Applying Digital Evolution to the Design of Self-Adaptive Software

Abstract — As software developers, we strive to create computational systems that are as robust and versatile as biological organisms have evolved to be in nature. We propose a software development methodology capable of producing selfadaptive software, using digital evolution to discover behaviors and optimize solutions. Employing this methodology we present an example behavioral concept from inception to fruition on physical hardware, as a proof of concept of the approach. We evolve environmentally-aware motility behaviors through digital evolution, automatically translate the evolved programs into C code, and compile and load the programs onto mobile robots

Synthesizing Physically-Realistic Environmental Models from Robot Exploration

Abstract. In previous work [4] a framework was demonstrated that allows an autonomous robot to automatically synthesize physically-realistic models of its own body. Here it is demonstrated how the same approach can be applied to empower a robot to synthesize physically-realistic models of its surroundings. Robots which build numerical or other nonphysical models of their environments are limited in the kinds of predictions they can make about the repercussions of future actions. In this paper it is shown that a robot equipped with a self-made, physicallyrealistic model can extrapolate: a slow-moving robot consistently predicts the much faster top speed at which it can safely drive across a terrain.