This paper addresses the problem of tuning the input and the output parameters of a fuzzy
logic controller. The system learns autonomously without supervision or a priori training …

This paper addresses the problem of tuning the input and the output parameters of a fuzzy
logic controller. A novel technique that combines Q (λ)‐learning with function approximation …

Humanoid robotics is a field of a great research interest nowadays. This work implements a
low-cost teleoperated system to control a humanoid robot, as a first step for further …

Automated control of mobile robot navigation is a challenging area in the field of robotics
research. In this work, an attempt is made to use a new neural network training algorithm …

The automatic navigation of wall following robot is playing important role in various real
world tasks such as underwater exploration, unmanned flight, and in automotive industries …

In any robotics application, the deployed robot has to navigate from source to a destination
for performing task (s). Efficient control of this navigation is a major research challenge in the …

In this paper, a new peak average power and time reduction (PAPTR) based on the adaptive
genetic algorithm (AGA) strategy is used in order to improve both the time reduction and …

This paper addresses the problem of tuning the input and the output parameters of a fuzzy
logic controller. A novel technique that combines Q (λ)-learning with function approximation …

This paper presents a two stage learning technique that combines a particle swarm
optimization (PSO)-based fuzzy logic control (FLC) algorithm with the Q-Learning fuzzy …

The control of autonomous mobile robot in an unknown environments include many
challenge. Fuzzy logic controller is one of the useful tool in this field. Performance of fuzzy …