Control of real-world complex robots using a biologically inspired algorithm

Elementary living beings, like bacteria, are able to reach food sources using only limited and very noisy sensory information. In this paper, we describe a very simple algorithm inspired from bacteria chemotaxis. We present a Markov chain model for studying the effect of noise on the behavior of an agent that moves according to this algorithm, and we show that, counterintuitively, the application of noise can increase the expected average performance over a fixed available time. After this theoretical analysis, experiments on real-world application of this algorithm are introduced. In particular, we show that the algorithm is able to control a complex robot arm, actuated by 17 McKibben pneumatic artificial muscles, without the need of any model of the robot or of its environment.