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See detailAn Evolutionary Algorithm to Optimise a Distributed UAV Swarm Formation System
Stolfi Rosso, Daniel UL; Danoy, Grégoire UL

in Applied Sciences (2022), 12(20),

In this article, we present a distributed robot 3D formation system optimally parameterised by a hybrid evolutionary algorithm (EA) in order to improve its efficiency and robustness. To achieve that, we ... [more ▼]

In this article, we present a distributed robot 3D formation system optimally parameterised by a hybrid evolutionary algorithm (EA) in order to improve its efficiency and robustness. To achieve that, we first describe the novel distributed formation algorithm3 (DFA3), the proposed EA, and the two crossover operators to be tested. The EA hyperparameterisation is performed by using the irace package and the evaluation of the three case studies featuring three, five, and ten unmanned aerial vehicles (UAVs) is performed through realistic simulations by using ARGoS and ten scenarios evaluated in parallel to improve the robustness of the configurations calculated. The optimisation results, reported with statistical significance, and the validation performed on 270 unseen scenarios show that the use of a metaheuristic is imperative for such a complex problem despite some overfitting observed under certain circumstances. All in all, the UAV swarm self-organised itself to achieve stable formations in 95% of the scenarios studied with a plus/minus ten percent tolerance. [less ▲]

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See detailOptimising Autonomous Robot Swarm Parameters for Stable Formation Design
Stolfi Rosso, Daniel UL; Danoy, Grégoire UL

in Proceedings of the Genetic and Evolutionary Computation Conference (2022, July 08)

Autonomous robot swarm systems allow to address many inherent limitations of single robot systems, such as scalability and reliability. As a consequence, these have found their way into numerous ... [more ▼]

Autonomous robot swarm systems allow to address many inherent limitations of single robot systems, such as scalability and reliability. As a consequence, these have found their way into numerous applications including in the space and aerospace domains like swarm-based asteroid observation or counter-drone systems. However, achieving stable formations around a point of interest using different number of robots and diverse initial conditions can be challenging. In this article we propose a novel method for autonomous robots swarms self-organisation solely relying on their relative position (angle and distance). This work focuses on an evolutionary optimisation approach to calculate the parameters of the swarm, e.g. inter-robot distance, to achieve a reliable formation under different initial conditions. Experiments are conducted using realistic simulations and considering four case studies. The results observed after testing the optimal configurations on 72 unseen scenarios per case study showed the high robustness of our proposal since the desired formation was always achieved. The ability of self-organise around a point of interest maintaining a predefined fixed distance was also validated using real robots. [less ▲]

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See detailSuSy-EnGaD: Surveillance System Enhanced by Games of Drones
Stolfi Rosso, Daniel UL; Brust, Mathias UL; Danoy, Grégoire UL et al

in Drones (2022), 6(13),

In this article, we propose SuSy-EnGaD, a surveillance system enhanced by games of drones. We propose three different approaches to optimise a swarm of UAVs for improving intruder detection, two of them ... [more ▼]

In this article, we propose SuSy-EnGaD, a surveillance system enhanced by games of drones. We propose three different approaches to optimise a swarm of UAVs for improving intruder detection, two of them featuring a multi-objective optimisation approach, while the third approach relates to the evolutionary game theory where three different strategies based on games are proposed. We test our system on four different case studies, analyse the results presented as Pareto fronts in terms of flying time and area coverage, and compare them with the single-objective optimisation results from games. Finally, an analysis of the UAVs trajectories is performed to help understand the results achieved. [less ▲]

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See detailImproving Pheromone Communication for UAV Swarm Mobility Management
Stolfi Rosso, Daniel UL; Brust, Mathias UL; Danoy, Grégoire UL et al

in ICCCI 2021: Computational Collective Intelligence (2021, July 30)

In this article we address the optimisation of pheromone communication used for the mobility management of a swarm of Unmanned Aerial Vehicles (UAVs) for surveillance applications. A genetic algorithm is ... [more ▼]

