TY - GEN
T1 - Acquiring grasp strategies for a multifingered robot hand using evolutionary algorithms
AU - Hirayama, Chiaki
AU - Watanabe, Toshiya
AU - Kawabata, Shinji
AU - Suganuma, Masanori
AU - Nagao, Tomoharu
PY - 2017/11/27
Y1 - 2017/11/27
N2 - In recent years, significant research has been conducted on grasp planning for multifingered robot hands. These studies have focused on determining how to obtain suitable grasps from among an infinite number of candidate grasps. This domain's goal is a successful application to unknown environments through the adoption of the extracted grasps. Under difficult conditions, such as grasping a target object that is adjacent to other objects, manipulating robot hands by indicating grasping points has been insufficient. Instead, grasp strategies that construct movements using each finger's joint servo controls and robot hand movements should be used. In addition, it is necessary to automatically acquire various grasp strategies to apply to unknown environments. In this paper, we propose a method that automatically obtains grasp strategies using a real-coded genetic algorithm (RCGA), which is an evolutionary algorithm. This method derives grasp strategies by optimizing combinations and structures that consist of simple finger joint servo controls and robot hand movements. By applying our method to several objects on a simulator, we collected various grasp strategies capable of handling difficult conditions.
AB - In recent years, significant research has been conducted on grasp planning for multifingered robot hands. These studies have focused on determining how to obtain suitable grasps from among an infinite number of candidate grasps. This domain's goal is a successful application to unknown environments through the adoption of the extracted grasps. Under difficult conditions, such as grasping a target object that is adjacent to other objects, manipulating robot hands by indicating grasping points has been insufficient. Instead, grasp strategies that construct movements using each finger's joint servo controls and robot hand movements should be used. In addition, it is necessary to automatically acquire various grasp strategies to apply to unknown environments. In this paper, we propose a method that automatically obtains grasp strategies using a real-coded genetic algorithm (RCGA), which is an evolutionary algorithm. This method derives grasp strategies by optimizing combinations and structures that consist of simple finger joint servo controls and robot hand movements. By applying our method to several objects on a simulator, we collected various grasp strategies capable of handling difficult conditions.
UR - http://www.scopus.com/inward/record.url?scp=85044184435&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85044184435&partnerID=8YFLogxK
U2 - 10.1109/SMC.2017.8122843
DO - 10.1109/SMC.2017.8122843
M3 - Conference contribution
AN - SCOPUS:85044184435
T3 - 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
SP - 1597
EP - 1602
BT - 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
Y2 - 5 October 2017 through 8 October 2017
ER -