SUN Nong-liang (孙农亮)1,2, WANG Yan-jun (王艳君)1
(1. College of Information and Electrical Engineering, Shandong University of Science and Technology, Qingdao 266510, China; 2. Engineering College, UC Santa Barbara, Santa Barbara, CA 93106, USA)
Abstract：This paper investigates a trajectory planning algorithm to reduce the manipulator's working time. A time-optimal trajectory planning (TOTP) is conducted based on improved adaptive genetic algorithm (IAGA) and combined with cubic triangular Bezier spline (CTBS). The CTBS based trajectory planning we did before can achieve continuous second and third derivation, hence it meets the stability requirements of the manipulator. The working time can be greatly reduced by applying IAGA to the puma560 trajectory planning when considering physical constraints such as angular velocity, angular acceleration and jerk. Simulation experiments in both Matlab and ADAMS illustrate that TOTP based on IAGA can give a time optimal result with smoothness and stability.
Key words：time-optimal trajectory planning (TOTP)； improved adaptive genetic algorithm (IAGA)； cubic triangular Bezier spline (CTBS)
CLD number： TP242.2 Document code： A
Article ID： 1674-8042(2012)02-0103-06 doi： 10.3969/j.issn.1674-8042.2012.02.001
References
[1] CAI Zi-xing. Robotics. Beijing: Tsinghua University Press, 2000, 25-28.
[2] Choi Y K, Park J H, Kim H S, et al. Optimal trajectory planning and sliding mode control for robots using evolution strategy. Robotica, 2000, 18: 423-428.
[3] ZHANG Hong-qiang. Time-optimal trajectory planning of industrial robot. Hunan University, Changsha, 2004, 16-22.
[4] Mirostaw G. The planning of robotic optimal motions in the presence of obstacles. The International Journal of Robotics Researeh, 1998, 17: 248-259.
[5] Zalzala A M S, Morris A S. A distributed on-line trajectory generator for intelligent sensory-based manipulators. Robotica, 1991, 9(2)：145-155.
[6] Dubowsky S, Shiller A, Shiller Z. Time optimal trajectory planning for robotic manipulators with obstacle avoidance: A CAD approach. Proc. of IEEE Conference on Robotics and Automation, 1986, 3: 1906-1912.
[7] Lee Y D, Lee B H, Kim H G. An evolutionary approach for time optimal trajectory planning of a robotic manipulator. Information Sciences, 1999, 113(3-4)：245-260.
[8] Holland J H. Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor, Mich. 1992, 35-72.
[9] LEI Ying-jie, ZHANG Shan-wen, LI Xu-wu. Application of genetic algorithm toolbox. Xi'an: Xi'an University of Electronic Science and Technology Press, 2005, 12-133.
[10] Srinivas M， Patnaik L M. Genetic algorithms: a survey. Computer, 1994, 27: 17-26.
[11] Srinivas M, Patnaik L M. Adaptive probabilities of crossover and mutation in genetic algorithms. Systems. IEEE Trans. On Man and Cybernetics, 1994, 24: 656-667.
[12] LI Jun, LI yan-hui, PENG Cun-yin. Path test data generation based on adaptive genetic algorithm. Computer Engineering, 2009, 35(2): 203-205.
[13] ZHANG Huai-zhu, XIANG Chang-bo, SONG Jian-zhong, et al. Application of improved adaptive genetic algorithm to image segmentation in real-time. Optics and Precision Engineering, 2008, 16: 333-337.
[14] Zhang Jing-zhao, Jiang Tao. Improved adaptive genetic algorithm. Computer Engineering and Applications, 2010, 46(11): 53-55.
[15] WANG Yan-jun, XU Wen-long, SUN Nong-liang. Manipulator trajectory planning based on the cubic triangular Bezier spline. Proc. of 8th World Congress on Intelligent Control and Automation, Jinan, 2010, 6485-6488.
[full text view]
