Coevolution of Myoelectric Hand Control under the Tactile Interaction among Fingers and Objects  

作　　者：Yuki Kuroda Yusuke Yamanoi Shunta Togo Yinlai Jiang Hiroshi Yokoi 

机构地区：[1]Joint Doctoral Program for Sustainability Research,Graduate School of Informatics and Engineering The University of Electro-Communications,Tokyo,Japan [2]Department of Mechan ical and Intelligent System Engineering,Graduate School of Informatics and Engineering The University of Electro Communications Tokyo,Japan [3]Center for Neuroscience and Biomedical Engineering,The University of Electro Communications Tokyo,Japan [4]Bejing Innovation Center for Inelligent Robots and Systems,Beijing,China

出　　处：《Cyborg and Bionic Systems》2022年第1期285-302,共18页类生命系统（英文）

基　　金：supported by JSPS KAKENHI Grant Num-bers JP18H03761,JP19K14941,JP19K12877,JP21K14125,and JST No.YA290326.

摘　　要：The usability of a prosthetic hand difers significantly from that of a real hand.Moreover,the complexity of manipulation increases as the number of degrees of freedom to be controlled increases,making manipulation with biological signals extremely difficult.To overcome this problem,users need to select a grasping posture that is adaptive to the object and a stable grasping method that prevents the object from flling.In previous studies,these have been left to the operating skills of the user,which is extremely difficult to achieve.In this study,we demonstrate how stable and adaptive grasping can be achieved according to the object regardless of the user's operation technique.The required grasping technique is achieved by determining the correlation bet ween the motor output and each sensor through the interaction between the prosthetic hand and the surrounding stimuli,such as myoelectricity,sense of touch,and grasping objects.The agents of the 16 DOF robot hand were trained with the myoelectric signals of six participants,including one child with a congenital forearm deficiency.Consequently,each agent could open and close the hand in response to the myoeletric stimuli and could accomplish the object pickup task.For the tasks,the agents successfully identified grasping patterns suitable for practical and stable positioning of the objects.In addition,the agents were able to pick up the object in a similar posture regardless of the partiipant,suggesting that the hand was optimized by evolutionary computation to a posture that prevents the object from being dropped.

关 键 词：ROBOT OBJECTS OVERCOME 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]