Bionic autonomous learning control of a two-wheeled self-balancing flexible robot

Abstract

This paper presents an OCPA (operant conditioning probabilistic automaton) bionic autonomous learning system based on Skinner's operant conditioning theory for solving the balance control problem of a two-wheeled flexible robot. The OCPA learning system consists of two stages: in the first stage, an operant action is selected stochastically from a set of operant actions and then used as the input of the control system; in the second stage, the learning system gathers the orientation information of the system and uses it for optimization until achieves control target. At the same time, the size of the operant action set can be automatically reduced during the learning process for avoiding little probability event. Theory analysis is made for the designed OCPA learning system in the paper, which theoretically proves the convergence of operant conditioning learning mechanism in OCPA learning system, namely the operant action entropy will converge to minimum with the learning process. And then OCPA learning system is applied to posture balanced control of two-wheeled flexible self-balanced robots. Robot does not have posutre balanced skill in initial state and the selecting probability of each operant in operant sets is equal. With the learning proceeding, the selected probabilities of optimal operant gradually tend to one and the operant action entropy gradually tends to minimum, and so robot gradually learned the posture balanced skill.

A study on the cognitive model of robot sensorimotor system1

“…Dominguez et al designed the competition neural network learning algorithm using Skinner's operant conditioning theory, and made the robot Arisco attain some selforganizing control skills [11]. Cai et al presented an OCPA (operant conditioning probabilistic automaton) bionic autonomous learning system based on Skinner's operant conditioning theory, and solved the balance control problem of a two-wheeled flexible robot [12]. Pinzon-Morales et al constructed a two hemispheric cerebellar neuronal network controller and applied to the control of an unstable 2-wheel balancing robot, and which was proved that the CNN controller could reproduce a basic form of unilateral learning similar to the real cerebellum [13].…”

Experimental investigation for better relative localization of a mobile robot using Taguchi method

“…Also, researchers have focused on the optimum path planning in mobile robots 14,26 and the stability analyses in mobile robots. 9,12,16,28 Robot's movement and abilities on particular terrain are affected by numerous factors like geometry and the type of locomotion system (wheeled, tracked, hybrid, legged, and jumping), properties of effectors (e.g., tyre type for wheeled robots), mass properties of a robot, and constraints resulting from characteristics of drives. 20,23 From the literature, it is observed that the majority of research is focused on the development of odometry error models, stability analyses, and path planning for mobile robots.…”