Journal of Shanghai Jiao Tong University

Journal of Shanghai Jiaotong University >

2018 , Vol. 52 >Issue 1: 45 - 53

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2018.01.008

Self-Collision Detection and Motion Planning for Dual-Arm Robot

Expand
  • 1. State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China; 2. Shanghai Aerospace Equipment Manufacturing Factory, Shanghai 200245, China; 3. Shanghai Institute of Aerospace Systems Engineering, Shanghai 201109, China

Online published: 2018-01-01

Fold

Abstract

A method based on geometric distance of space vector is proposed for the self-collision detection in motion planning of dual-arm robots. In terms of this, a linear repulsion force field which is improved from the artificial potential field method is used to describe the position relationship and collision quota among the links. Then a novel self-collision-free motion planning is derived by combining with the linear form of repulsion potential field. Compared with the traditional artificial potential filed planner, the improved linear force field can directly act as an operator to plan the trajectory of the dual-arm robot with properties that are simple and easy to be handled. Finally, a case study demonstrates the effectiveness of the motion planning algorithm with the real parameters of a dual-arm robot.

Key words： dual-arm robot; self-collision; collision detection; motion planning; potential field

Cite this article

WU Changzheng1，YUE Yi1,2，WEI Baochen1，LIU Dianfu3 . Self-Collision Detection and Motion Planning for Dual-Arm Robot[J]. Journal of Shanghai Jiaotong University, 2018 , 52(1) : 45 -53 . DOI: 10.16183/j.cnki.jsjtu.2018.01.008

References

［1］COHEN B, CHITTA S, LIKHACHEV M. Single- and dual-arm motion planning with heuristic search ［J］. Journal of Robotics Research, 2014, 33(2): 305-320. ［2］FEI Y Q, DING F Q, ZHAO X F. Collision-free motion planning of dual-arm reconfigurable robots［J］. Robotics & Computer Integrated Manufacturing, 2004, 20(4): 351-357. ［3］CHOSET H, LYNCH K M, HUTCHINSON S, et al. Principles of robot motion: Theory, algorithms, and implementations［M］. Massachusetts, USA: MIT Press, 2005. ［4］KUFFNER J, NISHIWAKI K, KAGAMI S. Self-collision detection and prevention for humanoid robots ［C］∥IEEE International Conference on Robotics and Automation (ICRA). Washington, USA: IEEE, 2002: 2265-2270. ［5］PARK C. Self-collision detection & avoidance algorithm for a robot manipulator［J］. International Journal of Engineering and Innovative Technology (IJEIT), 2015, 5(4): 139-142. ［6］陈友东, 宴亮, 谷平平. 双机器人系统的碰撞检测算法［J］. 北京航空航天大学学报, 2013, 39(12): 1644-1648. CHEN Youdong, YAN Liang, GU Pingping. Detection collision algorithm for two-manipulator system ［J］. Journal of Beijing University of Aeronautics and Astronautics, 2013, 39(12): 1644-1648. ［7］邹益胜. 虚拟样机环境下实时碰撞检测算法研究［D］. 成都: 西南交通大学机械工程学院, 2009. ［8］OUSSAMA K. Real-time obstacle avoidance for manipulators and mobile robots［J］. The International Journal of Robotics Research, 1986, 5(1): 90-98. ［9］姜力, 周扬, 孙奎, 等. 七自由度冗余机械臂避障控制［J］. 光学精密工程, 2013, 21(7): 1795-1801. JIANG Li, ZHOU Yang, SUN Kui, et al. Obstacle avoidance control for 7-DOF redundant manipulators ［J］. Optics and Precision Engineering, 2013, 21(7): 1795-1801. ［10］于振中, 闫继宏, 赵杰, 等. 改进人工势场法的移动机器人路径规划［J］. 哈尔滨工业大学学报, 2011, 43(1): 50-55. YU Zhenzhong, YAN Jihong, ZHAO Jie, et al. Mobile robot path planning based on improved artificial potential field method［J］. Journal of Harbin Institute of Technology, 2011, 43(1): 50-55. ［11］ELBANHAWI M M. Sampling-based robot motion planning: A review［J］. IEEE Access, 2014, 2(1): 56-77. ［12］LAVALLE S M. Rapidly-exploring random trees: A new tool for path planning［J］. Algorithmic & Computational Robotics New Directions, 1998: 293-308. ［13］KAVRAKI L, SVESTKA P, LATOMBE J, et al. Probabilistic roadmaps for path planning in high-dimensional configuration spaces［J］. IEEE Transactions on Robotics and Automation, 1996, 12(4): 566-580. ［14］ZUCKER M, RATLIFF N, DRAGAN A, et al. CHOMP: Covariant hamiltonian optimization for motion planning［J］. The International Journal of Robotics Research, 2013, 32(9/10): 1164-1193. ［15］KARAMAN SFRAZZOLI E. Sampling-based algorithms for optimal motion planning ［J］. The International Journal of Robotics Research, 2011, 30(7): 846-894. ［16］SCHULMAN J, DUAN Y, HO J, et al. Motion planning with sequential convex optimization and convex collision checking ［J］. The International Journal of Robotics Research, 2014, 33(9): 1251-1270. ［17］Aldebaran Robotic. Self-collision avoidance—NAO Software 1.14.5 documentation ［EB/OL］. ［2016-11-23］. http:∥doc.aldebaran.com/1-14/naoqi/motion/reflexes-collision-avoidance.html?highlight=self%20collision. ［18］SICILIANO B. Robotics: Modelling planning and control ［M］. Frist edition. London: Springer, 2009. ［19］CORKE P. Robotics, vision and control ［M］. Frist edition. Berlin, Germary: Springer, 2011.
Options
Outlines

/

〈 〉