A Neural Network-based kinematic and light-perception simulator for simple robotic evolution

Abstract

Current research reveals limited investigations into the use of Artificial Neural Networks (ANNs) as robot simulators. The noise-tolerance and generalization capabilities of ANNs, however, suggest that ANNs could be well-suited to this application. As a result of this observation, a novel technique has been identified wherein ANNs are used as robot simulators. ANNs were employed to simulate the motion dynamics of a mobile robot steered using differential steering, as well as the interaction of two light sensors onboard the robot with a light source in its vicinity. To test the performance of the developed simulators, these simulators were used to evolve a light-approaching robotic control structure in simulation, which was subsequently transferred to the real-world robot. Results indicate that the simulation-evolved controller transferred well from simulation to the real-world robot. It could thus be deduced that ANNs show definite promise as robot simulators.