Abstract

Automatic design is a promising approach to the design of control software for robot swarms. In an automatic design method, the design problem is cast into an optimization problem and is addressed using an optimization algorithm. In this article, we review studies in which automatic design methods are successfully applied. In particular, we focus our attention on how automatic methods are empirically assessed. An apparent issue that emerges from our review is that a solid, well- established, and consistently applied empirical practice is still missing. For example, studies that propose new methods and ideas do not typically provide any comparison with existing ones. We maintain that the lack of a proper empirical practice hinders the progress of the domain. In this article, we pursue two goals: we highlight the notable achievements in the automatic design of control software for robot swarms and we discuss the challenges to be overcome to establish a proper empirical practice for the domain.

Highlights

  • In swarm robotics, a large number of robots are deployed to accomplish a mission that is beyond the capabilities of a single robot (Dorigo et al, 2014)

  • We present a number of notable achievements in evolutionary swarm robotics

  • We discuss four main issues to be addressed in order to define an empirical practice that is appropriate for the automatic design of control software for robot swarms

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Summary

INTRODUCTION

A large number of robots are deployed to accomplish a mission that is beyond the capabilities of a single robot (Dorigo et al, 2014). The results achieved show that automatic design is a viable and promising approach to design the control software of robot swarms. We are convinced that the lack of a proper empirical practice hinders the progress of the research on the automatic design of control software for robot swarms. We contend that a wellestablished empirical practice that encourages comparisons would properly promote the best ideas proposed so far, would help to focus on promising directions, and would attract further researchers and investments to the domain.

ACHIEVEMENTS
Off-Line Methods
On-Line Methods
CHALLENGES
Reference Model
Precise Definition
Benchmarks
Robot Experiments
CONCLUSION
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