Overcoming the Instability defects with reliability using Alpha Ramda Humanoid robot for flexible trajectory with high precision using Generative Adversarial AI Model

It is well known that during different movements of humanoid robot due to the lag in the concurrency of data received through sensing with various sensors while the robot moves errors are generated and the distance and time factors varies. Hence, it is desired to make them concurrency thereby avoiding the robots difference in time factor, arising in the same distance moved with sensor error lags This is a crucial problem which is overcome by introducing the correction factor derived from the error of movement into the humanoid robot’s microcontroller through the algorithm which reduces error and thereby a concurrency is achieved after several experiments and evidenced through the implementation of the correction factor Fast and flexible walking is necessary for humanoid robots in the Robocup soccer competition, Instability is one of the major defects in humanoid robots. Recently, various methods on the stability and reliability of humanoid robots have been actively studied and proposed a next fuzzy-logic control scheme that would enable the robot to realize flexible walking or turning with high standard of stability by restricting the step length and inclining the body of robot to an appropriate extent. In this paper, a stabilization algorithm is proposed using the balance condition of the robot, which is measured using accelerometer sensors during standing, walking, and turning movement are estimated from these data. From this information the robot selects the proper motion pattern effectively by using Generative Adversarial Model with respect of other AI Models.