AutoMoDe-Mate: Automatic off-line design of spatially-organizing behaviors for robot swarms

Mendiburu, Fernando J.; Ramos, David Garzón; Morais, Marcos R. A.; Lima, A.M.N.; Birattari, Mauro

doi:10.1016/j.swevo.2022.101118

Cited by 9 publications

(2 citation statements)

References 71 publications

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“…EvoStick is the only neuroevolutionary method that has been tested in the automatic design of robot swarms for several missions, without undergoing any mission-specific modification 48 . Moreover, EvoStick served as a starting point to develop other neuroevolutionary methods for robots endowed with enhanced capabilities—see, for example, adaptations of EvoStick to study direct communication 53 , 58 and spatial organisation 69 . EvoStick, and therefore EvoPheromone, are simple and straightforward implementations of the neuroevolutionary approach.…”

Section: Methodsmentioning

confidence: 99%

Automatic design of stigmergy-based behaviours for robot swarms

Salman,

Garzón Ramos,

Birattari

2024

Commun Eng

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Stigmergy is a form of indirect communication and coordination in which individuals influence their peers by modifying the environment in various ways, including rearranging objects in space and releasing chemicals. For example, some ant species lay pheromone trails to efficiently navigate between food sources and nests. Besides being used by social animals, stigmergy has also inspired the development of algorithms for combinatorial optimisation and multi-robot systems. In swarm robotics, collective behaviours based on stigmergy have always been designed manually, which is time consuming, costly, hardly repeatable, and depends on the expertise of the designer. Here, we show that stigmergy-based behaviours can be produced via automatic design: an optimisation process based on simulations generates collective behaviours for a group of robots that can lay and sense artificial pheromones. The results of our experiments indicate that the collective behaviours designed automatically are as good as—and in some cases better than—those produced manually. By taking advantage of pheromone-based stigmergy, the automatic design process generated collective behaviours that exhibit spatial organisation, memory, and communication.

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Section: Methodsmentioning

confidence: 99%

Automatic design of stigmergy-based behaviours for robot swarms

Salman,

Garzón Ramos,

Birattari

2024

Commun Eng

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“…Francesca et al (2014) proposed AutoMoDe-Vanilla, an automatic modular design method that assembles finite-state machines out of a set of twelve handcrafted modules. Several flavors (i.e., implementations) of AutoMoDe have been proposed to study different elements of the design process, such as different module sets (Ligot et al, 2020a;Garzón Ramos and Birattari, 2020;Hasselmann and Birattari, 2020;Spaey et al, 2020;Mendiburu et al, 2022), hardware-software co-design (Salman et al, 2019), or optimization algorithms (Kuckling et al, 2020a;Kuckling et al, 2020b;Cambier and Ferrante, 2022). Besides finite-state machines, behavior trees have recently gained attention in the literature on automatic modular design.…”

Section: Automatic Modular Designmentioning

confidence: 99%

Recent trends in robot learning and evolution for swarm robotics

Kuckling

2023

Front. Robot. AI

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Swarm robotics is a promising approach to control large groups of robots. However, designing the individual behavior of the robots so that a desired collective behavior emerges is still a major challenge. In recent years, many advances in the automatic design of control software for robot swarms have been made, thus making automatic design a promising tool to address this challenge. In this article, I highlight and discuss recent advances and trends in offline robot evolution, embodied evolution, and offline robot learning for swarm robotics. For each approach, I describe recent design methods of interest, and commonly encountered challenges. In addition to the review, I provide a perspective on recent trends and discuss how they might influence future research to help address the remaining challenges of designing robot swarms.

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Automatic Design of Robot Swarms under Concurrent Design Criteria: A Study Based on Iterated F‐Race

Garzón Ramos,

Pagnozzi,

Stützle

et al. 2024

Advanced Intelligent Systems

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Automatic design is an appealing approach to realizing robot swarms. In this approach, a designer specifies a mission that the swarm must perform, and an optimization algorithm searches for the control software that enables the robots to perform the given mission. Traditionally, research in automatic design has focused on missions specified by a single design criterion, adopting methods based on single‐objective optimization algorithms. In this study, we investigate whether existing methods can be adapted to address missions specified by concurrent design criteria. We focus on the bi‐criteria case. We conduct experiments with a swarm of e‐puck robots that must perform sequences of two missions: each mission in the sequence is an independent design criterion that the automatic method must handle during the optimization process. We consider modular and neuroevolutionary methods that aggregate concurrent criteria via the weighted sum, hypervolume, or ‐norm. We compare their performance with that of Mandarina, an original automatic modular design method. Mandarina integrates Iterated F‐race as an optimization algorithm to conduct the design process without aggregating the design criteria. Results from realistic simulations and demonstrations with physical robots show that the best results are obtained with modular methods and when the design criteria are not aggregated.

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AutoMoDe-Mate: Automatic off-line design of spatially-organizing behaviors for robot swarms

Cited by 9 publications

References 71 publications

Automatic design of stigmergy-based behaviours for robot swarms

Automatic design of stigmergy-based behaviours for robot swarms

Recent trends in robot learning and evolution for swarm robotics

Automatic Design of Robot Swarms under Concurrent Design Criteria: A Study Based on Iterated F‐Race

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