Distributed formation control for swarm robots using mobile agents

Oikawa, Ryotaro; Takimoto, Munehiro; Kambayashi, Yahiko

doi:10.1109/saci.2015.7208181

Cited by 17 publications

(10 citation statements)

References 14 publications

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“…In order to measure quantitatively the degree of correspondence with the objective ideal formation, we calculated the average distance D as defined in Eq. (9). We calculated the barycenter for both actual and ideal points, calculating all relative coordinate of robots from the barycenter.…”

Section: Resultsmentioning

confidence: 99%

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Distributed Swarm Formation Using Mobile Agents

Kambayashi¹,

Oikawa²,

Takimoto³

2019

Multi-Agent Systems - Control Spectrum

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This chapter presents decentralized control algorithms for composing formations of swarm robots. The robots are connected by communication networks. They initially do not have control program to compose formations. Control programs that implement our algorithm are introduced later from outside as mobile software agents. Our controlling algorithm is based on the pheromone communication of social insects such as ants. We have implemented the ant and the pheromone as mobile software agents. Ant agents control the robots. Each ant agent has partial information about the formation it is supposed to compose. The partial information consists of relative locations with neighbor robots that are cooperatively composing the formation. Once the ant agent detects an idle robot, it occupies that robot and generates the pheromone agent to attract other ant agents to the location for neighbor robots. Then the pheromone agent repeatedly migrates to other robots to diffuse attracting information. Once the pheromone agent reaches the robot with an ant agent, the ant agent migrates to the robot closest to the location pointed by the pheromone agent and then drives the robot to the location. We have implemented simulators based on our algorithm and conducted experiments to demonstrate the feasibility of our approach.

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Section: Resultsmentioning

confidence: 99%

“…The contents of this chapter are partially presented at the 10th Jubilee IEEE International Symposium on Applied Computational Intelligence and Informatics, the 13th European Conference on Multi-Agent Systems, and the Intelligent Systems Conference 2017. They are found in [8][9][10][11].…”

Section: Introductionmentioning

confidence: 98%

Distributed Swarm Formation Using Mobile Agents

Kambayashi¹,

Oikawa²,

Takimoto³

2019

Multi-Agent Systems - Control Spectrum

Self Cite

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“…ACO was used to address the reformation problem in the multi-agent system in which recursion algorithm was proposed to reduce the distance travelled by each agent during reformation process [110]. Moreover, ACO was implemented for the formation control of swarm robots by implementing ants and pheromone level as software agents [111][112][113]. Here, the first agent calculates the location of the conceptual barycentre of the formation and all the locations for the robots to occupy, whereas the second agent physically drives the robots to the locations to compose the formation.…”

Section: Swarm Intelligence and Formation Controlmentioning

confidence: 99%

Formation Control for a Fleet of Autonomous Ground Vehicles: A Survey

Soni

2018

Robotics

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Autonomous/unmanned driving is the major state-of-the-art step that has a potential to fundamentally transform the mobility of individuals and goods. At present, most of the developments target standalone autonomous vehicles, which can sense the surroundings and control the vehicle based on this perception, with limited or no driver intervention. This paper focuses on the next step in autonomous vehicle research, which is the collaboration between autonomous vehicles, mainly vehicle formation control or vehicle platooning. To gain a deeper understanding in this area, a large number of the existing published papers have been reviewed systemically. In other words, many distributed and decentralized approaches of vehicle formation control are studied and their implementations are discussed. Finally, both technical and implementation challenges for formation control are summarized.

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“…The ability to locally measure the relative distances and bearings of neighboring robots is fundamental for collective behavior in swarm robotics. [98][99][100][101] For mROBerTOs, the swarm-sensing module, with IR emitters and detectors, was specifically designed and implemented for this purpose. mROBerTO sends out modulated IR signals via its IR emitters, achieved using the PWM feature on the ATmega328P microcontroller.…”

Section: Appendix C: Sensing and Communication For Collective Behaviormentioning

confidence: 99%

Design and implementation of a millirobot for swarm studies –mROBerTO

Kim¹,

Kashino²,

Colaco³

et al. 2018

Robotica

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SUMMARYThe use of millirobots, particularly in swarm studies, would enable researchers to verify their proposed autonomous cooperative behavior algorithms under realistic conditions with a large number of agents. While multiple designs for such robots have been proposed, they, typically, require custom-made components, which make replication and manufacturing difficult, and, mostly, employ non-modular integral designs. Furthermore, these robots' proposed small sizes tend to limit sensory perception capabilities and operational time. Some have resolved few of the above issues through the use of extensions that, unfortunately, add to their size.In contribution to the pertinent field, thus, a novel millirobot with an open-source design, addressing the above concerns, is presented in this paper. Our proposed millirobot has a modular design and uses easy to source, off-the-shelf components. Themilli-robot-Toronto (mROBerTO) also includes a variety of sensors and has a 16 × 16 mm2footprint.mROBerTO's wireless communication capabilities include ANT™, Bluetooth Smart, or both simultaneously. Data-processing is handled by an ARM processor with 256 KB of flash memory. Additionally, the sensing modules allow for extending or changing the robot's perception capabilities without adding to the robot's size. For example, the swarm-sensing module, designed to facilitate swarm studies, allows for measuring proximity and bearing to neighboring robots and performing local communications.Extensive experiments, some of which are presented herein, have illustrated the capability ofmROBerTOunits for use in implementing a variety of commonly proposed swarm algorithms.

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Distributed formation control for swarm robots using mobile agents

Cited by 17 publications

References 14 publications

Distributed Swarm Formation Using Mobile Agents

Distributed Swarm Formation Using Mobile Agents

Formation Control for a Fleet of Autonomous Ground Vehicles: A Survey

Design and implementation of a millirobot for swarm studies –mROBerTO

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