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In this paper, a control system representation based on finite state machines is utilized to build an evolutionary robotic framework where evolution is performed in a swarm of simple robots in an online and onboard manner. Experiments in simulation show that the framework is capable of robustly evolving basic benchmark behaviors like collision avoidance.
To achieve a desired global behavior for a swarm of robots where each robot has a local view and operating range in the environment is a well-known and challenging problem. Evolutionary Robotics is a self-adaptation approach which has been shown to effectively find robot controllers for behaviors which are hard to implement by hand. There, evolvability is(More)
Evolutionary Robotics is a collection of heuristics where robotic control systems are developed by following the example of natural evolution. An evolutionary run is performed by mutating the robots' controllers randomly and selecting for some desired behavioral properties. Overall, these properties should be improved over time leading to a stable increase(More)
This paper proposes a novel architectural concept for developing agent-based <i>simulations called Simulation Plugin Interface (SPI)</i>; furthermore, a simulation framework called <i>Easy Agent Simulation (EAS)</i> based on the proposed architecture is presented. The SPI introduces an intermediate layer between the simulation engine and the simulation(More)
In this paper, a theoretical and experimental study of the influence of environments on the selection process in evolutionary swarm robotics is conducted. The theoretical selection model is based on Markov chains. It is proposed to predict the success rate of evolutionary runs which are based on a selection mechanism depending on implicit environmental(More)
—A completely evolvable genotype-phenotype mapping (ceGPM) is studied with respect to its capability of improving the flexibility of artificial evolution. By letting mutation affect not only controller genotypes, but also the mapping from genotype to phenotype, the future effects of mutation can change over time. In this way, the need for prior parameter(More)