A hybrid strategy for the time- and energy-efficient trajectory planning of parallel platform manipulators

被引:37
作者
Chen, Chun-Ta [1 ]
Liao, Te-Tan [2 ]
机构
[1] Da Yeh Univ, Dept Mech & Automat Engn, Dacun 51591, Changhwa, Taiwan
[2] Far E Univ, Dept Mech Engn, Tainan 744, Taiwan
关键词
Parallel platform manipulator; Trajectory planning; Particle swarm optimization; Conjugate gradient method; Hybrid strategy;
D O I
10.1016/j.rcim.2010.06.012
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In planning the trajectories of motor-driven parallel platform manipulators the objective is to identify the trajectory which accomplishes the assigned motion with the minimal travel time and energy expenditure subject to the constraints imposed by the kinematics and dynamics of the manipulator structure In this study the possible trajectories of the manipulator are modeled using a parametric path representation and the optimal trajectory is then obtained using a hybrid scheme comprising the particle swarm optimization method and the local conjugate gradient method The numerical results confirm the feasibility of the optimized trajectories and show that the hybrid scheme is not only more computationally efficient than the standalone particle swarm optimization method but also yields solutions of a higher quality (C) 2010 Elsevier Ltd All rights reserved
引用
收藏
页码:72 / 81
页数:10
相关论文
共 19 条
[1]  
Abdellatif H, 2005, IEEE INT CONF ROBOT, P411
[2]  
Afroun M, 2006, MED C CONTR AUTOMAT, P179
[3]  
AGELINE P, 1998, P 7 ANN C EV PROGR, P591
[4]  
[Anonymous], 1979, IFAC P, DOI [DOI 10.1016/S1474-6670(17)65584-8, 10.1016/S1474-6670(17)65584-8]
[5]   Optimal path programming of the Stewart platform manipulator using the Boltzmann-Hamel-d'Alembert dynamics formulation model [J].
Chen, Chun-Ta ;
Liao, Te-Tan .
ADVANCED ROBOTICS, 2008, 22 (6-7) :705-730
[6]   Minimum cost trajectory planning for industrial robots [J].
Chettibi, T ;
Lehtihet, HE ;
Haddad, M ;
Hanchi, S .
EUROPEAN JOURNAL OF MECHANICS A-SOLIDS, 2004, 23 (04) :703-715
[7]  
Constantinescu D, 2000, J ROBOTIC SYST, V17, P233, DOI 10.1002/(SICI)1097-4563(200005)17:5<233::AID-ROB1>3.0.CO
[8]  
2-Y
[9]  
ESMIN AAA, 2005, IEEE T POWER SYST, V1, P1
[10]  
Kennedy J, 1995, 1995 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS PROCEEDINGS, VOLS 1-6, P1942, DOI 10.1109/icnn.1995.488968