Using Formal Methods and Agent-Oriented Software Engineering for Modeling NASA Swarm-Based Systems

Rouff, Christopher; Hinchey, Michael G.; Peña, Joaquín; Ruiz–Cortés, Antonio

doi:10.1109/sis.2007.367958

Cited by 10 publications

(9 citation statements)

References 41 publications

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“…In [26], Rouff et al compare a number of formal methods for representing and verifying part of the Autonomous Nano Technology Swarm (ANTS) mission aimed at sending small swarms of spacecraft to study the asteroid belt. As a result of this, in [27] four formal methods were selected, namely Communicating Sequential Processes (CSP), Weighted Synchronous Calculus of Communicating Systems (WSCCS), X-Machines and Unity Logic. These are proposed for use alongside techniques from agent-oriented software engineering.…”

Section: Related Workmentioning

confidence: 99%

Towards temporal verification of swarm robotic systems

Dixon

Winfield

Fisher

et al. 2012

Robotics and Autonomous Systems

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A robot swarm is a collection of simple robots designed to work together to carry out some task. Such swarms rely on: the simplicity of the individual robots; the fault tolerance inherent in having a large population of identical robots; and the self-organised behaviour of the swarm as a whole. Although robot swarms present an attractive solution to demanding real-world applications, designing individual control algorithms that can guarantee the required global behaviour is a difficult problem. In this paper we assess and apply the use of formal verification techniques for analysing the emergent behaviours of robotic swarms. These techniques, based on the automated analysis of systems using temporal logics, allow us to analyse whether all possible behaviours within the robot swarm conform to some required specification. In particular, we apply model-checking, an automated and exhaustive algorithmic technique, to check whether temporal properties are satisfied on all the possible behaviours of the system. We target a particular swarm control algorithm that has been tested in real robotic swarms, and show how automated temporal analysis can help to refine and analyse such an algorithm.

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Section: Related Workmentioning

confidence: 99%

Towards temporal verification of swarm robotic systems

Dixon

Winfield

Fisher

et al. 2012

Robotics and Autonomous Systems

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show abstract

“…In [39], Rouff et al compare a number of formal methods for representing and verifying part of the Autonomous Nano Technology Swarm (ANTS) mission aimed at sending small swarms of spacecraft to study the asteroid belt. As a result of this, in [38] four formal methods were selected, namely Communicating Sequential Processes (CSP), Weighted Synchronous Calculus of Communicating Systems (WSCCS), X-Machines and Unity Logic. These are proposed for use alongside techniques from agent-oriented software engineering.…”

Section: Formal Verification Of Robot Swarmsmentioning

confidence: 99%

Analysing robot swarm behaviour via probabilistic model checking

Konur¹,

Dixon²,

Fisher³

2012

Robotics and Autonomous Systems

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An alternative to deploying a single robot of high complexity can be to utilize robot swarms comprising large numbers of identical, and much simpler, robots. Such swarms have been shown to be adaptable, fault-tolerant and widely applicable. However, designing individual robot algorithms to ensure effective and correct overall swarm behaviour is actually very difficult. While mechanisms for assessing the effectiveness of any swarm algorithm before deployment are essential, such mechanisms have traditionally involved either computational simulations of swarm behaviour, or experiments with robot swarms themselves. However, such simulations or experiments cannot, by their nature, analyse all possible swarm behaviours. In this paper, we will develop and apply the use of automated probabilistic formal verification techniques to robot swarms, involving an exhaustive mathematical analysis, in order to assess whether swarms will indeed behave as required. In particular we consider a foraging robot scenario to which we apply probabilistic model checking.

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“…A key feature of CubeSats is their potential to form a collaborative swarm that covers a large geographical area. The resulting swarm allows CubeSats to have a longer contact time with ground stations and enables them to collectively take multiple measurements; consequently, they allow users to conduct comprehensive assessments of a given geographical region that otherwise would be impossible with a single conventional satellite [4,5].…”

Section: Introductionmentioning

confidence: 99%

A high gain S-band slot antenna with MSS for CubeSat

et al. 2018

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Cube satellites, aka CubeSats, are a class of tiny satellites that have become popular for space programs. This is because they can be built relatively cheaply using commercial off-the-shelf components. Moreover, CubeSats can communicate with each other, and assemble into swarms to carry out different functions: e.g., wide area measurements and sensing. Swarms of CubeSats also have the effect of increasing the contact period with ground stations allowing for a longer communications window. These capabilities require CubeSats to be equipped with an efficient, high gain, small antenna to facilitate cross-link or intersatellite communications. Henceforth, this paper presents a high gain coplanar waveguide (CPW)-fed slot antenna for CubeSats. A key feature is the use of a metasurface superstrate structure (MSS) to significantly improve gain and reduce back-lobe emissions. This also has the advantage of minimizing interference to components inside a CubeSat. We have comprehensively evaluated the antenna using the high-frequency simulator structure (HFSS) as well as a carrying out testing on a 3 U (10 x 10 x 30 cm3) CubeSat platform. We have studied the effect of MSS element sets and their position and the effect of a 3-U CubeSat body on the performance of the proposed antenna. The experimental results confirm that our antenna achieves a return loss of 21.5 dB and a fractional impedance bandwidth (BW) of 55.91% with S11 ≤ 10 dB and has a simulated and measured gains of 9.71 and 8.8 dBi respectively at the desired frequency of 2.45 GHz. In contrast, amongst all previous S-band planar antennas that are suitable for CubeSats, the best gain is only 5.96 dB at 2.45 GHz.

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Using Formal Methods and Agent-Oriented Software Engineering for Modeling NASA Swarm-Based Systems

Cited by 10 publications

References 41 publications

Towards temporal verification of swarm robotic systems

Towards temporal verification of swarm robotic systems

Analysing robot swarm behaviour via probabilistic model checking

A high gain S-band slot antenna with MSS for CubeSat

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