Bio-Inspired Self-Organizing Robotic Systems by Yan Meng, Yaochu Jin

By Yan Meng, Yaochu Jin

Self-organizing ways encouraged from organic platforms, similar to social bugs, genetic, molecular and mobile structures below morphogenesis, and human psychological improvement, has loved nice luck in complex robot structures that have to paintings in dynamic and altering environments. in comparison with classical regulate equipment for robot structures, the key benefits of bio-inspired self-organizing robot platforms contain robustness, self-repair and self-healing within the presence of approach mess ups and/or malfunctions, excessive adaptability to environmental adjustments, and independent self-organization and self-reconfiguration with out a centralized keep watch over. “Bio-inspired Self-organizing robot platforms” presents a precious reference for scientists, practitioners and study scholars engaged on constructing keep an eye on algorithms for self-organizing engineered collective platforms, equivalent to swarm robot platforms, self-reconfigurable modular robots, shrewdpermanent fabric established robot units, unmanned aerial automobiles, and satellite tv for pc constellations.

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A): The robot’s given shape, which is part of the fitness-function of the artificial evolutionary process. (b): Evolved shape of the virtual embryo which constructs the ANN network topology. (c): Example of one ANN topology, grown in this embryo. How to Engineer Robotic Organisms and Swarms? (a) Genome 45 (b) Multi-modular robot Fig. 15 a): Evolved genome structure that controls the growth process of the embryo shown in Fig. 14b. Each gene of this genome is able to produce proteins, which in turn can activate other genes, produce morphogens, change the receptivity of the cell for morphogens, or build neural links to other cells.

One of the results, that was reported in [10], is shown in Fig. 12. It shows a comparison of the best fitness obtained by artificial evolution for N = 12 independent runs per controller approach. The superiority of AHHS over a simple artificial neural network approach is significant. 5 How to Engineer Robotic Organisms and Swarms? AHHS2 complex ANN complex Fig. 12 Comparison between the AHHS controller and a simple artificial neural network for a gait learning task with three modules (N = 12) [10].

A) AHHS (b) ANN Fig. 13 Trajectory of the best evolved individual of (a) AHHS and (b) ANN controller in an “exploring the maze” scenario. 44 T. Schmickl 4 Evolutionary Shaping of Network Topology of Controllers to Body Shapes Neural networks are a state-of-the-art technology in evolutionary robotics. ([8]). One advantage is that they are easy to mutate and quite powerful and efficient in a computational sense. In contrast to out-of-the-box ANNs, living organisms show highly structured neural networks, often split up into brains and ganglia.

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