A BioInspired Neural Controller For a Mobile Robot

Abstract

Abstract-This paper focuses on the study of a bio-inspired neural controller used to govern a mobile robot. The network's architecture is based on the understanding that neurophysiologists have obtained on the nervous system of some simple animals, like arthropods or invertebrates. The neuronal model mimics the behavior of the natural cells present in the animal, and elaborates the continuous signals coming from the robot's sensors. The output generated by the controller, after scaling, commands the wheel rotation and therefore the robot's linear and angular velocity. The mobile robot, thanks to the controller, presents different behaviors, like reaching a sonorous source, avoiding obstacles and finding the recharge stations. In the network architecture different modules, charged of different functionality, are regulated and coordinated using an inhibition mechanism. In order to test the control strategy and the neural architecture, we implemented the system in Matlab and finally in hardware using a dedicated dual processor board equipped with an ARM7TDMI micro-controller. Results show that the neural controller can govern the robot efficiently with performances comparable with those described about the animal.

Simulation of a Simple Bio-Mimetic Robot with Neuromorphic Control System and Optimization Based on the Genetic Algorithm

“…Folgheraiter et al [3], [4] developed their own neural network architecture. Their neural control system is similar to the one that was used in this paper with several modifications and will be described in the following sections.…”

“…This work can be considered as a continuation of the project of Dr. Michele Folgheraiter, and his colleagues from Politecnico di Milano [3], [4]. In this work I improved and modified the neural network algorithm developed by Folgheraiter et al [3] [4], and introduced optimization algorithm.…”

“…In this work I improved and modified the neural network algorithm developed by Folgheraiter et al [3] [4], and introduced optimization algorithm.…”

Intelligent neuro-controller for navigation of mobile robot

“…The variable θ is the orientation of the local coordination of the local coordinate frame [P X c Yp] attached on the robot platform measured from the horizontal axis. Three generalized coordinates can describe the configuration of the robot as equation (4). The mobile robot system considered here is a rigid body and the wheels are pure rolling and no slippage.…”

“…Service robotics today requires synthesizing robust automatic systems able to cope with a complex and dynamic environment [4]. To demonstrate this kind of autonomy Muniz et al [8] have introduced a neural controller for a mobile robot that learns both forward and inverse odometry of a differential drive robot through an unsupervised learning by doing cycle.…”