Cooperative task scheduling for networked uninhabited air vehicles

Gil, A.E.; Passino, K.M.; Ganapathy, Sriram; Sparks, Andrew G.

doi:10.1109/taes.2008.4560207

Cited by 33 publications

(13 citation statements)

References 27 publications

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“…The major advantages of the multi-UAV network over its single-UAV counterpart can be summarized in terms of the networking viewpoint as well as the system viewpoint [6] [7]. Specifically, from the networking viewpoint:…”

Section: B Uav Network: Airborne Ad Hoc Networkmentioning

confidence: 99%

Taking Drones to the Next Level: Cooperative Distributed Unmanned-Aerial-Vehicular Networks for Small and Mini Drones

Wang

Jiang²,

Han

et al. 2017

IEEE Veh. Technol. Mag.

413

185

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Abstract-Unmanned Aerial Vehicles (UAV) have been widely used both in military and in civilian applications. However, the cooperation of small and mini drones in a network is capable of further improving both the performance and the coverage area of UAVs. Naturally, there are numerous new challenges to be solved before the wide-spread introduction of multi-UAV based heterogeneous Flying Ad Hoc Networks (FANET), including the formulation of a stable network structure. Meanwhile, an efficient gateway selection algorithm and management mechanism is required as well. On the other hand, the stability control of the hierarchical UAV network guarantees the efficient collaboration of the drones. In this article, we commence with surveying the FANET structure and its protocol architecture. Then, a variety of distributed gateway selection algorithms and cloudbased stability control mechanisms are addressed, complemented by a range of open challenges.Index Terms-Multi-UAV network system of small and mini drones, FANET network structure, distributed gateway selection algorithms, cloud-based stability control for collaboration and cooperation.

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Section: B Uav Network: Airborne Ad Hoc Networkmentioning

confidence: 99%

Taking Drones to the Next Level: Cooperative Distributed Unmanned-Aerial-Vehicular Networks for Small and Mini Drones

Wang

Jiang²,

Han

et al. 2017

IEEE Veh. Technol. Mag.

413

185

View full text Add to dashboard Cite

show abstract

“…Over the last one decade or so, computer-aided simulation of natural swarms has attracted a lot of research interest. It has been perceived through the efforts of the researchers that the functional principles from such biological swarms can be utilized in engineering for the development and control of multi-agent systems, such as the formation control of multi-robot systems [2,3], unmanned land, sea, or air vehicles [4,5], surveillance [6], rendezvous [7], coverage control on mobile sensing networks [8], and so on. Recently the concept of Computing Networks [9] has been developed to generalize swarm and neural architectures.…”

Section: Introductionmentioning

confidence: 99%

Chaotic patterns in the discrete-time dynamics of social foraging swarms with attractant–repellent profiles: an analysis

Das

2015

Nonlinear Dyn

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This article investigates the chaotic dynamical characteristics of a discrete-time social foraging swarm model in which the individuals move in a ddimensional space according to an attractant/repellent or a nutrient profile. The collective behavior of the swarm model results from the balance between interindividual interactions and the simultaneous interactions of the swarm members with their environment. Analysis undertaken in the paper indicates that chaos can be introduced into the system by tuning the parameters of the mutual attraction-repulsion function and the attractant-repellent profiles of the swarm dynamics [as given in Eq. (1)]. Ranges of different parameters to ensure sufficient condition for chaotic characteristics have been determined. Apart from chaos, stable and limit cyclic behaviors are also demonstrated for various parameter ranges. Effect of different attractant/repellent gradient profiles on the mean swarm trajectory in the presence of chaos has also been studied. Presence of chaos is determined by means of the Lyapunov exponent. Results obtained from the theoretical analysis are validated through comprehensive simulations.

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“…Flight path planning, trajectory tracking, collision and obstacle avoidance, synchronization between cooperative tasks [11], motion control [8], wireless communication, visual sensing, and task allocation which is described and discussed more in this paper, are some of hot topics in UAV research area. Note that most of these topics are also common in the autonomous collective robotics [11].…”

Section: Introductionmentioning

confidence: 99%

Discrete invasive weed optimization algorithm: application to cooperative multiple task assignment of UAVs

Ramezani

Hajimirsadeghi

Lucas

2009

Proceedings of the 48h IEEE Conference on Decision and Control (CDC) Held Jointly With 2009 28th Chinese Control Conference

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This paper presents a novel discrete population based stochastic optimization algorithm inspired from weed colonization. Its performance in a discrete benchmark, timecost trade-off (TCT) problem, is evaluated and compared with five other evolutionary algorithms. Also we use our proposed discrete invasive weed optimization (DIWO) algorithm for cooperative multiple task assignment of unmanned aerial vehicles (UAVs) and compare the solutions with those of genetic algorithms (GAs) which have shown satisfactory results in the previous works. UAV task assignment problem is of great interest among researchers and many deterministic and stochastic methods have been devised to come up with the problem. Monte Carlo simulations show successful results that verify better performance of DIWO compared to GAs in both optimality of the solutions and computational time.

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Cooperative task scheduling for networked uninhabited air vehicles

Cited by 33 publications

References 27 publications

Taking Drones to the Next Level: Cooperative Distributed Unmanned-Aerial-Vehicular Networks for Small and Mini Drones

Taking Drones to the Next Level: Cooperative Distributed Unmanned-Aerial-Vehicular Networks for Small and Mini Drones

Chaotic patterns in the discrete-time dynamics of social foraging swarms with attractant–repellent profiles: an analysis

Discrete invasive weed optimization algorithm: application to cooperative multiple task assignment of UAVs

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