Optimization-based Design of Robot Swarms


Swarm robotics is a promising approach to coordinating large groups of autonomous robots. Unfortunately, the lack of a general methodology for designing collective behaviors for robot swarms hinders its real-world application.

An important share of the research in swarm robotics has been dedicated to optimization-based design. In optimization-based design, the mission to be accomplished by the swarm is specified by defining a performance measure, a function that evaluates the extent to which the swarm attains the goals and/or satisfies the constraints of the mission. The design problem is formulated as an optimization problem: the possible individual behaviours are the search space explored by an optimization algorithm that maximises the aforementioned performance measure

It is our contention that two approaches to optimization-based design should be disentangled: semi-automatic design and automatic (or, more explicitly, fully automatic) design.

In semi-automatic design, a human designer operates an optimization algorithm as its main design tool. Semi-automatic design is an iterative process in which the designer, guided by their intuition and experience, instantiates a first optimization process, evaluates the behavior generated, modifies the optimization process on the basis of the results observed… these steps are repeated until the designer is satisfied with the behavior obtained and/or feels that it cannot be improved any further. All in all, semi-automatic design relies on an optimization algorithm, but features a human designer in the loop. In automatic design, the optimization process is performed in a fully automatic way and does not provide for any per-mission intervention of a human designer.

In our vision, both semi-automatic and automatic design are relevant to the development of swarm robotics; they will occupy two different niches and will address different contexts of application. Semi-automatic design is appealing to handle an individual, complex design problem for a specific mission that would be too difficult or time-consuming to be solved manually. On the other hand, automatic design is appealing when a design process is to be executed repeatedly on different missions belonging to a given class and it is impossible, impractical, or economically unfeasible that a human designer performs, supervises, or checks the design process itself.

Disentangling semi-automatic and automatic design is crucial to properly frame the future research. The clear understanding of the specificities of semi-automatic and automatic design will allow the community to properly state the relevant research questions and to define appropriate experimental protocols to address them. It will also contribute to set correct and realistic expectations on what each of the two approaches could and should produce.

This work is a core conceptual contribution of the DEMIURGE project, funded by the European Research Council via an ERC Consolidator Grant awarded to Mauro Birattari of IRIDIA, the artificial intelligence laboratory of the ULB. The scope of the DEMIURGE project is indeed the automatic design of robot swarms. This article frames the central ideas of the field, disentangles them from other ideas that are related but different, presents the existing results, isolates the fundamental problems, sets out the research questions that characterize it and outlines the path of its future development.


Read the original article:

M. Birattari, A. Ligot, and K. Hasselmann (2020), Disentangling automatic and semi-automatic approaches to the optimization-based design of control software for robot swarms.

Nature Machine Intelligence, 2(9):494–499.

DOI: 10.1038/s42256-020-0215-0

Read it online: https://rdcu.be/b6aVI


Mauro Birattari

Mauro Birattari

A Research Director of the Belgian Fonds de la Recherche Scientifique—FNRS at IRIDIA, Université libre de Bruxelles. He received a Master's degree in Electrical and Electronic Engineering from Politecnico di Milano in 1997 and a doctoral degree in Information Technologies from the Faculty of Engineering of the Université libre de Bruxelles in 2004. His research focuses on swarm intelligence, collective robotics, machine learning, and on the application of artificial intelligence techniques to the automatic design of algorithms.

Dr. Birattari is the principal investigator of the project "DEMIURGE: automatic design of robot swarms," funded by the European Research Council through an ERC Consolidator Grant.

Antoine Ligot

Antoine Ligot

A PhD candidate at IRIDIA, Université libre de Bruxelles. He received a Master's degree in Computational Intelligence from Université libre de Bruxelles in 2016. His research interests include swarm robotics, automatic design of collective behaviors, design methodologies and methods to handle the reality gap.

Ken Hasselmann

Ken Hasselmann

A PhD candidate at IRIDIA and teaching assistant at École Polytechnique de Bruxelles, Université libre de Bruxelles. He received a Master's degree in Electronics and Embedded Systems Engineering from INP-ENSEEIHT in Toulouse and a Master's degree in Innovation from Toulouse School of Management in 2014. His research interests include swarm robotics, machine learning, and the automatic design of collective behaviors and communication protocols for robot swarms.


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