Practical formation control of swarm robots using mobile agents

Yajima, Hideaki; Oikawa, Ryotaro; Takimoto, Munehiro; Kambayashi, Yahiko

doi:10.1109/intellisys.2017.8324251

Cited by 5 publications

(3 citation statements)

References 15 publications

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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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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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“…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: 96%

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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“…This paper is an extension of the work originally presented in SAI Intelligent Systems Conference 2017. 1 The work presented our basic idea of multiple software mobile agents that mimic the behaviors of ants and in°uences of pheromones, and demonstrated that the idea is applicable to compose two-dimensional formations of multiple mobile robots. In this paper, we have extended the work to show that our system can direct mobile robots to compose three-dimensional polyhedrons.…”

Section: Introductionmentioning

confidence: 99%

Formation Control of Swarm Robots Using Mobile Agents

Kambayashi

Yajima

Shyoji

et al. 2019

Vietnam J. Comp. Sci.

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In this paper, we propose an algorithm for controlling a fleet of swarm robots that construct three-dimensional forms. The swarm robots coordinate with each other through network communication, and compose formations such as polyhedrons presented as spherical coordinates. Our control algorithm achieves communication through mobile software agents, which introduce control programs to robots that initially have no information about the formation. Mobile software agents are autonomous objects that can migrate from one robot to other robots through a communication network and can deliver control programs as they are needed. We have made our swarm robot system to mimic the behaviors of the leafcutter ants. A leafcutter ant is a typical social insect and uses pheromone for communication. In our robot control system, we have implemented ants and pheromones as mobile software agents. We call the mobile software agents that drive the mobile robots as ant agents, and call the other agents that provide communication as pheromone agents. The ant agents drive the swarm robots to locations identified by the pheromone agents. Each ant agent has only partial information. There is no need for either a central control or an agent that has the entire design of the formation. In order to diffuse the partial information among the neighboring robots, each ant agent generates pheromone agents and dispatches them to the surrounding robots. Dispatched pheromone agent looks for a proper ant agent to influence towards a desired relative location. It is the ant agent that actually drives the robot by following the guidance of the pheromone agent, and the collective actions of ant agents and pheromone agents achieve the composition of the objective formation. We have implemented a simulator based on our algorithm and conducted numerical experiments. The results demonstrate that our mobile robot control system is feasible and efficient in practice in practical situations.

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Practical formation control of swarm robots using mobile agents

Cited by 5 publications

References 15 publications

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

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

Distributed Swarm Formation Using Mobile Agents

Formation Control of Swarm Robots Using Mobile Agents

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