Navigational control strategy of humanoid robots using average fuzzy-neuro-genetic hybrid technique

Abstract

In this research paper navigational path planning of humanoid robots using developed average fuzzy-neuro-genetic hybrid technique has been analysed. Inputs to the hybrid controller are front, front-left and front-right obstacle distances and target location obtained from ultrasonic and image sensors of humanoid robot. Three Artificial Intelligence (AI) controllers such as, fuzzy logic, neural network and genetic algorithms have been used in parallel for robot navigation control. The outputs from sensors are fed as inputs to the hybrid controllers. The average output from the controller in the form of steering angle is used for robot dynamic movements while avoiding obstacles and reaching targets. A close agreement has been observed during comparison of simulation and experimental results.