Target Tracking and Surrounding with Swarm Robots

Blázovics, László; Csorba, Kristóf; Forstner, Bertalan; Charaf, Hassan

doi:10.1109/ecbs.2012.41

Cited by 10 publications

(9 citation statements)

References 9 publications

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“…The initial position of swarm robots are shown in Table 4 that are given arbitrarily. In this simulation, the value of  is 0.45 and is less than the lower limit 0.59 of  computed according to the systems parameters listed in Table 1 and (27). However, the prey does not turn at each step, so it can also be hunted successfully.…”

Section: Special Casementioning

confidence: 96%

“…For the static non-convex and convex obstacles environment with satisfying the conditions of following barriers, the following barriers robots can avoid non-convex and convex obstacles safely as long as the following barriers robots can keep the latest positional information of the target (by the sense of themselves or communicating with others to get the latest positional information of the target), then as long as time step length  of the following barriers robots satisfies (26) or (27), and the hunting systems are stable.…”

Section: Special Casementioning

confidence: 99%

“…Therefore, for most instances the swarm robots can also hunt successfully when  is less than the lower limit given by (26) or (27).…”

mentioning

confidence: 99%

See 2 more Smart Citations

Hunting in Unknown Environments with Dynamic Deforming Obstacles by Swarm Robots

Zhang¹,

Zhang²,

Zhou³

et al. 2015

IJCA

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Section: Special Casementioning

confidence: 96%

Section: Special Casementioning

confidence: 99%

See 1 more Smart Citation

Hunting in Unknown Environments with Dynamic Deforming Obstacles by Swarm Robots

Zhang¹,

Zhang²,

Zhou³

et al. 2015

IJCA

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show abstract

“…In contrast with the patrolling, the second part of the main algorithm, the target interception, is a complex behaviour. Therefore in order to keep it easily understandable, we not just mixed the basic behaviours of Mataric [6] but we also used an additional basic behaviour, the Circulation, which was introduced in [10] and can be seen on Algorithm 1. This behaviour enables the robots to circulate around a given target on a predefined circular orbit.…”

Section: B Target Interceptionmentioning

confidence: 99%

On the benefits of cognitive infocommunication for mobile communication nodes using cooperative concepts

Blázovics

Forstner

Charaf

et al. 2013

2013 IEEE 4th International Conference on Cognitive Infocommunications (CogInfoCom)

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The distributed coordination of a group of mobile robots became a widely studied area in the last decades however the communication aided solutions became also popular research. In this paper we present the concept of cognitive swarm which enables to design faster and reliable cooperative groups by the use of cognitive infocommunication. We demonstrate the benefits of our new concept by a scenario in which a swarm of mobile robots had to guard a given area by intercepting eventual intruders. Therefore we introduce the area surveillance problem and we give both a baseline and a cognitive infocommuncation aided solution for that by the use of the basic behaviour set as fundamental. We show through simulation results that the proposed cognitive scheme can reduce the surrounding time by the factor of two leading to faster interception.

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“…Some research focuses on coating objects as an independent task under the name of target surrounding or boundary coverage. The techniques used in this context include stochastic robot behaviors [13,14], rule-based control mechanisms [15] and potential field-based approaches [16]. While the analytic techniques developed in swarm robotics are somewhat relevant to this work, many such systems assume more computational power and movement capabilities than the model studied in this work does.…”

Section: Related Workmentioning

confidence: 99%

On the Runtime of Universal Coating for Programmable Matter

Derakhshandeh

Gmyr

Porter

et al. 2016

Lecture Notes in Computer Science

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Abstract. Imagine coating buildings and bridges with smart particles (also coined smart paint) that monitor structural integrity and sense and report on traffic and wind loads, leading to technology that could do such inspection jobs faster and cheaper and increase safety at the same time. In this paper, we study the problem of uniformly coating objects of arbitrary shape in the context of self-organizing programmable matter, i.e., programmable matter which consists of simple computational elements called particles that can establish and release bonds and can actively move in a self-organized way. Particles are anonymous, have constant-size memory, and utilize only local interactions in order to coat an object. We continue the study of our Universal Coating algorithm by focusing on its runtime analysis, showing that our algorithm terminates within a linear number of rounds with high probability. We also present a matching linear lower bound that holds with high probability. We use this lower bound to show a linear lower bound on the competitive gap between fully local coating algorithms and coating algorithms that rely on global information, which implies that our algorithm is also optimal in a competitive sense. Simulation results show that the competitive ratio of our algorithm may be better than linear in practice.

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Target Tracking and Surrounding with Swarm Robots

Cited by 10 publications

References 9 publications

Hunting in Unknown Environments with Dynamic Deforming Obstacles by Swarm Robots

Hunting in Unknown Environments with Dynamic Deforming Obstacles by Swarm Robots

On the benefits of cognitive infocommunication for mobile communication nodes using cooperative concepts

On the Runtime of Universal Coating for Programmable Matter

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