In this article we address the optimisation of pheromone communication used for the mobility management of a swarm of Unmanned Aerial Vehicles (UAVs) for surveillance applications. A genetic algorithm is proposed to optimise the exchange of pheromone maps used in the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model which improves the vehicles’ routes in order to achieve unpredictable trajectories as well as maximise area coverage. Experiments are conducted using realistic simulations, which additionally permit to assess the impact of packet loss ratios on the performance of the surveillance system, in terms of reliability and area coverage. [less ▲]

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See detailSwarm-based counter UAV defense system
Brust, Matthias R. UL; Danoy, Grégoire UL; Stolfi Rosso, Daniel UL et al

in Discover Internet of Things (2021), 1(1),

Unmanned Aerial Vehicles (UAVs) have quickly become one of the promising Internet-of-Things (IoT) devices for smart cities. Thanks to their mobility, agility, and onboard sensors'customizability, UAVs ... [more ▼]

Unmanned Aerial Vehicles (UAVs) have quickly become one of the promising Internet-of-Things (IoT) devices for smart cities. Thanks to their mobility, agility, and onboard sensors'customizability, UAVs have already demonstrated immense potential for numerous commercial applications. The UAVs expansion will come at the price of a dense, high-speed and dynamic traffic prone to UAVs going rogue or deployed with malicious intent. Counter UAV systems (C-UAS) are thus required to ensure their operations are safe. Existing C-UAS, which for the majority come from the military domain, lack scalability or induce collateral damages. This paper proposes a C-UAS able to intercept and escort intruders. It relies on an autonomous defense UAV swarm, capable of self-organizing their defense formation and to intercept the malicious UAV. This fully localized and GPS-free approach follows a modular design regarding the defense phases and it uses a newly developed balanced clustering to realize the intercept- and capture-formation. The resulting networked defense UAV swarm is resilient to communication losses. Finally, a prototype UAV simulator has been implemented. Through extensive simulations, we demonstrate the feasibility and performance of our approach. [less ▲]

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See detailUAV-UGV-UMV Multi-Swarms for Cooperative Surveillance
Stolfi Rosso, Daniel UL; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in Frontiers in Robotics and AI (2021), 8

In this paper we present a surveillance system for early detection of escapers from a restricted area based on a new swarming mobility model called CROMM-MS (Chaotic Rössler Mobility Model for Multi ... [more ▼]

In this paper we present a surveillance system for early detection of escapers from a restricted area based on a new swarming mobility model called CROMM-MS (Chaotic Rössler Mobility Model for Multi-Swarms). CROMM-MS is designed for controlling the trajectories of heterogeneous multi-swarms of aerial, ground and marine unmanned vehicles with important features such as prioritising early detections and success rate. A new Competitive Coevolutionary Genetic Algorithm (CompCGA) is proposed to optimise the vehicles’ parameters and escapers’ evasion ability using a predator-prey approach. Our results show that CROMM-MS is not only viable for surveillance tasks but also that its results are competitive in regard to the state-of-the-art approaches. [less ▲]

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See detailOptimizing the Performance of an Unpredictable UAV Swarm for Intruder Detection
Stolfi Rosso, Daniel UL; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in Optimization and Learning - Third International Conference, OLA 2020, Cádiz, Spain, February 17-19, 2020, Proceedings (2020)

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See detailCompetitive Evolution of a UAV Swarm for Improving Intruder Detection Rates
Stolfi Rosso, Daniel UL; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in 2020 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2020, New Orleans, LA, USA, May 18-22, 2020 (2020)

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See detailA Cooperative Coevolutionary Approach to Maximise Surveillance Coverage of UAV Swarms
Stolfi Rosso, Daniel UL; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in IEEE 17th Annual Consumer Communications & Networking Conference CCNC 2020, Las Vegas, NV, USA, January 10-13, 2020 (2020)

Detailed reference viewed: 129 (21 UL)