基于AOCA仿生学习模型的两轮机器人自主平衡学习研究  

Self-balance Learning of Two-wheeled Robot Based on Autonomous Operant Conditioning Automata

作　　者：戴丽珍[1] 杨刚[1] 阮晓钢[2] 

机构地区：[1]华东交通大学电气与电子工程学院,南昌330013 [2]北京工业大学电子信息与控制工程学院人工智能与机器人研究所,北京100124

出　　处：《自动化学报》2014年第9期1951-1957,共7页Acta Automatica Sinica

基　　金：国家自然科学基金项目(61075110);高等学校博士学科点专项科研基金(20101103110007);江西省教育厅科技项目(GJJ14402)资助~~

摘　　要：以两轮机器人的自主平衡学习控制为研究对象,针对传统控制方法无法实现机器人类似人或动物的渐进学习过程,依据斯金纳的操作条件反射理论建立了一种自治操作条件反射自动机(Autonomous operant conditioning automaton,AOCA)模型,设计一种基于AOCA的仿生学习算法,并进行机器人姿态平衡学习实验仿真研究.实验结果表明,基于AOCA的仿生学习方法能有效地实现机器人的自主平衡学习控制,机器人系统的平衡能力在学习控制过程中自组织地渐进形成,并得以发展和完善.Since the gradual learning process like humans or animals of two-wheeled robot cannot be realized by the traditional control methods, an autonomous operant conditioning automaton （AOCA） is established based on Skinner＇s theory of operant conditioning for self-balance learning control of robots. A bionic learning algorithm based on AOCA is proposed to balance the two-wheeled robot. The corresponding simulation experiments for self-balance learning control of the two wheeled robot are given, in which the robot effectively realizes autonomous balance. Theoretical analysis and simulation show that the autonomous operant conditioning automata bionic learning model applied to the two-wheeled robot for autonomous balance learning control makes the robot progressive formation of self-organization, development and improvement of its balance.

关 键 词：两轮机器人 自主平衡 仿生学 操作条件反射 自学习 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]