首页 » 文章 » 文章详细信息
International Journal of Advanced Robotic Systems Volume 16 ,Issue 3 ,2019-05-07
A sampling-based optimized algorithm for task-constrained motion planning
Research Article
Kai Mi 1 , 2 Haojian Zhang 1 , 2 Jun Zheng 1 Jianhua Hu 1 Dengxiang Zhuang 1 , 2 Yunkuan Wang 1
Show affiliations
DOI:10.1177/1729881419847378
Received 2018-7-26, accepted for publication 2019-4-2, Published 2019-4-2
PDF
摘要

We consider a motion planning problem with task space constraints in a complex environment for redundant manipulators. For this problem, we propose a motion planning algorithm that combines kinematics control with rapidly exploring random sampling methods. Meanwhile, we introduce an optimization structure similar to dynamic programming into the algorithm. The proposed algorithm can generate an asymptotically optimized smooth path in joint space, which continuously satisfies task space constraints and avoids obstacles. We have confirmed that the proposed algorithm is probabilistically complete and asymptotically optimized. Finally, we conduct multiple experiments with path length and tracking error as optimization targets and the planning results reflect the optimization effect of the algorithm.

关键词

asymptotically optimization;sampling based;path planning;task space constraints;Redundant manipulators

授权许可

© The Author(s) 2019
This article is distributed under the terms of the Creative Commons Attribution 4.0 License (http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).

图表
通讯作者

Jun Zheng.Intelligent Manufacturing Technology and System Research Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.jun.zheng@ia.ac.cn

推荐引用方式

Kai Mi,Haojian Zhang,Jun Zheng,Jianhua Hu,Dengxiang Zhuang,Yunkuan Wang. A sampling-based optimized algorithm for task-constrained motion planning. International Journal of Advanced Robotic Systems ,Vol.16, Issue 3(2019)

您觉得这篇文章对您有帮助吗?
分享和收藏
0

是否收藏?

