Research Papers

Bio-Inspired Coalition Formation Algorithms for Multirobot Systems

[+] Author and Article Information
Binsen Qian

Department of Mechanical and
Aerospace Engineering,
University of California at Davis,
Davis, CA 95616
e-mail: bqian@ucdavis.edu

Harry H. Cheng

Fellow ASME
Integration Engineering Laboratory,
Department of Mechanical and Aerospace
University of California at Davis,
Davis, CA 95616
e-mail: hhcheng@ucdavis.edu

1Corresponding author.

Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received August 21, 2015; final manuscript received February 6, 2018; published online April 26, 2018. Assoc. Editor: Bahram Ravani.

J. Comput. Inf. Sci. Eng 18(2), 021010 (Apr 26, 2018) (8 pages) Paper No: JCISE-15-1272; doi: 10.1115/1.4039638 History: Received August 21, 2015; Revised February 06, 2018

This paper presents two bio-inspired algorithms for coalition formation of multiple modular robot systems. An effective and efficient coalition formation system can help modular robot system take full advantage of reconfigurability of modular robots. In this paper, the multirobot coalition formation problem is illustrated and a mathematical model for the problem is described. Two bio-inspired algorithms, ant-colony algorithm (ACA) and genetic algorithm (GA), are introduced for solving the mathematical model. With the two algorithms, it is able to form a large number of robots into many different groups for a variety of applications, such as parallel performance of multiple tasks by multiple teams of robots. The paper compares the efficiency and effectiveness of two algorithms for solving the presented problem with case study. The results for the comparison study are analyzed and discussed. Also, the implementation details of the simulation and experiment using ACA are presented in the paper.

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Fig. 1

An example illustrates the coalition formation problem: (a) two tasks and five robots in the working area, (b) three robots are selected for one task and two for the other, and (c) the robots form a group around the task

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Fig. 2

Programming architecture of ACA

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Fig. 3

Programming architecture of canonical GA

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Fig. 4

Experimental layouts: (a) experimental layout with four tasks, (b) experimental layout with eight tasks, and (c) experimental layout with 16 tasks

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Fig. 5

Distance distributions for three scenarios: (a) distance distribution for 400 times run of scenario with four task, (b) distance distribution for 400 times run of scenario with eight task, and (c) distance distribution for 400 times run of scenario with 16 task

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Fig. 6

Average execution time of three scenarios for two algorithms

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Fig. 7

The virtual and hardware Linkbot: (a) the mobile configuration of a Linkbot and (b) the virtual Linkbot in RoboSim

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Fig. 8

A sample code for controlling robot in Ch

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Fig. 9

The setup for simulation and experiment: (a) the layout for simulation and experiments and (b) The initial layout of Linkbots

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Fig. 10

The simulation and experiment result: (a) an example animation generated from RoboSim and (b) final positions of Linkbots in four groups



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