27,718 research outputs found
Embodied Evolution in Collective Robotics: A Review
This paper provides an overview of evolutionary robotics techniques applied
to on-line distributed evolution for robot collectives -- namely, embodied
evolution. It provides a definition of embodied evolution as well as a thorough
description of the underlying concepts and mechanisms. The paper also presents
a comprehensive summary of research published in the field since its inception
(1999-2017), providing various perspectives to identify the major trends. In
particular, we identify a shift from considering embodied evolution as a
parallel search method within small robot collectives (fewer than 10 robots) to
embodied evolution as an on-line distributed learning method for designing
collective behaviours in swarm-like collectives. The paper concludes with a
discussion of applications and open questions, providing a milestone for past
and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl
EEG Resting-State Brain Topological Reorganization as a Function of Age
Resting state connectivity has been increasingly studied to investigate the effects of aging on the brain. A reduced organization
in the communication between brain areas was demonstrated b
y combining a variety of different imaging technologies (fMRI,
EEG, and MEG) and graph theory. In this paper, we propose a methodology to get new insights into resting state connectivity and
its variations with age, by combining advanced techniques of effective connectivity estimation, graph theoretical approach, and
classification by SVM method. We analyzed high density EEG signal
srecordedatrestfrom71healthysubjects(age:20–63years).
Weighted and directed connectivity was computed by means of Partial Directed Coherence based on a General Linear Kalman filter
approach. To keep the information collected by the estimator, weighted and directed graph indices were extracted from the resulting
networks. A relation between brain network properties and age of the subject was found, indicating a tendency of the network to
randomly organize increasing with age. This result is also confirmed dividing the whole population into two subgroups according
to the age (young and middle-aged adults): significant differences exist in terms of network organization measures. Classification
of the subjects by means of such indices returns an accuracy greater than 80
A Decentralized Mobile Computing Network for Multi-Robot Systems Operations
Collective animal behaviors are paradigmatic examples of fully decentralized
operations involving complex collective computations such as collective turns
in flocks of birds or collective harvesting by ants. These systems offer a
unique source of inspiration for the development of fault-tolerant and
self-healing multi-robot systems capable of operating in dynamic environments.
Specifically, swarm robotics emerged and is significantly growing on these
premises. However, to date, most swarm robotics systems reported in the
literature involve basic computational tasks---averages and other algebraic
operations. In this paper, we introduce a novel Collective computing framework
based on the swarming paradigm, which exhibits the key innate features of
swarms: robustness, scalability and flexibility. Unlike Edge computing, the
proposed Collective computing framework is truly decentralized and does not
require user intervention or additional servers to sustain its operations. This
Collective computing framework is applied to the complex task of collective
mapping, in which multiple robots aim at cooperatively map a large area. Our
results confirm the effectiveness of the cooperative strategy, its robustness
to the loss of multiple units, as well as its scalability. Furthermore, the
topology of the interconnecting network is found to greatly influence the
performance of the collective action.Comment: Accepted for Publication in Proc. 9th IEEE Annual Ubiquitous
Computing, Electronics & Mobile Communication Conferenc
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