Quasi-Minimum Time Trajectory Planning and Experiments for Prototype Direct-Drive Robot Arm Driven by Stepping Motors Using GA
Author(s) -
Hiroyuki Kojima,
Kengo Motomura,
Yoshifumi Kuwano,
Keiichi Abe,
Hajime Hosoya
Publication year - 2010
Publication title -
international journal of automation technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.513
H-Index - 18
eISSN - 1883-8022
pISSN - 1881-7629
DOI - 10.20965/ijat.2010.p0161
Subject(s) - trajectory , control theory (sociology) , torque , acceleration , trajectory optimization , genetic algorithm , computer science , robot , tracking (education) , physics , control (management) , artificial intelligence , psychology , pedagogy , classical mechanics , astronomy , machine learning , thermodynamics
In this paper, a quasi-minimum time trajectory planning of three-link direct-drive robot arm driven with stepping motors using a genetic algorithm (GA) was proposed. The prototype direct-drive robot arm was newly manufactured in this study. The trajectory for a semiconductor wafer transfer work consists of three trajectory portions: a straight line, a curved line, and a straight line. In the trajectory planning, three trajectory portions are expressed by polynomials of time. Using the boundary and continuous conditions concerning joint angle, joint angular velocity and joint angular acceleration, the whole trajectory is described by a chromosome consisting of five genes. Then, the fitness function of the genetic algorithm for the quasi-minimum time control was defined, under the constraint condition that the stepping motor torques should not exceed pull-out torques. Furthermore, the numerical calculations and experiments have been carried out, and it is confirmed that the quasi-minimum time trajectory planning can be executed successfully, and the trajectory tracking control can be well performed.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom