Consensus-based data sharing for large-scale aerial swarm coordination in lossy communications environments

Davis, Duane; Chung, Timothy H.; Clement, Michael R.; Day, Michael A.

doi:10.1109/iros.2016.7759559

Cited by 18 publications

(20 citation statements)

References 25 publications

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“…Several recent studies proposed the use of global control mechanisms over swarms of uavs in order to control their actions as they accomplish a given task. For example, centralized mission planners to distribute tasks to uavs are suggested in [35], whereas a global coordination among swarm members is discussed in [10]. A pre-planned 3d distribution of multiple uavs is performed to optimize power used by each aircraft in [2].…”

Section: Swarms Of Uavsmentioning

confidence: 99%

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Artificial intelligence and game theory controlled autonomous UAV swarms

Kusyk

Samoylov

et al. 2020

Evol. Intel.

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Autonomous unmanned aerial vehicles (uavs) operating as a swarm can be deployed in austere environments, where cyber electromagnetic activities often require speedy and dynamic adjustments to swarm operations. Use of central controllers, uav synchronization mechanisms or pre-planned set of actions to control a swarm in such deployments would hinder its ability to deliver expected services. We introduce artificial intelligence and game theory based flight control algorithms to be run by each autonomous uav to determine its actions in near real-time, while relying only on local spatial, temporal and electromagnetic (em) information. Each uav using our flight control algorithms positions itself such that the swarm maintains mobile ad-hoc network (manet) connectivity and uniform asset distribution over an area of interest. Typical tasks for swarms using our algorithms include detection, localization and tracking of mobile em transmitters. We present a formal analysis showing that our algorithms can guide a swarm to maintain a connected manet, promote a uniform network spreading, while avoiding overcrowding with other swarm members. We also prove that they maintain manet connectivity and, at the same time, they can lead a swarm of autonomous uavs to follow or avoid an em transmitter. Simulation experiments in opnet modeler verify the results of formal analysis that our algorithms are capable of providing an adequate area coverage over a mobile em source and maintain manet connectivity. These algorithms are good candidates for civilian and military applications that require agile responses to the changes in dynamic environments for tasks such as detection, localization and tracking mobile em transmitters.

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Section: Swarms Of Uavsmentioning

confidence: 99%

“…Similarly, weight k (t, p i ) in Eqs. (10) and (11) incorporates changes in em landscape for frequency f k at time t with RSS k are the signal strength measurements for f k . Assignment of uavs to each em frequency f k is a different class of problem and is beyond the scope of this paper.…”

Section: Multiple Em Transmittersmentioning

confidence: 99%

Artificial intelligence and game theory controlled autonomous UAV swarms

Kusyk

Samoylov

et al. 2020

Evol. Intel.

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“…From aggregation, more complex patterns and formations such as flocking can be assembled. Consensus is a typical method for achieving a collective decision among the swarm …”

Section: Related Workmentioning

confidence: 99%

A mission‐based architecture for swarm unmanned systems

Giles

Giammarco

2019

Systems Engineering

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This research applies a mission engineering approach with model‐based systems engineering foundations to formalize a swarm unmanned system design methodology and architecture. The architectural framework and methodology herein presented extend current swarm system design methods, which are primarily bottom‐up approaches focused on the behavior of individual agents. We introduce a top‐down, hierarchical approach with an overarching mission decomposed into phases, tactics, plays, and algorithms. Three unmanned aerial vehicle swarm case studies (one of which is discussed in this paper) are used to demonstrate the approach and its effectiveness in formalizing a mission‐based framework of common patterns in swarm missions that promote architecture reusability.

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“…The swarm-oriented programming language Buzz allows for an easier manipulation of the consensus strategy [Pinciroli and Beltrame (2016b)]. The work of [Davis et al (2016)] proposed a shared table to reach consensus on the state of a swarm of UAVs. Their approach uses a single list to which each robot appends its own value, and the entries on the robots get updated in a query-and-response fashion.…”

Section: Consensusmentioning

confidence: 99%

SOUL: data sharing for robot swarms

et al. 2019

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Interconnected devices and mobile robotic systems are increasingly present in many real-life scenarios. To be effective, these systems need to collect large amounts of data from their environment, and often these data need to be aggregated, shared, and distributed. Generally, multi-robot systems share state information and commands through a communication channel, but this is often too limited in many contexts (e.g. sharing large 3D maps). This paper introduces SOUL (Swarm-Oriented Upload of Large data), a mechanism that allows members of a fully distributed system to share large amounts of data with their peers. We leverage a BitTorrent-like strategy to share data in smaller chunks, or datagrams, with policies and bidding strategies that minimize reconstruction time. We performed extensive simulations to study the properties of the system and to demonstrate its scalability. We also report experiments on real robots with two realistic deployment scenarios: searching for objects in a scene, and replacing the whole identity of a defective robot.

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Consensus-based data sharing for large-scale aerial swarm coordination in lossy communications environments

Cited by 18 publications

References 25 publications

Artificial intelligence and game theory controlled autonomous UAV swarms

Artificial intelligence and game theory controlled autonomous UAV swarms

A mission‐based architecture for swarm unmanned systems

SOUL: data sharing for robot swarms

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