Path Planning and Optimization of Humanoid Manipulator in Cartesian Space

doi:10.1007/s12204-022-2416-7

Abstract

Abstract: To solve the problems of low efficiency and multi-solvability of humanoid manipulator Cartesian space path planning in physical human-robot interaction, an improved bi-directional rapidly-exploring random tree algorithm based on greedy growth strategy in 3D space is proposed. The workspace of manipulator established based on Monte Carlo method is used as the sampling space of the rapidly-exploring random tree, and the opposite expanding greedy growth strategy is added in the random tree expansion process to improve the path planning efficiency. Then the generated path is reversely optimized to shorten the length of the planned path, and the optimized path is interpolated and pose searched in Cartesian space to form a collision-free optimized path suitable for humanoid manipulator motion. Finally, the validity and reliability of the algorithm are verified in an intelligent elderly care service scenario based on Walker2, a large humanoid service robot.

CLC Number:

TP242

LI Shiqi (李世其), LI Xiao∗ (李肖), HAN Ke (韩可), XIONG Youjun (熊友军), XIE Zheng (谢铮), CHEN Jinliang (陈金亮). Path Planning and Optimization of Humanoid Manipulator in Cartesian Space[J]. J Shanghai Jiaotong Univ Sci, 2022, 27(5): 614-620.

References 26

[1]	YANG T, ZOU H B, LIU X Y, et al. Simulated research on synchronization control of humanoid manipulator [J]. Computer Simulation, 2019, 36(7): 302-307 (in Chinese).
[2]	PANDEY A K, GELIN R. A mass-produced sociable humanoid robot: Pepper: the first machine of its kind [J]. IEEE Robotics & Automation Magazine, 2018, 25(3): 40-48.
[3]	OKITA S Y, NG-THOW-HING V, SARVADEVABHATLA R. Learning together: ASIMO developing an interactive learning partnership with children [C]//RO-MAN 2009—The 18th IEEE International Symposium on Robot and Human Interactive Communication. Toyama, Japan: IEEE, 2009: 1125-1130.
[4]	LV H H, YANG G, ZHOU H Y, et al. Teleoperation of collaborative robot for remote dementia care in home environments [J]. IEEE Journal of Translational Engineering in Health and Medicine, 2020, 8: 1400510.
[5]	ACKERMAN E. UBTECH shows off massive upgrades to Walker humanoid robot [EB/OL]. (2019-01- 08). https: // spectrum.ieee.org/automaton/robotics/ humanoids/ubtech-upgrades-Walker-humanoid-robot.
[6]	BAKERW, KINGSTON Z, MOLL M, et al. Robonaut 2 and You: Specifying and executing complex operations [C]//2017 IEEE Workshop on Advanced Robotics and its Social Impacts. Austin, TX, USA: IEEE, 2017: 1-8.
[7]	SCHMAUS P, LEIDNER D, KR¨UGER T, et al. Preliminary insights from the METERON SUPVIS Justin space-robotics experiment [J]. IEEE Robotics and Automation Letters, 2018, 3(4): 3836-3843.
[8]	HUO F C, CHI J, HUANG Z J, et al. Review of path planning for mobile robots [J]. Journal of Jilin University (Information Science Edition), 2018, 36(6): 639- 647 (in Chinese).
[9]	CHEN Q L, JIANG H Y, ZHENG Y J. Summary of rapidly-exploring random tree algorithm in robot path planning [J]. Computer Engineering and Applications, 2019, 55(16): 10-17 (in Chinese).
[10]	KHATIB O. Real-time obstacle avoidance for manipulators and mobile robots [J]. The International Journal of Robotics Research, 1986, 5(1): 90-98.
[11]	GAI S N, SUN R, CHEN S J, et al. 6-DOF robotic obstacle avoidance path planning based on artificial potential field method [C]//2019 16th International Conference on Ubiquitous Robots. Jeju, Korea: IEEE, 2019: 165-168.
[12]	QI R L, ZHOU W J, WANG T J. An obstacle avoidance trajectory planning scheme for space manipulators based on genetic algorithm [J]. Robot, 2014, 36(3): 263-270 (in Chinese).
[13]	WU C J, ZHOU S J, XIAO L C. Dynamic path planning based on improved ant colony algorithm in traffic congestion [J]. IEEE Access, 2020, 8: 180773-180783.
[14]	LI F, JIANG Q, QUANW, et al. Manipulation skill acquisition for robotic assembly using deep reinforcement learning [C]//2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Hong Kong, China: IEEE, 2019: 13-18.
[15]	KAMALI K, BONEV I A, DESROSIERS C. Realtime motion planning for robotic teleoperation using dynamic-goal deep reinforcement learning [C]//2020 17th Conference on Computer and Robot Vision. Ottawa, Canada: IEEE, 2020: 182-189.
[16]	RAVANKAR A A, RAVANKAR A, EMARU T, et al. HPPRM: hybrid potential based probabilistic roadmap algorithm for improved dynamic path planning of mobile robots [J]. IEEE Access, 2020, 8: 221743-221766.
[17]	SIMONIN E, DIARD J. BBPRM: a behavior-based probabilistic roadmap method [C]//2008 IEEE International Conference on Systems, Man and Cybernetics. Singapore: IEEE, 2008: 1719-1724.
[18]	LAVALLE S M . Rapidly-exploring random trees: A new tool for path planning [EB/OL]. [2021-05-18]. https://www.cs.csustan.edu/～xliang/Courses/CS4710- 21S/Papers/06%20RRT.pdf.
[19]	SUN F C, ZHANG Y N, SHI X H. Improved rapidly-exploring random tree path planning algorithm [J]. Transducer and Microsystem Technologies, 2017, 36(9): 129-131 (in Chinese).
[20]	KUFFNER J J, LAVALLE S M. RRT-connect: An efficient approach to single-query path planning [C]//IEEE International Conference on Robotics and Automation. San Francisco, CA, USA: IEEE, 2000: 995-1001.
[21]	KARAMAN S, FRAZZOLI E. Incremental samplingbased algorithms for optimal motion planning [M]//Robotics: Science and systems VI. Cambridge, MA, USA: MIT Press, 2011.
[22]	KARAMAN S, FRAZZOLI E. Sampling-based algorithms for optimal motion planning [J]. The International Journal of Robotics Research, 2011, 30(7):846- 894.
[23]	GAMMELL J D, SRINIVASA S S, BARFOOT T D. Informed RRT*: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic [C]//2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. Chicago, IL, USA: IEEE, 2014: 2997-3004.
[24]	LIU J Y, FAN P Q. Path planning of manipulator based on improved RRT*-connect algorithm [J]. Computer Engineering and Applications, 2021, 57(6): 274- 278 (in Chinese).
[25]	LIU Y L, ZUO G Y. Improved RRT path planning algorithm for humanoid robotic arm [C]//2020 Chinese Control and Decision Conference. Hefei, China: IEEE, 2020: 397-402.
[26]	BORDALBA R, ROS L, PORTA J M. A randomized kinodynamic planner for closed-chain robotic systems [J]. IEEE Transactions on Robotics, 2021, 37(1): 99- 115.