By Yan Meng, Yaochu Jin
Self-organizing methods encouraged from organic platforms, resembling social bugs, genetic, molecular and mobile structures less than morphogenesis, and human psychological improvement, has loved nice good fortune in complex robot platforms that have to paintings in dynamic and altering environments. in comparison with classical keep watch over equipment for robot structures, the key merits of bio-inspired self-organizing robot platforms comprise robustness, self-repair and self-healing within the presence of method mess ups and/or malfunctions, excessive adaptability to environmental adjustments, and self reliant self-organization and self-reconfiguration with out a centralized regulate. “Bio-inspired Self-organizing robot structures” offers a beneficial reference for scientists, practitioners and study scholars engaged on constructing keep an eye on algorithms for self-organizing engineered collective structures, akin to swarm robot structures, self-reconfigurable modular robots, clever fabric dependent robot units, unmanned aerial automobiles, and satellite tv for pc constellations.
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Extra resources for Bio-Inspired Self-Organizing Robotic Systems
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. Genes and proteins are indicated by geometrical shapes. Interactions of proteins and genes are indicated by arrows. b): Formation of a multi-modular robot using VE in a simulation environment.
An engineering approach to generate such patterns of artificial neural networks is Hyperneat , an extension of Neat , which was used successfully in several applications [5, 4]. To enhance our evolutionary robotic systems, we aim for a similar pattern-generating system which is inspired by the growth process of multi-cellular organisms (see Fig. 14). To achieve this, we developed of virtual embryogenesis (VE) to generate various topologies autonomously and dynamically [39, 16]. The main idea of VE is the simulation of EvoDevo-like processes .
As natural selection shaped natural systems into efficient and robust configurations, we aimed for increasing the level of bio-inspiration, as can be seen by the algorithms described in the following. 3 Trophallaxis-Inspired Algorithm In contrast to the vector-based strategy, the next algorithm does not require any ’vector’-calculations which can lead to an aggregation of calculation errors (due to noise) within the swarm. It requires less computational power of the robots and requires the communication of two floating point numbers as ’messages’ between neighboring robots.