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Smooth Trajectory Generation Method Using Quadratic Programming Method

이차 계획법을 활용한 부드러운 궤적 생성 방법

  • Sung, Minchang (Department of Electrical and Electronic Engineering, Hanyang University) ;
  • Choi, Youngjin (Department of Electrical and Electronic Engineering, Hanyang University)
  • Received : 2022.03.05
  • Accepted : 2022.04.06
  • Published : 2022.08.31

Abstract

This paper proposes a method that can generate a smooth trajectory from the discontinuous trajectory in kinematic, dynamic, and task-space trajectory constraints. The problem is defined as the minimization of kinetic energy, and then the simulation is performed by using the MATLAB. Kinematic and inverse kinematic equations are derived for the simulation of the 6-DOF robotic arm. The simulation results showed that the trajectory of each joint is generated while satisfying the constraints without any discontinuity. There are small errors in the Cartesian trajectory, but unnecessary deceleration and acceleration can be eliminated. In addition, it is possible to quickly switch between the robotic tasks by applying the proposed method.

Keywords

Acknowledgement

This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2019R1 A2C1088375), and in part by the Technology Innovation Program funded by the Korean Ministry of Trade, industry and Energy, (20008908), Republic of Korea

References

  1. C.-H. Lee, "Industrial Robot," Institute for Information Technology Advancement, Jincheon, Korea, [Online], https://scienceon.kisti.re.kr/srch/selectPORSrchReport.do?cn=TRKO200500019526.
  2. D. Constantinescu and E. A. Croft, "Smooth and time-optimal trajectory planning for industrial manipulators along specified paths," Journal of Robotic Systems, vol. 17, no. 5, pp 233-249, 2000, DOI: 10.1115/imece1999-0065.
  3. J. E. Bobrow, S. Dubowsky, and J. S. Gibson, "Time-optimal control of robotic manipulators along specified paths," The International Journal of Robotics Research, vol. 4, no. 3, pp. 3-17, 1985, DOI: 10.1177/027836498500400301.
  4. A. Y. Lee, G. Jang, and Y, Choi, "Infinitely differentiable and continuous trajectory planning for mobile robot control," 2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), Jeju, Korea, 2013, DOI: 10.1109/urai.2013.6677386.
  5. A. Y. Lee and Y. Choi, "Smooth trajectory planning methods using physical limits," Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, vol. 229, no. 12, pp. 2127-2143, 2015, DOI: 10.1177/0954406214553982.
  6. J. Ichnowski, M. Danielczuk, J. Xu, and V. Satish, "GOMP: Grasp-optimized motion planning for bin picking," 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 2020, DOI: 10.1109/icra40945.2020.9197548.
  7. K. M. Lynch and F. C. Park, "Trajectory Generation," Modern Robotics, 1st ed., Cambridge University Press, 2017, ch. 9, sec. 1, pp. 325-326, DOI: 10.1017/9781316661239.
  8. J. Kim, S.-R. Kim, S.-J. Kim, and D.-H. Kim, "A practical approach for minimum-time trajectory planning for industrial robots," Industrial Robot, vol. 37, no. 1, pp. 51-61, 2010, DOI: 10.1108/01439911011009957.
  9. Neuromeka-Indy7, [Online], https://s3.ap-northeast-2.amazonaws.com/landing.neuromeka.com/upload/Neuromeka_catalogue_generic_ko.pdf, Accessed: March 1, 2022.
  10. B. Stellato, G. Banjac, P. Goulart, A. Bemporad, Bartolomeo, and S. Boyd, "OSQP: An operator splitting solver for quadratic programs," 2018 UKACC 12th International Conference on Control (CONTROL), Sheffield, UK, 2018, DOI: 10.1109/control.2018.8516834.