Swarm Robots Communication-base Mobile Ad-Hoc Network (MANET)

Tutuko, Bambang; Nurmaini, Siti

doi:10.11591/eecsi.v1.361

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…A key component of swarms is local communication between neighboring agents. In robotic swarms, this communication is typically implemented using wireless technologies, for example WiFi (Hauert et al, 2010), Zigbee (Cianci et al, 2006), or other radio based technologies (Li et al, 2009b;Tutuko and Nurmaini, 2014). While other systems have been proposed using light (Rubenstein et al, 2014;Roberts et al, 2009;Trenkwalder et al, 2020), or sensory readings such as vision (Floreano et al, 2007;Garattoni and Birattari, 2018).…”

Section: Introductionmentioning

confidence: 99%

Morphological communication for swarms

Sharma¹,

Hauert²,

Hauser³

2020

The 2020 Conference on Artificial Life

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Robotic swarms rely on local communication between agents to exhibit cooperative emergent behaviours. Local communication is typically implemented with technologies that require dedicated electronics, that can be expensive and difficult to miniaturise or mass-produce. Computational resources are then needed to transform this information into a robot action following a set of rules, further limiting swarm lifetime (battery) and scalability. In this paper, we propose an alternative approach by using the concept of morphological computation (computation through morphology) for local communication in swarms. In such a swarm, local communication is implemented as simple mass-spring-damper systems between agents, instead of electronics. We test this approach in a simple scenario where a swarm has to squeeze through a narrow gap while floating on water. We tested different types of swarms (with different levels of control) and measured their average performance and energy efficiency. We found that by offloading the majority of communication and information processing to the morphology, swarms can exhibit interesting, emergent, cooperative behaviour to solve the given task.

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Section: Introductionmentioning

confidence: 99%

Morphological communication for swarms

Sharma¹,

Hauert²,

Hauser³

2020

The 2020 Conference on Artificial Life

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“…In swarm robotics, the merits of MANETs have been recognized. Two types of MANETs are commonly deployedradio-based (Li et al 2009;Tutuko and Nurmaini 2014) and optical (Di Caro et al 2009;Gutiérrez et al 2009a;Rubenstein et al 2012) ones. The underlying technologies have their advantages and disadvantages:…”

Section: Introductionmentioning

confidence: 99%

SwarmCom: an infra-red-based mobile ad-hoc network for severely constrained robots

et al. 2019

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Swarm robotics investigates groups of relatively simple robots that use decentralized control to achieve a common goal. While the robots of many swarm systems communicate via optical links, the underlying channels and their impact on swarm performance are poorly understood. This paper models the optical channel of a widely used robotic platform, the e-puck. It proposes SwarmCom, a mobile ad-hoc network for mobile robots. SwarmCom has a detector that, with the help of the channel model, was designed to adapt to the environment and nearby robots. Experiments with groups of up to 30 physical e-pucks show that (i) SwarmCom outperforms the state-of-the-art infra-red communication software-libIrcom-in range (up to 3 times further), bit error rate (between 50 and 63% lower), or throughput (up to 8 times higher) and that (ii) the maximum number of communication channels per robot is relatively low, which limits the load per robot even for high-density swarms. Using channel coding, the bit error rate can be further reduced at the expense of throughput. SwarmCom could have profound implications for swarm robotics, contributing to system understanding and reproducibility, while paving the way for novel applications. Keywords Swarm robotics • MANET • Infra-red communication • Channel model • e-puck • libIrcom S.M. Trenkwalder is a recipient of a DOC Fellowship of the Austrian Academy of Sciences. This is one of the several papers published in Autonomous Robots comprising Special Issue on Robot Communication Challenges: Real-World Problems, Systems, and Methods.

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“…it has been successfully and widely used to control the motion of swarm robots [19]. This technique can shorten the time and refine the movement of robots in a very complex system, so that it can avoid obstacles [13][14], [20]. Fuzzy logic is one of the most useful methods of computational intelligence that offers the efficiency and simplicity [21][22][23].…”

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confidence: 99%

Analysis on swarm robot coordination using fuzzy logic

Handayani¹,

Nurmaini²,

Yani³

et al. 2019

IJEECS

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In this paper, coordination among individual of swarm robot in communicating to maintain the safe distance between robots was analyzed. Each robot coordinates their movements to avoid obstacles and moving simultaneously. Evaluation of swarm robot performance is analyzed in this paper, namely: the coordination among robots to share information in safe distance determination. In controlling the coordination of motion, each robot has a sensor that provides several inputs about its surrounding environment. Fuzzy logic control in this paper allows uncertain input, and produces unlimited commands to control motion direction with speed settings according to environmental conditions. In this experiment, it is obtained that the size of the environment affects the coordination of robots.

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Swarm Robots Communication-base Mobile Ad-Hoc Network (MANET)

Cited by 3 publications

References 9 publications

Morphological communication for swarms

Morphological communication for swarms

SwarmCom: an infra-red-based mobile ad-hoc network for severely constrained robots

Analysis on swarm robot coordination using fuzzy logic

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