Evolving control for modular robotic units

Østergaard, Esben Hallundbæk; Lund, Henrik Hautop

doi:10.1109/cira.2003.1222297

Cited by 19 publications

(17 citation statements)

References 14 publications

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“…Lattice modular robotic systems use cluster-flow locomotion and reconfiguration: in order to move, the robot continuously reconfigures (modules attaching and detaching over a lattice of other modules), thereby giving the impression that the module cluster "flows" on the ground and around obstacles. The Crystalline robot (Vona and Rus, 2000), Telecube (Vassilvitskii et al, 2002), and the ATRON (Ostergaard and Lund, 2003) are examples of such robots. Chaintype robots normally locomote in a static configuration (i.e.…”

Section: Related Workmentioning

confidence: 99%

Learning to Move in Modular Robots using Central Pattern Generators and Online Optimization

Sproewitz

Moeckel

Maye

et al. 2008

The International Journal of Robotics Research

118

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This article addresses the problem of how modular robotics systems, i.e. systems composed of multiple modules that can be configured into different robotic structures, can learn to locomote. In particular, we tackle the problems of online learning, that is, learning while moving, and the problem of dealing with unknown arbitrary robotic structures.We propose a framework for learning locomotion controllers based on two components: a central pattern generator (CPG) and a gradient-free optimization algorithm: Powell's method. The CPG is implemented as a system of coupled nonlinear oscillators in our YaMoR modular robotic system, with one oscillator per module. The nonlinear oscillators are coupled together across modules using Bluetooth communication to obtain specific gaits, i.e. synchronized patterns of oscillations among modules. Online learning is done by running the Powell optimization algorithm in parallel to the CPG model, with the speed of locomotion being the criterion to be optimized. Interesting aspects of the optimization are: it is carried out online, it does not require stopping or resetting the robots, and it is fast.We present results showing the interesting properties of this framework for a modular robotic system. In particular, our CPG model can readily be implemented in a distributed system, it is cheap computationally, it exhibits limit cycle behavior (temporary perturbations are rapidly forgotten), it produces smooth trajectories even when control parameters are abruptly changed, and it is robust against imperfect communication among modules. We also present results of learning to move with three different robot structures. Interesting locomotion modes are obtained after running the optimization for less than 60 minutes.

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Section: Related Workmentioning

confidence: 99%

Learning to Move in Modular Robots using Central Pattern Generators and Online Optimization

Sproewitz

Moeckel

Maye

et al. 2008

The International Journal of Robotics Research

118

View full text Add to dashboard Cite

show abstract

“…Østergaard and Lund explored evolving controllers of M-TRAN [41] and ATRON [30] selfreconfigurable modular robotic systems in simulation. Employing Genetic Algorithm for implementing M-TRAN walking behavior is very complicated because evolving each controller locally to generate a global behavior is affected by the conditions of neighboring modules.…”

Section: Self-reconfigurationmentioning

confidence: 99%

“…Since any replication process requires an external material supply, some lattice positions may act as dispensers, where new modules reappear when removed from that location. Self-replication is classified to the following types [41].…”

Section: Self-reproductionmentioning

confidence: 99%

Evolutionary Modular Robotics: Survey and Analysis

Alattas

Patel

Sobh

2018

J Intell Robot Syst

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This paper surveys various applications of artificial evolution in the field of modular robots. Evolutionary robotics aims to design autonomous adaptive robots automatically that can evolve to accomplish a specific task while adapting to environmental changes. A number of studies have demonstrated the feasibility of evolutionary algorithms for generating robotic control and morphology. However, a huge challenge faced was how to manufacture these robots. Therefore, modular robots were employed to simplify robotic evolution and their implementation in real hardware. Consequently, more research work has emerged on using evolutionary computation to design modular robots rather than using traditional hand design approaches in order to avoid cognition bias. These techniques have the potential of developing adaptive robots that can achieve tasks not fully understood by human designers. Furthermore, evolutionary algorithms were studied to generate global modular robotic behaviors including; self-assembly, self-reconfiguration, self-repair, and self-reproduction. These characteristics allow modular robots to explore unstructured and hazardous environments. In order to accomplish the aforementioned evolutionary modular robotic promises, this paper reviews current research on evolutionary robotics and modular robots. The motivation behind this work is to identify the most promising methods that can lead to developing autonomous adaptive robotic systems that require the minimum task related knowledge on the designer side.

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“…In M-TRAN [11], an automatic locomotion generation method (ALPG) is used to produce locomotion in arbitrary module configurations using a neural oscillator as a model for the central pattern generator (CPG) and GAs for evolving parameters. ATRON robot [12] uses GAs to determine how well the combination of modules performs a given task when artificial evolution has been used to develop the individual controllers. The evolutionary algorithm was a simple GA working on a string of bytes.…”

Section: Introductionmentioning

confidence: 99%

Offline GA-Based Optimization for Heterogeneous Modular Multiconfigurable Chained Microrobots

Brunete

Hernando

Gambao

2013

IEEE/ASME Trans. Mechatron.

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Abstract-This paper presents a GA-based optimization procedure for bioinspired heterogeneous modular multiconfigurable chained microrobots. When constructing heterogeneous chained modular robots that are composed of several different drive modules, one must select the type and position of the modules that form the chain. One must also develop new locomotion gaits that combine the different drive modules. These are two new features of heterogeneous modular robots that they do not share with homogeneous modular robots. This paper presents an offline control system that allows the development of new configuration schemes and locomotion gaits for these heterogeneous modular multiconfigurable chained microrobots. The offline control system is based on a simulator that is specifically designed for chained modular robots and allows them to develop and learn new locomotion patterns.

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Evolving control for modular robotic units

Cited by 19 publications

References 14 publications

Learning to Move in Modular Robots using Central Pattern Generators and Online Optimization

Learning to Move in Modular Robots using Central Pattern Generators and Online Optimization

Evolutionary Modular Robotics: Survey and Analysis

Offline GA-Based Optimization for Heterogeneous Modular Multiconfigurable Chained Microrobots

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