17-09-2012, 04:54 PM
Morphogenetic Robotics: An Emerging New Field in Developmental Robotics
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Abstract
Developmental robotics is also known as epigenetic
robotics.We propose in this paper that there is one substantial difference
between developmental robotics and epigenetic robotics,
since epigenetic robotics concentrates primarily on modeling the
development of cognitive elements of living systems in robotic systems,
such as language, emotion, and social skills, while developmental
robotics should also cover the modeling of neural and morphological
development in single- and multirobot systems. With
the recent rapid advances in evolutionary developmental biology
and systems biology, increasing genetic and cellular principles
underlying biological morphogenesis have been revealed. These
principles are helpful not only in understanding biological development,
but also in designing self-organizing, self-reconfigurable,
and self-repairable engineered systems. In this paper, we propose
morphogenetic robotics, an emerging new field in developmental
robotics, is an important part of developmental robotics in addition
to epigenetic robotics. By morphogenetic robotics, we mean
a class of methodologies in robotics for designing self-organizing,
self-reconfigurable, and self-repairable single- or multirobot systems,
using genetic and cellular mechanisms governing biological
morphogenesis. We categorize these methodologies into three areas,
namely,morphogenetic swarmrobotic systems,morphogenetic
modular robots, and morphogenetic body and brain design for
robots. Examples are provided for each of the three areas to illustrate
the main ideas underlying the morphogenetic approaches to
robotics.
WHAT IS AND WHY MORPHOGENETIC ROBOTICS?
DEVELOPMENTAL robotics is an interdisciplinary field
of robotics that employs simulated or physical robots to
understand natural intelligence on the one hand, and to design
better robotic systems using principles in biological development,
on the other hand [57]. The term developmental robotics
is often used interchangeably with other two terms, namely, epigenetic
robotics [64] and ontogenetic robotics, which focuses
on modeling the postnatal development of cognitive behaviors
in living systems, such as language, emotion, anticipation
MULTICELLULAR MORPHOGENESIS AND ITS
COMPUTATIONAL MODELING
A. Biological Morphogenesis and Metamorphosis
Morphogenesis of animals can be divided into early embryonic
development and later embryonic development [27]. Early
embryonic development typically involves cleavage, gastrulation,
and axis formation, while later embryonic development is
mainly responsible for the development of the nervous systems,
starting with the segregation of neural and glial cells from the
ectoderm germ layer [80]. An example of morphogenesis of
nematostella vectensis is illustrated in Fig. 2.
Metamorphosis is another interesting stage of biological development.
There are two types of metamorphosis, namely, incomplete
and complete metamorphosis. For organisms underlying
incomplete metamorphosis, there are three developmental
stages, in which nymphs look similar to adults. In contrast,
organisms that undergo complete metamorphosis have four developmental
stages, in which the shape of the organisms changes
drastically. Fig. 3 illustrates incomplete and complete metamorphosis
of insects.
Intermediate Summary
Compared to existing approaches [36], the morphogenetic approach
to swarm robotic systems has the following advantages.
First, the global behavior, i.e., the target shape in the context of
pattern formation, can be embedded in the robot dynamics in the
form of morphogen gradients. In pattern formation, the global
shape can be described using parametrized models such as a
NURBS model that can represent both analytical and free-form
shapes. The GRN model can then generate implicit local interaction
rules automatically to generate the global behavior, which
can be guaranteed through a rigorous mathematical proof. Second,
the morphogenetic approach is robust to perturbations in
the system and in the environment. Third, it has also shown that
the morphogenetic approach can provide a unified framework
for multirobot shape formation and boundary coverage [31],
since the representation of the target shape is independent of a
specific global coordination system. Morphogenetic approaches
to self-organization of collective systems can potentially be applied
to solving other engineering problems such as the topology
self-reconfiguration of communication networks [54].