参考文献
[1] L Kavraki, P Svestka, JC Latombe. Probabilistic roadmaps for path planning high-dimensional configuration spaces. IEEE Trans Robot Autom 1996; 12(4): 566–580.
[2] S Huang, Y Peng, W Wei. Clamping weighted least-norm method for the manipulator kinematic control: avoiding joint limits. In: 33rd Chinese control conference (CCC), Nanjing, China, 28–30 July 2014, pp. 8309–8314. Piscataway, NJ: IEEE.
[3] W Wang, M Zhu, X Wang. An improved artificial potential field method of trajectory planning and obstacle avoidance for redundant manipulators. Int J Adv Robot Syst 2018; 15(5): 1–13.
[4] AD Luca, G Oriolo. The reduced gradient method for solving redundancy in robot arms. IFAC Proc Vol 1990, 23(8): 133–138.
[5] M Hoy, AS Matveev, AV Savkin. Algorithms for collision free navigation of mobile robots in complex cluttered environments: a survey. Robotica 2015; 33(3): 463–497.
[6] SM Lavalle. Rapidly-exploring random trees: a new tool for path planning. Technical report, Department of Computer Science, Iowa State University, 1998.
[7] B Siciliano. Kinematic control of redundant robot manipulators: a tutorial. J Intell Robot Syst 1990; 3(3): 201–212.
[8] O Khatib. Real-time obstacle avoidance for manipulators and mobile robots. In: IEEE International Conference on Robotics and Automation, St. Louis, USA, 25–28 March 1985, pp. 500–505. Piscataway, NJ: IEEE.
[9] SM LaValle, JJ Kuffner. Rapidly-exploring random trees: progress and prospects. In: Workshop on algorithmic foundations of robotics, 2000.
[10] JJ Kuffner, SM LaValle. RRT-connect: an efficient approach to single-query path planning. In: IEEE international conference on robotics and automation, Piscataway, USA, 24–28 April 2000, pp. 995–1001. Piscataway, NJ: IEEE.
[11] S Huang, Y Peng, W Wei. Clamping weighted least-norm method for the manipulator kinematic control with constraints. Int J Control 2016; 89(11): 2240–2249.
[12] R Bellman. Dynamic programming. Science 1966; 153(3731): 34–37.
[13] AS Deo. Application of optimal damped least-squares method to inverse kinematics of robotic manipulators. Master’s Thesis, Department of Electrical and Computer Engineering, Rice University, Houston, TX, April 1991.
[14] P Wenger. Cuspidal and noncuspidal robot manipulators. Robotica 2007; 25(6): 677–689.
[15] M Stilman. Task constrained motion planning in robot joint space. In: IEEE/RSJ international conference on intelligent robots and systems, Vancouver, Canada, 24–28 September 2007, pp. 3074–3081. Piscataway, NJ: IEEE.
[16] G Oriolo. Stabilization of self-motions in redundant robots. In: IEEE international conference on robotics and automation, 1994, pp. 704–709. Piscataway, NJ: IEEE.
[17] G Oriolo, M Ottavi, M Vendittelli. Probabilistic motion planning for redundant robots along given end-effector paths. In: IEEE/RSJ international conference on intelligent robots and systems, Lausanne, Switzerland, 30 September–4 October 2002, pp. 1657–1662. Piscataway, NJ: IEEE.
[18] A Mclean, S Cameron. The virtual springs method: path planning and collision avoidance for redundant manipulators. Int J Robot Res 1996; 15(4): 300–319.
[19] DP Martin, J Baillieul, JM Hollerbach. Resolution of kinematic redundancy using optimization techniques. IEEE Trans Robot Autom 1989; 5(4): 529–533.
[20] JR Ray. Nonholonomic constraints. Am J Phys 1966; 34(5): 406–408.
[21] S Chiaverini, G Oriolo, I Walker. Kinematically redundant manipulators. In: KhatibOSicilianoB (eds) Handbook of robotics. Germany: Springer, 2009, pp. 245–268.
[22] S Karaman, E Frazzoli. Sampling-based algorithms for optimal motion planning. Int J Rob Res 2011; 30(7): 846–894.
[23] J Nasir, F Islam, U Malik. RRT*-SMART: a rapid convergence implementation of RRT*. Int J Adv Robot Syst 2013; 10(7): 1–12.
[24] PE Hart, NJ Nilsson, B Raphael. A formal basis for the heuristic determination of minimum cost paths. IEEE Trans Syst Sci Cybern 1968; 4(2): 100–107.
[25] N Ratliff, M Zucker, JA Bagnell. CHOMP: gradient optimization techniques for efficient motion planning. In: IEEE international conference on robotics and automation, Kobe, Japan, 12–17 May 2009, pp. 489–494. Piscataway, NJ: IEEE.
[26] J Pan, S Chitta, D Manocha. FCL: a general purpose library for collision and proximity queries. In: IEEE international conference on robotics and automation, Minnesota, USA, 14–18 May 2012, pp. 3859–3866. Piscataway, NJ: IEEE.
[27] M Kalakrishnan, S Chitta, E Theodorou. STOMP: stochastic trajectory optimization for motion planning. In: IEEE international conference on robotics and automation, Shanghai, China, 9–13 May 2011, pp. 4569–4574. Piscataway, NJ: IEEE.
[28] M Mukadam, X Yan, B Boots. Gaussian process motion planning. In: IEEE international conference on robotics and automation, Stockholm, Sweden, 16–21 May 2016, pp. 9–15. Piscataway, NJ: IEEE.
[29] CA Klein, CH Huang. Review of pseudoinverse control for use with kinematically redundant manipulators. IEEE Trans Syst Man Cybern 1983; 13(3): 245–250.
[30] MoveIt! software. http://moveit.ros.org (accessed 5 June 2018).
[31] M Cefalo, G Oriolo, M Vendittelli. Planning safe cyclic motions under repetitive task constraints. In: IEEE international conference on robotics and automation, Karlsruhe, Germany, 6–10 May 2013, pp. 3807–3812. Piscataway, NJ: IEEE.
[32] G Oriolo, M Cefalo, M Vendittelli. Repeatable motion planning for redundant robots over cyclic tasks. IEEE Trans Robot 2017; 99: 1–14.
[33] G Oriolo, M Vendittelli. A control-based approach to task-constrained motion planning. In: IEEE/RSJ international conference on intelligent robots and systems, St Louis, MO, USA, 10–15 October 2009, pp. 297–302. Piscataway, NJ: IEEE.