Open-ended on-board Evolutionary Robotics for robot swarms

Baele, Guy; Bredèche, Nicolas; Haasdijk, Evert; Maere, Steven; Michiels, Nico K.; Peer, Yves Van de; Schmickl, Thomas; Schwarzer, Christopher; Thenius, Ronald

doi:10.1109/cec.2009.4983072

Cited by 12 publications

(11 citation statements)

References 33 publications

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“…In this paper we are interested in a fixed-size population of autonomous physical agents using local communication, such as autonomous robots, facing unknown and/or dynamic environments. This class of problems typically applies when the environment is unknown to the human designer until the population of agents is actually made operational in the real situation [1], or whenever the environment is expected to change during operation with no indication of when and how these changes will impact survival strategies.…”

Section: Introductionmentioning

confidence: 99%

Environment-driven distributed evolutionary adaptation in a population of autonomous robotic agents

Bredèche

Montanier

Liu

et al. 2012

Mathematical and Computer Modelling of Dynamical Systems

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This paper is concerned with a fixed-size population of autonomous agents facing unknown, possibly changing, environments. The motivation is to design an embodied evolutionary algorithm that can cope with the implicit fitness function hidden in the environment so as to provide adaptation in the long run at the level of the population. The proposed algorithm, termed mEDEA, is shown to be both efficient in unknown environments and robust to abrupt and unpredicted changes in the environment. The emergence of consensus towards specific behavioural strategies is examined, with a particular focus on algorithmic stability. Finally, a real world implementation of the algorithm is decribed with a population of 20 real-world e-puck robots.

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Section: Introductionmentioning

confidence: 99%

Environment-driven distributed evolutionary adaptation in a population of autonomous robotic agents

Bredèche

Montanier

Liu

et al. 2012

Mathematical and Computer Modelling of Dynamical Systems

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“…In all previous examples the devices cannot make decisions or change their behaviour without the input of a human operator. Swarm robotics have been studied in the context of producing different collective behaviors to solve tasks such as: aggregation [1], pattern formation [2], self-assembly and morphogenesis [3], object clustering, assembling and construction [4], collective search and exploration [5,6], coordinated motion [7], collective transportation [8,9], self-deployment [10], foraging [11] and others.…”

Section: Introductionmentioning

confidence: 99%

Swarm robotics:design and implementation

Abuelhaija¹,

Jebrein²,

Baldawi³

2020

IJECE

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This project presents a swarming and herding behaviour using simple robots. The main goal is to demonstrate the applicability of artificial intelligence (AI) in simple robotics that can then be scaled to industrial and consumer markets to further the ability of automation. AI can be achieved in many different ways; this paper explores the possible platforms on which to build a simple AI robots from consumer grade microcontrollers. Emphasis on simplicity is the main focus of this paper. Cheap and 8 bit microcontrollers were used as the brain of each robot in a decentralized swarm environment were each robot is autonomous but still a part of the whole. These simple robots don’t communicate directly with each other. They will utilize simple IR sensors to sense each other and simple limit switches to sense other obstacles in their environment. Their main objective is to assemble at certain location after initial start from random locations, and after converging they would move as a single unit without collisions. Using readily available microcontrollers and simple circuit design, semiconsistent swarming behaviour was achieved. These robots don’t follow a set path but will react dynamically to different scenarios, guided by their simple AI algorithm.

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“…After almost twenty years of ER research, simple crawling robots have been automatically designed then manufactured [64]; neural networks have been evolved to allow wheeled robot to avoid obstacles then autonomously charge their battery [29]; neural networks have also been evolved to drive walking [50,55] and flying [78,90] robots, as well as self-organizing swarm of robots [5,38].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Robotics: Exploring New Horizons

Doncieux

Mouret

Bredèche

et al. 2011

Studies in Computational Intelligence

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This paper considers the field of Evolutionary Robotics (ER) from the perspective of its potential users: roboticists. The core hypothesis motivating this field of research is discussed, as well as the potential use of ER in a robot design process. Four main aspects of ER are presented: (a) ER as an automatic parameter tuning procedure, which is the most mature application and is used to solve real robotics problem, (b) evolutionary-aided design, which may benefit the designer as an efficient tool to build robotic systems (c) ER for online adaptation, i.e. continuous adaptation to changing environment or robot features and (d) automatic synthesis, which corresponds to the automatic design of a mechatronic device and its control system. Critical issues are also presented as well as current trends and pespectives in ER. A section is devoted to a roboticist's point of view and the last section discusses the current status of the field and makes some suggestions to increase its maturity.

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Open-ended on-board Evolutionary Robotics for robot swarms

Cited by 12 publications

References 33 publications

Environment-driven distributed evolutionary adaptation in a population of autonomous robotic agents

Environment-driven distributed evolutionary adaptation in a population of autonomous robotic agents

Swarm robotics:design and implementation

Evolutionary Robotics: Exploring New Horizons

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