Artificial Behavioral System By Sensor-Motor Mapping Strategy For Multi-Objective Robot Tasks

Abstract

In this study, behavioral system based robot control architecture is built up for a four-wheel driven and four-wheel steered mobile robot. Behavioral system is determined as evolutionary neural-fuzzy inference system for behavior generation and self-learning processes in the general robot control architecture. The kinematics and dynamic model of the mobile robot with non-holonomic constraints is used as present structure which is modeled in previous studies. The posture and speed of the robot and the configurations, speeds and torques of the wheels can be observed from the simulation plant and virtual reality viewer. The behaviors are investigated regarding their gains, fuzzy inference structures, real-time applicability and their coordination.