AutoMoDe: A Modular Approach to the Automatic Off-Line Design and Fine-Tuning of Control Software for Robot Swarms
Although swarm robotics is widely recognized as a promising approach to coordinating large groups of robots, a general methodology for designing collective behaviors for robot swarms is still missing. Automatic off-line design is an appealing solution but it is prone to the so-called reality gap, wh...
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| Published in | Automated Design of Machine Learning and Search Algorithms pp. 73 - 90 |
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| Main Authors | , , |
| Format | Book Chapter |
| Language | English |
| Published |
Switzerland
Springer International Publishing AG
2021
Springer International Publishing |
| Series | Natural Computing Series |
| Online Access | Get full text |
| ISBN | 3030720683 9783030720681 |
| ISSN | 1619-7127 |
| DOI | 10.1007/978-3-030-72069-8_5 |
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| Summary: | Although swarm robotics is widely recognized as a promising approach to coordinating large groups of robots, a general methodology for designing collective behaviors for robot swarms is still missing. Automatic off-line design is an appealing solution but it is prone to the so-called reality gap, which is the reason for performance drops when control software developed in simulation is deployed on real robots. We present here our research on AutoMoDe, a novel approach to the automatic off-line design of robot swarms, which is based on the principle of modularity. AutoMoDe produces control software for robot swarms by selecting, combining, instantiating, and fine-tuning predefined parametric modules that represent low-level behaviors defined in a mission-agnostic way. By restricting the generation of control software to the instances that can be produced with the given modules, we effectively inject a bias in the design process and consequently reduce its variance. As confirmed by the empirical studies realized so far, this reduces the risk of overfitting simulation models and improves the chances of crossing the reality gap successfully. |
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| Bibliography: | The three authors equally contributed to the realization of this chapter and should be considered as co-first authors. The research presented has been conceived and directed by MB; the core experiments were performed by GF; and the manuscript was drafted by AL, revised by MB, and optimized by the three authors. The research has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 681872). MB acknowledges support from the Belgian Fonds de la Recherche Scientifique–FNRS, of which he is a Research Director. |
| ISBN: | 3030720683 9783030720681 |
| ISSN: | 1619-7127 |
| DOI: | 10.1007/978-3-030-72069-8_5 |