Modeling and Flocking Consensus Analysis for Large-Scale UAV Swarms

Li, Bing; Li, Jie; Huang, Ke-Wei

doi:10.1155/2013/368369

Cited by 12 publications

(13 citation statements)

References 26 publications

(27 reference statements)

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“…In essence, swarm theory refers to individuals selforganizing to work collectively to accomplish goals. Swarm theory relates to both natural and artificial life, and mathematicians have studied the organization of ant colonies (Dorigo et al 1999) and flocking behavior of birds and insects (Li et al 2013), in an attempt to understand this phenomenon. Swarm theory is already being used with unmanned autonomous vehicles for first response to disasters, investigating potentially dangerous situations, search and rescue, and for military purposes (http://bit.ly/1Pel9Qz).…”

Section: Bio-loggers: Recording the Movement Of Animalsmentioning

confidence: 99%

Futurecasting ecological research: the rise of technoecology

et al. 2018

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Abstract. Increasingly complex research questions and global challenges (e.g., climate change and biodiversity loss) are driving rapid development, refinement, and uses of technology in ecology. This trend is spawning a distinct sub-discipline, here termed "technoecology." We highlight recent ground-breaking and transformative technological advances for studying species and environments: bio-batteries, lowpower and long-range telemetry, the Internet of things, swarm theory, 3D printing, mapping molecular movement, and low-power computers. These technologies have the potential to revolutionize ecology by providing "next-generation" ecological data, particularly when integrated with each other, and in doing so could be applied to address a diverse range of requirements (e.g., pest and wildlife management, informing environmental policy and decision making). Critical to technoecology's rate of advancement and uptake by ecologists and environmental managers will be fostering increased interdisciplinary collaboration. Ideally, such partnerships will span the conception, implementation, and enhancement phases of ideas, bridging the university, public, and private sectors.

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Section: Bio-loggers: Recording the Movement Of Animalsmentioning

confidence: 99%

Futurecasting ecological research: the rise of technoecology

et al. 2018

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“…(1) Judge the physical constraints of LAV [20], if the LAV meets physical characteristics and communication capability, it will form the Group LAVs, and perform a search mission;…”

Section: Problem Formulation and Analysis 21 The Behaviors In The Prmentioning

confidence: 99%

“…So the transition probability from state η to state n during time interval [t,t + τ] is τW t (η, n). According to [20], the primary equation model as follows:…”

Section: Probability Analysis Based On State Transformationmentioning

confidence: 99%

Research on the Dynamic Evolution Behavior of Group Loitering Air Vehicles

Ning

2016

Applied Mathematics and Nonlinear Sciences

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In this paper we focus on integrated Reconnaissance/Strike LAV, in order to reveal the evolution regularity when group LAVs combats cooperatively. The evolution of cooperative behavior of group LAVs, which is described with finite state machine, can be regarded as a conversion process of a LAV in different task states, using the rate equation for probability analysis. Then based on the missions of integration of reconnaissance, attack, and damage effectiveness evaluation, we build the model of finite state machine based on behavior state transition. Solved with Runge-Kutta method. We can analyze how the key technology quota of LAV impact on the operational effectiveness of Group LAVs.the fractional order control approach.

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“…Compared with other intelligent algorithms such as GA, ACO, PSO, this algorithm has a better performance in searching the optimal solution. In our pre-work [21]- [23], we modeled, analyzed and researched the related content of LAV swarm cooperative combat. The LAVs cooperative combat needs real-time or near real-time path planning.…”

Section: Introductionmentioning

confidence: 99%

LAV Path Planning by Enhanced Fireworks Algorithm on Prior Knowledge

Ning

YeHong

2016

Applied Mathematics and Nonlinear Sciences

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Path planning plays an extremely important role in the design of LAVs (Loitering Air Vehicles) to accomplish the air combat task fleetly and reliably. The planned path should ensure that LAVs reach the destination along the optimal path with minimum probability of being found and minimal consumed fuel. Traditional methods tend to find local best solutions due to the large search space. So it takes a lot of time and consumes a lot of computing resources. In this paper, a new young intelligent algorithm-fireworks algorithm is introduced, and EFWA (enhanced fireworks algorithm)-its enhanced version is used to find the optimal solution. At the same time, the battlefield prior knowledge is fully utilized to predict the existence space of the potential optimal trajectory. Greatly the search space reduced, plan planning efficiency is significantly improved. Path planning method effectiveness in this paper has further been improved compared with FACPSO. Moreover, the EFWA on prior knowledge performs well on the application of dynamic path planning when the threats cruise randomly than FAC-PSO.

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Modeling and Flocking Consensus Analysis for Large-Scale UAV Swarms

Cited by 12 publications

References 26 publications

Futurecasting ecological research: the rise of technoecology

Futurecasting ecological research: the rise of technoecology

Research on the Dynamic Evolution Behavior of Group Loitering Air Vehicles

LAV Path Planning by Enhanced Fireworks Algorithm on Prior Knowledge

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