A Serialization Algorithm for Mobile Robots Using Mobile Agents with Distributed Ant Colony Clustering

Shintani, Munehiro; Lee, Shawn; Takimoto, Munehiro; Kambayashi, Yahiko

doi:10.1007/978-3-642-23851-2_27

Cited by 12 publications

(7 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The pheromone agent is generated by the ant agent when its driving robot reaches a cluster, and repeatedly migrates among robots for guiding other ant agents to drive robots to the cluster. Shintani et al have improved the mobile agent-based algorithm to serialize multiple robots while they are self-collected [5,6]. Our approaches are extensions of the mobile agent-based clustering.…”

Section: Background and Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Distributed Swarm Formation Using Mobile Agents

Kambayashi¹,

Oikawa²,

Takimoto³

2019

Multi-Agent Systems - Control Spectrum

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Background and Related Workmentioning

confidence: 99%

“…We have shown the advantages of mobile agents in robot systems for several applications. They are searching targets [1,2], transporting objects to a designated collection area [3], clustering robots [4], and serialization of robots [5,6].…”

Section: Introductionmentioning

confidence: 99%

Distributed Swarm Formation Using Mobile Agents

Kambayashi¹,

Oikawa²,

Takimoto³

2019

Multi-Agent Systems - Control Spectrum

Self Cite

View full text Add to dashboard Cite

show abstract

“…The pheromone agent is generated by the ant agent when its driving robot reaches a cluster, and repeatedly migrates to robots to guide ant agents to drive robots to the cluster. The mobile agent based algorithm has been improved to serialize collected robots (Shintani et al, 2011b;Shintani et al, 2011a) The applications of ants' behaviors to cooperative transportation has been proposed by Kube and Bonabeau (Kube and Bonabeau, 2000). It transports an object through the interplay of forces.…”

Section: Introductionmentioning

confidence: 99%

Cooperatively Transporting Unknown Objects using Mobile Agents

Takahashi

Takimoto

Kambayashi

2014

Proceedings of the 6th International Conference on Agents and Artificial Intelligence

View full text Add to dashboard Cite

This paper presents an algorithm for cooperatively transporting objects by multiple robots without any initial knowledge. The robots are connected by communication networks, and the controlling algorithm is based on the pheromone communication of social insects such as ants. Unlike traditional pheromone based cooperative transportation, we have implemented the pheromone as mobile software agents that control the mobile robots corresponding to the ants. The pheromone agent has the vector value pointing to its birth location inside, which is used to guide a robot to the birth location. Since the pheromone agent can diffuse with migrations between robots as well as a physical pheromone, it can attract other robots scattering in a work field to the birth location. Once the robot finds an object, it briefly pushes the object, measuring the degree of the inclination of the object. The robot generates a pheromone agent with the vector value to pusing point suitable for suppressing the inclination of the object. The process of the pushes and generations of pheromone agents enables the efficient transportation of the object. We have implemented a simulator based on our algorithm, and conducted experiments to demonstrate the feasibility of our approach.

show abstract

“…We have implemented several multi-robot systems such as cooperatively assemble themselves at energy-wise optimal locations (Kambayashi et al, 2012), and serialize themselves (Shintani et al, 2011). For all the multi-robot systems, we have designed and implemented multi-agent systems that control the robot systems.…”

Section: Introductionmentioning

confidence: 99%

Self-Optimizing Algorithms for Mobile Ad Hoc Networks based on Multiple Mobile Agents

Kambayashi

Shinohara

Takimoto

2014

Proceedings of the 6th International Conference on Agents and Artificial Intelligence

View full text Add to dashboard Cite

This paper presents algorithms that form optimal connecting configurations for Mobile Ad Hoc Networks (MANETs). MANET is a computer network that is dynamically formed by autonomous mobile nodes. Today, the communication network is one of the most important infrastructures. When it is lost by either natural or accidental disaster, the recovery of the communication network should be one of the first priorities. We are proposing a way of constructing an extemporized communication network on the spot by a herd of mobile robots that communicate by wireless link. The networks we are considering are formed by multiple relay robots; therefore the algorithms are naturally distributed ones and executed by the herd of relay robots. The relay robots move cooperatively but without any central control. In order to collect and to distribute enough information to coordinate the behaviours of participating relay robots, we employ mobile software agents that we have developed and succeeded in using many applications. There are a number of multi-robot systems that take advantage of MANET, and look for efficient use of relay robot while maintaining connectivity. Our study contributes this line of investigation. The numerical experiments show that our algorithms provide optimal configurations in certain cases.156 Kambayashi Y., Shinohara T. and Takimoto M.. Self-Optimizing Algorithms for Mobile Ad Hoc Networks based on Multiple Mobile Agents.

show abstract

A Serialization Algorithm for Mobile Robots Using Mobile Agents with Distributed Ant Colony Clustering

Cited by 12 publications

References 7 publications

Distributed Swarm Formation Using Mobile Agents

Distributed Swarm Formation Using Mobile Agents

Cooperatively Transporting Unknown Objects using Mobile Agents

Self-Optimizing Algorithms for Mobile Ad Hoc Networks based on Multiple Mobile Agents

Contact Info

Product

Resources

About