developmentalrobotics is also known as epigenetic robotics. We propose in this paper that there is one substantial difference between developmentalrobotics and epigenetic robotics, since epigenetic robotics concentr...
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developmentalrobotics is also known as epigenetic robotics. We propose in this paper that there is one substantial difference between developmentalrobotics 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 developmentalrobotics should also cover the modeling of neural and morphological development in single-and multirobot systems. With the recent rapid advances in evolutionarydevelopmental 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 developmentalrobotics, is an important part of developmentalrobotics 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 swarm robotic 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.
developmental evolution of collective swarm behaviours promises new ways to evolve swarms with different movement characteristics. Preliminary work has developed value functions that can recognize emergent swarm behav...
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ISBN:
(纸本)9783030649838;9783030649845
developmental evolution of collective swarm behaviours promises new ways to evolve swarms with different movement characteristics. Preliminary work has developed value functions that can recognize emergent swarm behaviour and distinguish it from random behaviour in point-mass boid simulations. This paper examines the performance of several variants of such functions recognizing the emergent behaviour of simulated robots, which have different movement properties to point-mass boid simulations as they are constrained by the manoeuvrability of the physical robot. We designed two boid guidance algorithms for controlling Pioneer3DX robots. Five value functions were then examined and compared for their ability to distinguish swarming behaviour from unstructured behaviour. Results show that four of these can be used to distinguish structured collective behaviours of the robots and distinguish such behaviour from random movement patterns.
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