Evolution, Individual Learning, and Social Learning in a Swarm of Real Robots

Heinerman, Jacqueline; Rango, Massimiliano; Eiben, A. E.

doi:10.1109/ssci.2015.152

Cited by 24 publications

(27 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…They are also suitable (and have been actually used [12][13][14]) for experimenting with Evolutionary Robotics [6]. A graphical representation of the feasible regions is shown in Figure 4.…”

Section: Robotsmentioning

confidence: 99%

Design of Powered Floor Systems for Mobile Robots with Differential Evolution

Medvet

Seriani

Bartoli

et al. 2019

Applications of Evolutionary Computation

View full text Add to dashboard Cite

Mobile robots depend on power for performing their task. Powered floor systems, i.e., surfaces with conductive strips alternatively connected to the two poles of a power source, are a practical and effective way for supplying power to robots without interruptions, by means of sliding contacts. Deciding where to place the sliding contacts so as to guarantee that a robot is actually powered irrespective of its position and orientation is a difficult task. We here propose a solution based on Differential Evolution: we formally define problem-specific constraints and objectives and we use them for driving the evolutionary search. We validate experimentally our proposed solution by applying it to three real robots and by studying the impact of the main problem parameters on the effectiveness of the evolved designs for the sliding contacts. The experimental results suggest that our solution may be useful in practice for assisting the design of powered floor systems.

show abstract

Section: Robotsmentioning

confidence: 99%

Design of Powered Floor Systems for Mobile Robots with Differential Evolution

Medvet

Seriani

Bartoli

et al. 2019

Applications of Evolutionary Computation

View full text Add to dashboard Cite

show abstract

“…According to the authors, the concurrently designed swarm performed relatively better than the swarm with fixed hardware configuration. Heinerman et al (2015) studied the relationship between individual and social learning in physical robot swarms. The authors used six Thymio II robots in their experiments.…”

Section: State Of the Artmentioning

confidence: 99%

Peer Review #1 of "Concurrent design of control software and configuration of hardware for robot swarms under economic constraints (v0.1)"

2019

View full text Add to dashboard Cite

Designing a robot swarm is challenging due to its self-organized and distributed nature: complex relations exist between the behavior of the individual robots and the collective behavior that results from their interactions. In this paper, we study the concurrent automatic design of control software and the automatic configuration of the hardware of robot swarms. We introduce Waffle, a new instance of the AutoMoDe family of automatic design methods that produces control software in the form of a probabilistic finite state machine, configures the robot hardware, and selects the number of robots in the swarm. We test Waffle under economic constraints on the total monetary budget available and on the battery capacity of each individual robot comprised in the swarm. Experimental results obtained via realistic computer-based simulation on three collective missions indicate that different missions require different hardware and software configuration, and that Waffle is able to produce effective and meaningful solutions under all the experimental conditions considered.

show abstract

“…We propose that the energy neutral region is of greatest interest for researchers wishing to conduct research moving beyond genetic evolution of survival, for example using individual or social learning [8] or task-driven research [4]. In this region, on the one hand, robots are able to survive, while on the other, the environment does not over-provide, thus ensuring that there is scope for robots to learn novel behaviours.…”

Section: Behaviours In the Neutral Regionmentioning

confidence: 99%

Understanding Environmental Influence in an Open-Ended Evolutionary Algorithm

Steyven

Hart

Paechter

2016

Parallel Problem Solving From Nature – PPSN XIV

View full text Add to dashboard Cite

Abstract. It is well known that in open-ended evolution, the nature of the environment plays in key role in directing evolution. However, in Evolutionary Robotics, it is often unclear exactly how parameterisation of a given environment might influence the emergence of particular behaviours. We consider environments in which the total amount of energy is parameterised by availability and value, and use surface plots to explore the relationship between those environment parameters and emergent behaviour using a variant of a well-known distributed evolutionary algorithm (mEDEA). Analysis of the resulting landscape show that it is crucial for a researcher to select appropriate parameterisations in order that the environment provides the right balance between facilitating survival and exerting sufficient pressure for new behaviours to emerge. To the best of our knowledge, this is the first time such an analysis has been undertaken.

show abstract

Evolution, Individual Learning, and Social Learning in a Swarm of Real Robots

Cited by 24 publications

References 16 publications

Design of Powered Floor Systems for Mobile Robots with Differential Evolution

Design of Powered Floor Systems for Mobile Robots with Differential Evolution

Peer Review #1 of "Concurrent design of control software and configuration of hardware for robot swarms under economic constraints (v0.1)"

Understanding Environmental Influence in an Open-Ended Evolutionary Algorithm

Contact Info

Product

Resources

About