TY - JOUR
T1 - Analysis and multi-objective optimization of a kind of teaching manipulator
AU - Fan, Zhun
AU - You, Y.
AU - Cai, X.
AU - Zheng, Haodong
AU - Zhu, Guijie
AU - Li, W.
AU - Garg, A.
AU - Deb, Kalyanmoy
AU - Goodman, Erik
N1 - Publisher Copyright:
© 2019
PY - 2019/11
Y1 - 2019/11
N2 - Designing and setting manipulator trajectories in a programming system can be a tedious and time-consuming task for manufacturers. In this paper, one kind of six degree-of-freedom (DOF) teaching manipulator is designed and developed for conveniently setting and recording trajectories for industrial robots. A constrained multi-objective optimization problem is formulated to optimize the design of the teaching manipulator. Two performance indexes, i.e. the magnitude of the peak operating force and difference between the maximum and minimum magnitude of operating forces are adopted as the objectives. Two PPS-based (push and pull search) algorithms, including PPS-MOEA/D and PPS-M2M, are suggested to solve the formulated CMOP. Several state-of-the-art CMOEAs, including MOEA/D-ACDP, MOEA/D-CDP, NSGA-II-CDP and CM2M, are also tested. The experimental results indicate that PPS-MOEA/D has the best performance among the six compared algorithms, and the PPS-based methods as a group outperform their counterparts without adopting the PPS framework, which demonstrates the superiority of the PPS framework for solving real-world optimization problems.
AB - Designing and setting manipulator trajectories in a programming system can be a tedious and time-consuming task for manufacturers. In this paper, one kind of six degree-of-freedom (DOF) teaching manipulator is designed and developed for conveniently setting and recording trajectories for industrial robots. A constrained multi-objective optimization problem is formulated to optimize the design of the teaching manipulator. Two performance indexes, i.e. the magnitude of the peak operating force and difference between the maximum and minimum magnitude of operating forces are adopted as the objectives. Two PPS-based (push and pull search) algorithms, including PPS-MOEA/D and PPS-M2M, are suggested to solve the formulated CMOP. Several state-of-the-art CMOEAs, including MOEA/D-ACDP, MOEA/D-CDP, NSGA-II-CDP and CM2M, are also tested. The experimental results indicate that PPS-MOEA/D has the best performance among the six compared algorithms, and the PPS-based methods as a group outperform their counterparts without adopting the PPS framework, which demonstrates the superiority of the PPS framework for solving real-world optimization problems.
KW - Constrained multi-objective optimization
KW - Robot modeling
KW - Teaching manipulator design
UR - http://www.scopus.com/inward/record.url?scp=85070352446&partnerID=8YFLogxK
U2 - 10.1016/j.swevo.2019.06.011
DO - 10.1016/j.swevo.2019.06.011
M3 - Article
AN - SCOPUS:85070352446
SN - 2210-6502
VL - 50
JO - Swarm and Evolutionary Computation
JF - Swarm and Evolutionary Computation
M1 - 100554
ER -