Trajectory planning of the nursing robot based on the center of gravity for aluminum alloy structure
Source abstract
Abstract
Abstract In this paper, the robot arm is manufactured to increase the structural strength and improve safety. The stability of the nursing robot in the process of carrying out the typical nursing task of holding patients was studied, and the influence of the center of gravity on the movement stability of the nursing robot was analyzed. The mathematical model of the stability of the robot is built by using the inverse kinematics solution of the robot. By studying the trajectory planning of a nursing robot under the condition of ZMP constraint, the robot can move safely and optimally along the prescribed trajectory between two working points. The simulation results show that the algorithm can significantly improve the work safety of the robot. In the experiment, four pressure sensors are used to measure the pressure of four wheels on the ground, the data are obtained and substituted into the expression of center of pressure (COP) method. The results show that the stability is in a reasonable moving area without any hidden danger, and its COP value is less than the stable qualitative boundary, which verifies the rationality and effectiveness of the optimal center of gravity stability planning algorithm.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Robotic Path Planning Algorithms · Control and Dynamics of Mobile Robots · Robotic Mechanisms and Dynamics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical Engineering and Automation, Beihang University , Beijing 100191 , China
Beijing General Research Institute of Electronic Engineering , Beijing 100854 , People’s Republic of China
Beijing Institute of Astronautical Systems Engineering , Beijing 100076 , People’s Republic of China
References (37)
Show source-provided references
1. Robinson, H., B. MacDonald, and E. Broadbent. The role of healthcare robots for older people at home: a review. International Journal of Social Robotics, Vol. 6, No. 4, 2014, pp. 575–591.10.1007/s12369-014-0242-2 ↗
2. Fischinger, D., P. Einramhof, K. Papoutsakis, W. Wohlkinger, P. Mayer, P. Panek, et al. Hobbit, a care robot supporting independent living at home: first prototype and lessons learned. Robotics and Autonomous Systems, Vol. 75, 2016, pp. 60–78.10.1016/j.robot.2014.09.029 ↗
3. Manti, M., A. Pratesi, E. Falotico, M. Cianchetti, and C. Laschi Soft assistive robot for personal care of elderly people. IEEE International Conference on Biomedical Robotics and Biomechatronics, 2016, pp. 833–838.10.1109/BIOROB.2016.7523731 ↗
4. Mukai, T., A. Hirano, and H. Nakashima. Development of a nursing-care assistant robot riba that can lift a human in its arms. IEEE International Conference on Intelligent Robots and Systems, 2010, pp. 5996–6001.10.1109/IROS.2010.5651735 ↗
5. Berns, K. and S. A. Mehdi. Use of an autonomous mobile robot for elderly care. IEEE Computer Society, 2010, pp. 121–126.10.1109/ATEQUAL.2010.30 ↗
6. Tsukahara, A., R. Kawanishi, Y. Hasegawa, and Y. Sankai. Sit-to-stand and stand-to-sit transfer support for complete paraplegic patients with robot suit HAL. Advanced Robot, Vol. 24, No. 11, 2010, pp. 1615–1638.10.1163/016918610X512622 ↗
7. Pollack, M. E., L. Brown, D. Colbry, C. Orosz, B. Peintner, S. Ramakrishnan, et al. Pearl: a mobile robotic assistant for the elderly. Proceedings AAAI Workshop, AAAI Technical Report WS-02-0, 2002.
