Robust Spatial Navigation in a Robot Inspired by Chemotaxis in Caenorhabditis elegans

Abstract

We report on the design and implementation of an autonomous robot that performs phototaxis under the control of a simulated neural network. The mechanical configuration of the robot and its neural network controller are patterned after those believed to produce chemotaxis in the nematode Caenorhabditis elegans. The network is first optimized to produce phototaxis in a simulated, nematode-like robot and then is tested on a real robot. We find that both the simulated and real robot perform reliably, making nearly identical trajectories for similar environments and similar starting conditions. Furthermore, their performance is robust to significant perturbations of the robot's locomotion parameters. Finally, we discuss the implicit computational rule that this network uses to control phototaxis. This makes the results intuitive and improves our intuition about control of tactic behavior in two dimensions.