topology management algorithm is proposed in order to adjust the position of UAV relays, so that the FANET is always connected to Ground Control Stations (GCSs). Three PSO-based swarm mobility algorithms are proposed in [10] for UAVs performing reconnaissance missions over targets in hostile environments. Other distributed bio-inspired techniques for swarm creation and management are proposed -among others-by [22] and [23].…”

Section: Related Workmentioning

confidence: 99%

“…there must exist a multihop path between each couple of UAVs; constraint (6) avoids the collisions among the UAVs; constraint (7) ensures that the throughput of each wireless link (AtA or AtG) cannot exceed the current link capacity; constraint (8) ensures the consistency of the data flow through the aerial mesh, i.e. if an aerial link u i , u j carries data originated by h z ∈ H , then there is an incoming data link on UAV u i (u i is routing data for h z ); constraints (9) and (10) guarantee that there is no multipath in the mesh, namely each data flow can be sent and received by only one node, respectively; constraint (11) states that the rescue personnel is not transmitting data; constraints (12) and (13) ensure that any data flow labeled with h i ∈ H is generated only by h i .…”

Section: B Problem Formulationmentioning

confidence: 99%

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A GPS-Free Flocking Model for Aerial Mesh Deployments in Disaster-Recovery Scenarios

et al. 2020

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In the aftermath of a large-scale emergency, Unmanned Aerial Vehicles (UAVs) can play a key role as mobile communication systems supporting rescue operations on the ground. At the same time, the deployment of autonomous UAV swarms still poses severe challenges in terms of distributed mobility, swarm connectivity and mesh networking. To this purpose, we propose ELAPSE (aErial LocAl Positioning System for Emergency), a novel, distributed framework for aerial mesh deployment that supports discovery and multi-hop connectivity among rescue personnel and emergency requesters. ELAPSE integrates components of swarm mobility, positioning and Quality-of-Service (QoS) support, while targeting UAV devices at different levels of hardware complexity. Three contributions are provided in this study. First, we present a novel, bio-inspired swarm mobility algorithm which natively addresses QoS-based aerial mesh connectivity, coverage of the ground nodes and UAV collision avoidance through the abstraction of virtual springs. Second, we investigates its implementation when geo-location capabilities are not available: to this aim, we propose local-based and cooperative-based techniques through which each UAV can estimate the position of its neighbours, and hence correctly adjust its direction and speed. Third, we analyze the feasibility of the ELAPSE framework through a twofold evaluation: i.e. a large-scale OMNeT++ simulation showing the effectiveness of the distributed mesh formation and localization techniques, and a small-case ground robotic testbed demonstrating the impact of QoS mechanisms on the system operations.

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Section: A Motivationsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: B Problem Formulationmentioning

confidence: 99%

See 2 more Smart Citations

A GPS-Free Flocking Model for Aerial Mesh Deployments in Disaster-Recovery Scenarios

et al. 2020

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“…As a swarm intelligence optimization algorithm, PSO has many advantages, such as stability, a short time convergence, few parameters to adjust and ease of implementation. PSO has been successfully applied to the combination optimization of various engineering problem areas, such as data mining [47], artificial neural network training [48], vehicle path planning [49], medical diagnosis [50,51] and system and engineering design [52].…”

Section: Pso Algorithmmentioning

confidence: 99%

A Weighted Ensemble Learning Algorithm Based on Diversity Using a Novel Particle Swarm Optimization Approach

et al. 2020

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In ensemble learning, accuracy and diversity are the main factors affecting its performance. In previous studies, diversity was regarded only as a regularization term, which does not sufficiently indicate that diversity should implicitly be treated as an accuracy factor. In this study, a two-stage weighted ensemble learning method using the particle swarm optimization (PSO) algorithm is proposed to balance the diversity and accuracy in ensemble learning. The first stage is to enhance the diversity of the individual learner, which can be achieved by manipulating the datasets and the input features via a mixed-binary PSO algorithm to search for a set of individual learners with appropriate diversity. The purpose of the second stage is to improve the accuracy of the ensemble classifier using a weighted ensemble method that considers both diversity and accuracy. The set of weighted classifier ensembles is obtained by optimization via the PSO algorithm. The experimental results on 30 UCI datasets demonstrate that the proposed algorithm outperforms other state-of-the-art baselines.

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UAV Path Planning Based on Variable Neighborhood Search Genetic Algorithm

Zhang

Wang

Lei

et al. 2021

Lecture Notes in Computer Science

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Reconnaissance Mission Conducted by UAV Swarms Based on Distributed PSO Path Planning Algorithms

Cited by 112 publications

References 29 publications

A GPS-Free Flocking Model for Aerial Mesh Deployments in Disaster-Recovery Scenarios

A GPS-Free Flocking Model for Aerial Mesh Deployments in Disaster-Recovery Scenarios

A Weighted Ensemble Learning Algorithm Based on Diversity Using a Novel Particle Swarm Optimization Approach

UAV Path Planning Based on Variable Neighborhood Search Genetic Algorithm

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