8. Yoshida, E. and M. Poirier. Whole-body motion planning for pivoting based manipulation by humanoids. Applied Bionics and Biomechanics, Vol. 3, No. 3, 2006, pp. 227–235.10.1155/2006/683746 ↗
9. Liu, Z., L. Li, G. Wang, J. Chen, and J. Yi. A semi-analytical model for predicting the machining deformation of thin-walled parts considering machining-induced and blank initial residual stress. The International Journal of Advanced Manufacturing Technology, Vol. 110, No. 5, 2020, pp. 139–161.10.1007/s00170-020-05862-1 ↗
10. Gao, H., Y. Zhang, Q. Wu, and J. Song. An analytical model for predicting the machining deformation of a plate blank considers biaxial initial residual stresses. International Journal of Advanced Manufacturing Technology, Vol. 93, No. 1–4, 2017, pp. 1473–1486.10.1007/s00170-017-0528-2 ↗
11. Archibald, M. M. and A. Barnard. Futurism in nursing: technology, robotics and the fundamentals of care. J Clin Nurs, Vol. 27, No. 11–12, 2017, pp. 2473–2480.10.1111/jocn.14081 ↗
12. Shin, H. K. and B. K. Kim. Energy-efficient gait planning and control for biped robots utilizing the allowable ZMP region. IEEE Transactions on Robotics, Vol. 30, No. 4, 2014, pp. 986–993.10.1109/TRO.2014.2305792 ↗
13. Jezernik, S., G. Colombo, T. Keller, H. Frueh, and M. Morari, Robotic orthosis lokomat: a rehabilitation and research tool. Neuromodulation, Vol. 6, No. 2, 2003, pp. 108–115.10.1046/j.1525-1403.2003.03017.x ↗
14. Mohammad, N. and V. M. Jaime. A statistical approach for uncertain stability analysis of mobile robot. 2013 IEEE International Conference on Robotics and Automation (ICRA) Karlsruhe, Germany, 2013, pp. 191–196.
15. Vukobratovic, M. and D. Juricic. Contributions to the synthesis of biped gait. IEEE Trans on Bio-Medical Engineerin, Vol. 16, No. 1, 1969, pp. 1–6.10.1109/TBME.1969.4502596 ↗
16. Napoleon, S. and M. Nakaura. Sampel balance control analysis of humanoid robot based on ZMP feedback control. Proceedings of the 2002 IEEE/RSJ International Conference on Intelligent Robots and System. Lausanne (Swizerland), 2002, pp. 2437–2442.10.1109/IRDS.2002.1041633 ↗
17. Plestan, F., J. W. Grizzle, E. R. Westervelt, and G. Abba. Stable walking of a 7-DOF biped robot. IEEE Transactions on Robotics and Automation, Vol. 19, No. 4, 2004, pp. 653–668.10.1109/TRA.2003.814514 ↗
18. Su, X., G. M. Xu, and D. H. Jiang. Abatement of segregation with the electro and static magnetic field during twin-roll casting of 7075 alloy sheet. Materials Science and Engineering A, Vol. 599, 2014, pp. 279–285.10.1016/j.msea.2014.01.042 ↗
19. Wang, S., L. Meng, S. Yang, C. Fang, H. Hao, S. Dai, et al. Microstructure of Al–Zn–Mg–Cu–Zr–0.5Er alloy under as-cast and homogenization conditions. Transactions of Nonferrous Metals Society of China, Vol. 21, 2011, pp. 1449–1454.10.1016/S1003-6326(11)60880-7 ↗
20. Chegini, M. and M. H. Shaeri. Effect of equal channel angular pressing on the mechanical and tribological behavior of Al–Zn–Mg–Cu alloy. Materials Characterization, Vol. 140, 2018, pp. 147–161.10.1016/j.matchar.2018.03.045 ↗
21. Peng, L. M., X. M. Mao, and K. D. Xu. Simulation and control model for interactions among process parameters of directional solidification continuous casting. Transactions of Nonferrous Metals Society of China, Vol. 10, 2000, pp. 449–452.
