Skip to main content

An Experimental Setup to Test Time-Jerk Optimal Trajectories for Robotic Manipulators

  • Conference paper
  • First Online:
Advances in Service and Industrial Robotics (RAAD 2023)

Part of the book series: Mechanisms and Machine Science ((Mechan. Machine Science,volume 135))

Included in the following conference series:

Abstract

An experimental setup to test optimal time-jerk trajectories for robotic manipulators is presented in this paper. The setup is used, in this work, to test the execution of smooth motion profiles passing through a sequence of via-points, designed by means of the optimization of a mixed time-jerk cost function. Experimental tests are performed on a Franka Emika robot with seven degrees of freedom equipped with accelerometers to measure the motion-induced oscillations of the end-effector. The experimental results show a good agreement with the numerical tests and demonstrate the feasibility of the approach chosen for optimizing smooth trajectories for robotic manipulators.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 299.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Carabin, G., Scalera, L.: On the trajectory planning for energy efficiency in industrial robotic systems. Robotics 9(4), 89 (2020)

    Article  Google Scholar 

  2. Trigatti, G., Boscariol, P., Scalera, L., Pillan, D., Gasparetto, A.: A look-ahead trajectory planning algorithm for spray painting robots with non-spherical wrists. In: Gasparetto, A., Ceccarelli, M. (eds.) MEDER 2018. MMS, vol. 66, pp. 235–242. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-00365-4_28

    Chapter  Google Scholar 

  3. Cook, C.C., Ho, C.Y.: The Application of Spline Functions to Trajectory Generation for Computer-Controlled Manipulators, pp. 101–110. Springer, Cham (1984)

    Google Scholar 

  4. Boscariol, P., Gasparetto, A., Vidoni, R.: Planning continuous-jerk trajectories for industrial manipulators. In: Engineering Systems Design and Analysis, vol. 44861, pp. 127–136 (2012)

    Google Scholar 

  5. Fang, Y., Jie, H., Liu, W., Shao, Q., Qi, J., Peng, Y.: Smooth and time-optimal s-curve trajectory planning for automated robots and machines. Mech. Mach. Theory 137, 127–153 (2019)

    Article  Google Scholar 

  6. Dai, C., Lefebvre, S., Yu, K.M., Geraedts, J.M., Wang, C.C.: Planning jerk-optimized trajectory with discrete time constraints for redundant robots. IEEE Trans. Autom. Sci. Eng. 17(4), 1711–1724 (2020)

    Article  Google Scholar 

  7. Wu, G., Zhang, S.: Real-time jerk-minimization trajectory planning of robotic arm based on polynomial curve optimization. Proc. Inst. Mech. Eng. Part C: J. Mech. Eng. Sci. 236, 10852–10864 (2022)

    Google Scholar 

  8. Abu-Dakka, F.J., Assad, I.F., Alkhdour, R.M., Abderahim, M.: Statistical evaluation of an evolutionary algorithm for minimum time trajectory planning problem for industrial robots. Int. J. Adv. Manuf. Technol. 89(1), 389–406 (2017)

    Article  Google Scholar 

  9. Palleschi, A., Garabini, M., Caporale, D., Pallottino, L.: Time-optimal path tracking for jerk controlled robots. IEEE Rob. Autom. Lett. 4(4), 3932–3939 (2019)

    Article  Google Scholar 

  10. Gasparetto, A., Zanotto, V.: A technique for time-jerk optimal planning of robot trajectories. Rob. Compt.-Int. Manuf. 24(3), 415–426 (2008)

    Article  Google Scholar 

  11. Zanotto, V., Gasparetto, A., Lanzutti, A., Boscariol, P., Vidoni, R.: Experimental validation of minimum time-jerk algorithms for industrial robots. J. Intell. Rob. Syst. 64(2), 197–219 (2011)

    Article  Google Scholar 

  12. Huang, J., Pengfei, H., Kaiyuan, W., Zeng, M.: Optimal time-jerk trajectory planning for industrial robots. Mech. Mach. Theory 121, 530–544 (2018)

    Article  Google Scholar 

  13. Scalera, L., Giusti, A., Vidoni, R., Gasparetto, A.: Enhancing fluency and productivity in human-robot collaboration through online scaling of dynamic safety zones. Int. J. Adv. Manuf. Technol. 121(9), 6783–6798 (2022)

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by the Laboratory for Big Data, IoT, Cyber Security (LABIC) funded by Friuli Venezia Giulia, and the Laboratory for Artificial Intelligence for Human-Robot Collaboration (AI4HRC) funded by Fondazione Friuli. We thank LAMA FVG for the fabrication of the aluminium flange.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alessandro Gasparetto .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lozer, F., Scalera, L., Boscariol, P., Gasparetto, A. (2023). An Experimental Setup to Test Time-Jerk Optimal Trajectories for Robotic Manipulators. In: Petrič, T., Ude, A., Žlajpah, L. (eds) Advances in Service and Industrial Robotics. RAAD 2023. Mechanisms and Machine Science, vol 135. Springer, Cham. https://doi.org/10.1007/978-3-031-32606-6_36

Download citation

Publish with us

Policies and ethics