Neuromodulation Based Control of Autonomous Robots in ROS Environment

The paper presents a control approach based on vertebrate neuromodulation and its implementation on autonomous robots in the open-source, open-access environment of robot operating system (ROS) within a cloud computing framework. A spiking neural network (SNN) is used to model the neuromodulatory function for generating context based behavioral responses of the robots to sensory input signals. The neural network incorporates three types of neurons-cholinergic and noradrenergic (ACh/NE) neurons for attention focusing and action selection, dopaminergic (DA) neurons for rewards-and curiosity-seeking, and serotonergic (5-HT) neurons for risk aversion behaviors. The model depicts description of neuron activity that is biologically realistic but computationally efficient to allow for large-scale simulation of thousands of neurons. The model is implemented using graphics processing units (GPUs) for parallel computing in real-time using the ROS environment. The model is implemented to study the risk-taking, risk-aversive, and distracted behaviors of the neuromodulated robots in single-and multi-robot configurations. The entire process is implemented in a distributed computing framework using ROS where the robots communicate wirelessly with the computing nodes through the on-board laptops. Results are presented for both single-and multi-robot configurations demonstrating interesting behaviors.