22. Chen, G., C. Ren, Z. Ke, J. Li, and X. Yang. Modeling of flow behavior for 7050-T7451 aluminum alloy considering microstructural evolution over a wide range of strain rates. Mechanics of Materials, Vol. 95, 2016, pp. 146–157.10.1016/j.mechmat.2016.01.006 ↗
23. Wang, G. S., Z. H. Zhao, and J. Z. Cui. The magnetic field interference in dual-ingot low frequency electromagnetic continuous casting. Advanced Materials Research, Vol. 821, 2013, pp. 868–872.10.4028/www.scientific.net/AMR.821-822.868 ↗
24. Li, D., D. Zhang, S. Liu, Z. Shan, X. Zhang, Q. Wang, et al. Dynamic recrystallization behavior of 7085 aluminum alloy during hot deformation. Transactions of Nonferrous Metals Society of China, Vol. 26, 2016, pp. 1491–1497.10.1016/S1003-6326(16)64254-1 ↗
25. Evanoff, B. and L. Wolf. Reduction in injury rates in nursing personnel through introduction of mechanical lifts in the workplace. Amer J Indust Med, Vol. 44, No. 5, 2003, pp. 451–457.10.1002/ajim.10294 ↗
26. Kallmann, M. Analytical inverse kinematics with body posture control. Computer Animation and Virtual Worlds, Vol. 19, No. 2, 2008, pp. 79–91.10.1002/cav.176 ↗
27. Gook, B. and J. Rosen. Kinematic analysis of 7 degrees of freedom upper-limb exoskeleton robot; with tilted shoulder abduction. International Journal of Precision Engineering & Manufacturing, Vol. 14, No. 1, 2013, pp. 69–76.10.1007/s12541-013-0011-4 ↗
28. Denavit, J. and R. S. Hartenberg. A kinematic notation for lower-pair mechanisms based on matrices. Journal of Applied Mechanics, Vol. 21, No. 5, 1995, pp. 215–221.10.1115/1.4011045 ↗
29. Chirikjian, G. S. and J. W. Burdick. A modal approach to hyper-redundant manipulator kinematics. Robotics & Automation IEEE Transactions on, Vol. 10, No. 3, 1994, pp. 343–354.10.1109/70.294209 ↗
30. Xu Z., S. Wei, N. Wang, and X. Zhang. Trajectory planning with Bezier curve in cartesian space for industrial gluing robot. International Conference on Intelligent Robotics and Applications, 2014, pp. 146–154.10.1007/978-3-319-13963-0_15 ↗
31. Lee, C. S. G. and M. Ziegler. Geometric approach in solving inverse kinematics of PUMA robots. IEEE Transactions on Aerospace and Electronic Systems, Vol. 6, 1984, pp. 695–706.10.1109/TAES.1984.310452 ↗
32. Atawnih, A., D. Papageorgiou, and Z. Doulgeri. Kinematic control of redundant robots with guaranteed joint limit avoidance. Robotics & Autonomous Systems, Vol. 79, 2016, pp. 122–131.10.1016/j.robot.2016.01.006 ↗
33. Eiichi, Y. and P. Mathieu. Whole-body motion planning for pivoting based manipulation by humanoids. Applied Bionics and Biomechanics, Vol. 3, No. 3, 2006, pp. 227–235.10.1533/abbi.2006.0031 ↗
34. Lai, C. K. and K. K. Shyu. A novel motor drive design for incremental motion system via sliding-mode control method. IEEE Transactions on Industrial Electronics, Vol. 52, No. 2, 2005, pp. 499–507.10.1109/TIE.2005.844230 ↗
35. Kanoun, O., F. Lamiraux, and P. B. Wieber. Kinematic control of redundant manipulators: generalizing the task-priority framework to inequality task. IEEE Transactions on Robotics, Vol. 27, No. 4, 2011, pp. 785–792.10.1109/TRO.2011.2142450 ↗
36. Paul, R. P. and C. N. Stevenson. Kinematics of robot wrist. The International Journal of Robotics Research, Vol. 2, No. 1, 1983, pp. 31–38.10.1177/027836498300200103 ↗
37. Albert, A. and W. Gerth. Analytic path planning algorithms for bipedal robots without a trunk. Journal of Intelligent and Robotic systems, Vol. 36, 2003, pp. 109–127.10.1023/A:1022600522613 ↗