A concept of common reference object to the cooperative transportation of multiple mobile robots

Xin Yang, Keigo Watanabe, Kiyotaka Izumi

Research output: Contribution to journalArticle

Abstract

Aconcept named "common reference object" is proposed for a cooperative transportation using multiple nonholonomic mobile robots and a decentralized system is constructed to demonstrate the present concept. In this system, one agent acts as the leader which is able to plan and manipulate the omnidirectional motion of an object. Other agents referred to as followers cooperatively transport the object by keeping a constant position relative to the object. During the transportation operation, the leader robot can not only plan the motion of the object but also broadcast the local velocity of the object to other agents. Then, each follower receives such information and generates its own velocity in the local coordinate using a mapping process. In this paper, neural network (NN) and genetic algorithm (GA) are tested in identifying the mapping process. Simulation results show an acceptable performance of the present concept.

Original languageEnglish
Pages (from-to)209-221
Number of pages13
JournalInternational Journal of Knowledge-Based and Intelligent Engineering Systems
Volume9
Issue number3
Publication statusPublished - 2005
Externally publishedYes

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Mobile robots
Genetic algorithms
Robots
Neural networks

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Artificial Intelligence

Cite this

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AB - Aconcept named "common reference object" is proposed for a cooperative transportation using multiple nonholonomic mobile robots and a decentralized system is constructed to demonstrate the present concept. In this system, one agent acts as the leader which is able to plan and manipulate the omnidirectional motion of an object. Other agents referred to as followers cooperatively transport the object by keeping a constant position relative to the object. During the transportation operation, the leader robot can not only plan the motion of the object but also broadcast the local velocity of the object to other agents. Then, each follower receives such information and generates its own velocity in the local coordinate using a mapping process. In this paper, neural network (NN) and genetic algorithm (GA) are tested in identifying the mapping process. Simulation results show an acceptable performance of the present concept.

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