Coverage Optimization with Connectivity Preservation for UAV Swarms Applying Chaotic Dynamics

Rosalie, Martin; Brust, Matthias R.; Danoy, Grégoire; Chaumette, Serge; Bouvry, Pascal

doi:10.1109/icac.2017.26

Cited by 24 publications

(13 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using multiple UAVs can be very efficient for surveillance missions thanks to collaboration between the platforms because this makes it possible to support a large number of capabilities (multi-sensor, multi-platform, multi-modal capabilities). However, since the on board communication systems have a limited range, a compromise has to be found between two adversary criteria: maximizing area coverage and preserving network connectivity [19]. A method achieving these objectives, based on chaotic dynamics and ant colony optimization (ACO), has been presented by Rosalie et al [19] and Bouvry et al [6].…”

Section: A Mobility Strategies For Fanetsmentioning

confidence: 99%

“…However, since the on board communication systems have a limited range, a compromise has to be found between two adversary criteria: maximizing area coverage and preserving network connectivity [19]. A method achieving these objectives, based on chaotic dynamics and ant colony optimization (ACO), has been presented by Rosalie et al [19] and Bouvry et al [6].…”

Section: A Mobility Strategies For Fanetsmentioning

confidence: 99%

“…A large number of studies that deal with swarms of UAVs consider area coverage missions (keeping connectivity constraints in mind [19], [15], [23]) but most of them consider obstacle-free environments. In our work, we consider the case of urban-like areas.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A 3D Mobility Model for Autonomous Swarms of Collaborative UAVs

Falomir

Chaumette

Guerrini

2019

2019 International Conference on Unmanned Aircraft Systems (ICUAS)

Self Cite

View full text Add to dashboard Cite

Collaboration between several Unmanned Aerial Vehicles (UAVs) can produce high-quality results in numerous missions, including surveillance, search and rescue, tracking or identification. Such a combination of collaborative UAVs is referred to as a swarm. These several platforms enhance the global system capabilities by supporting some form of resilience and by increasing the number and/or the variety of the embedded sensors. Furthermore, several UAVs organized in a swarm can (should the ground control station support this) be considered as a single entity from an operator pointof-view. We aim at using such swarms in complex and unknown environments, and in the long term, allow compact flights. Dynamic path planning computation for each UAV is a major task to perform their mission. To define this path planning, we have implemented a three-dimensional (3D) mobility model for swarms of UAVs using both the Artificial Potential Fields (APF) principle and a global path planning method. In our model, the collaboration between the platforms is made by sharing information about the detected obstacles. To provide a significant validation of our mobility model, we have simulated real-world environments and real-world sensors characteristics, using the OMNeT++ network simulator.

show abstract

Section: A Mobility Strategies For Fanetsmentioning

confidence: 99%

Section: A Mobility Strategies For Fanetsmentioning

confidence: 99%

See 1 more Smart Citation

A 3D Mobility Model for Autonomous Swarms of Collaborative UAVs

Falomir

Chaumette

Guerrini

2019

2019 International Conference on Unmanned Aircraft Systems (ICUAS)

Self Cite

View full text Add to dashboard Cite

show abstract

“…They allow stabilization of compact formations, even in environments containing obstacles [24]. Rosalie et al [22] recently proposed a model combining Boids principle, Ant Colony Optimization and chaotic dynamics to fulfill a mission of area coverage while maximizing connectivity within the swarm.…”

Section: Path Planning For Multi-platform Systemsmentioning

confidence: 99%

A Mobility Model Based on Improved Artificial Potential Fields for Swarms of UAVs

Falomir

Chaumette

Guerrini

2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Self Cite

View full text Add to dashboard Cite

A combination of several autonomous UAVs can be used to perform collaborative tasks. Such a combination is referred to as a swarm of drones. The use of multiple platforms can extend the system global capacities thanks to the resulting variety of embedded sensors and to information sharing. In this case, path planning and thus obstacles avoidance is still a major task. To deal with this issue, mobility models have to be implemented. Our contribution presented in this paper is a mobility model for swarms of UAVs based on the Artificial Potential Fields (APF) principle. In our model, the involved UAVs collaborate by sharing data about the obstacles that they detected. By doing so, a UAV which is not close enough to an obstacle to detect it thanks to its own sensors will still have the proper data to take this obstacle into account in its path planning. To validate our mobility strategies with realistic constraints we simulate the performances of existing sensors and transmitters, and consider real-world environment.

show abstract

“…Early studies on mobility discussed models as simple as random mobility [11], which later generated the more complex concept of chaos enhanced mobility [12], [13], [14]. Other studies proposed analytic and/or parametric approaches with fixed pre-tuned parameters [15], [8], [9], [16], [17], [10], [18].…”

Section: Introductionmentioning

confidence: 99%

Survivable Networks via UAV Swarms Guided by Decentralized Real-Time Evolutionary Computation

Leu

Tang

2019

2019 IEEE Congress on Evolutionary Computation (CEC)

View full text Add to dashboard Cite

The survivable network concept refers to contexts where the wireless communication between ground agents needs to be maintained as much as possible at all times, regardless of any adverse conditions that may arise. In this paper we propose a nature-inspired approach to survivable networks, in which we bring together swarm intelligence and evolutionary computation. We use an on-line real-time Genetic Algorithm to optimize the movements of an UAV swarm towards maintaining communication between the ground agents. The proposed approach models the ground agents and the UAVs as boidsbased swarms, and optimizes the movement of the UAVs using different instances of the GA running independently on each UAV. The UAV coordination mechanism is an implicit one, embedded in the fitness function of the Genetic Algorithm instances. The behaviors of the individual UAVs emerge into an aggregated optimization of the overall network survivability. The results show that the proposed approach is able to maintain satisfactory network survivability levels regardless of the ground agents' movements, including for cases as complex as random walks.

show abstract

Coverage Optimization with Connectivity Preservation for UAV Swarms Applying Chaotic Dynamics

Cited by 24 publications

References 18 publications

A 3D Mobility Model for Autonomous Swarms of Collaborative UAVs

A 3D Mobility Model for Autonomous Swarms of Collaborative UAVs

A Mobility Model Based on Improved Artificial Potential Fields for Swarms of UAVs

Survivable Networks via UAV Swarms Guided by Decentralized Real-Time Evolutionary Computation

Contact Info

Product

Resources

About