收稿日期: 2016-01-04
修回日期: 2016-07-18
网络出版日期: 2016-12-01
基金资助
广东省-教育部产学研合作专项资金项目( 2013B090500093) ; 广东省科技计划项目( 2015A020219001) ; 国家自然科学基金面上项目( 61372140) ; 广州市机器人软件及复杂信息处理重点实验室( 15180007) ; 广州市科技创新委员会项目 ( 201609010075)
Falling Detection and Control of Humanoid Robots Based on Multi-Sensor Information Fusion
Received date: 2016-01-04
Revised date: 2016-07-18
Online published: 2016-12-01
Supported by
Supported by the Industry-University-Research Joint Project of Guangdong Province and the Ministry of Education ( 2013B090500093) , the Science and Technology Planning Projects of Guangdong Province( 2015A020219001) and the General Program of the National Natural Science Foundation of China( 61372140)
毕盛 刘皓熙 闵华清 董敏 黄鑫龙 . 基于多传感器信息融合的仿人机器人跌倒检测及控制[J]. 华南理工大学学报(自然科学版), 2017 , 45(1) : 95 -101 . DOI: 10.3969/j.issn.1000-565X.2017.01.014
When humanoid robots are walking,it is essential to detect in time whether the robots are going to fall,so as to prevent the robots from the falling or reduce the damage from the falling with the help of the corresponding protection actions.In this paper,a multi-sensor information fusion model in the stage of walking is constructed by using attitude sensors ( including an acceleration sensor and a gyroscope) and force sensor resistors ( FSR) .Then,a comprehensive judgment method of the falling detection of a humanoid robot is proposed by adopting fuzzy logic decision method,and the corresponding controllers are designed to detect whether the robot is going to fall.If the falling is unavoidable,the corresponding protection actions will be adopted.Otherwise,if the falling can be avoided,it will be prevented by controlling hip joints.The experimental results of SCUT-I humanoid robot show that the proposed method can detect the moment at which the robot is going to fall,and then prevent the falling by using the stability controller or reduce the damage to the robot by using the falling controller to bring about the corresponding protection actions.
Key words: humanoid robot; control; multi-sensor fusion; falling detection
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