JACIII Vol.22 No.5 pp. 621-628
doi: 10.20965/jaciii.2018.p0621


Evolutionary Acquisition of Autonomous Specialization in a Path-Formation Task of a Robotic Swarm

Motoaki Hiraga*, Toshiyuki Yasuda**, and Kazuhiro Ohkura*

*Graduate School of Engineering, Hiroshima University
1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8527, Japan

**Graduate School of Science and Engineering, University of Toyama
3190 Gofuku, Toyama 930-8555, Japan

October 26, 2017
May 21, 2018
September 20, 2018
evolutionary robotics, task allocation, division of labor, task specialization
Evolutionary Acquisition of Autonomous Specialization in a Path-Formation Task of a Robotic Swarm

Settings of the path-formation task

Task allocation is an important concept not only in biological systems but also in artificial systems. This paper reports a case study of autonomous task allocation behavior in an evolutionary robotic swarm. We address a path-formation task that is a fundamental task in the field of swarm robotics. This task aims to generate the collective path that connects two different locations by using many simple robots. Each robot has a limited sensing ability with distance sensors, a ground sensor, and a coarse-grained omnidirectional camera to perceive its local environment and the limited actuators composed of two colored LEDs and two-wheeled motors. Our objective is to develop a robotic swarm with autonomous specialization behavior from scratch, by exclusively implementing a homogeneous evolving artificial neural network controller for the robots to discuss the importance of embodiment that is the source of congestion. Computer simulations demonstrate the adaptive collective behavior that emerged in a robotic swarm with various swarm sizes and confirm the feasibility of autonomous task allocation for managing congestion in larger swarm sizes.

Cite this article as:
M. Hiraga, T. Yasuda, and K. Ohkura, “Evolutionary Acquisition of Autonomous Specialization in a Path-Formation Task of a Robotic Swarm,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.5, pp. 621-628, 2018.
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Last updated on Oct. 23, 2018