Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-25T04:55:40.986Z Has data issue: false hasContentIssue false
  • English
  • Français

Energy-Optimal Motion Trajectory of an Omni-Directional Mecanum-Wheeled Robot via Polynomial Functions

Published online by Cambridge University Press:  06 November 2019

Li Xie Open the ORCID record for Li Xie [Opens in a new window] ,
Karl Stol  and
Weiliang Xu
Li Xie*
Affiliation:
Department of Mechanical Engineering, University of Auckland (UoA), Auckland, New Zealand, E-mails: [email protected], [email protected]
Karl Stol
Affiliation:
Department of Mechanical Engineering, University of Auckland (UoA), Auckland, New Zealand, E-mails: [email protected], [email protected]
Weiliang Xu
Affiliation:
Department of Mechanical Engineering, University of Auckland (UoA), Auckland, New Zealand, E-mails: [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

The Mecanum wheel is one of the practical omni-directional wheel designs in industry, especially for heavy-duty tasks in a confined floor. An issue with Mecanum-wheeled robots is inefficient use of energy. In this study, the robotic motion trajectories are optimized to minimize the energy consumption, where a robotic path is expressed in polynomial functions passing through a given set of via points, and a genetic algorithm is used to find the polynomial’s coefficients being decision variables. To attempt a further reduction in the energy consumption, the via points are also taken as decision variables for the optimization. Both simulations and experiments are conducted, and the results show that the optimized trajectories result in a significant reduction in energy consumption, which can be further lowered when the via points become decision variables. It is also found that the higher the order of the polynomials the larger the reduction in the energy consumption.

Keywords

Mecanum wheelOmni-directional robotEnergy optimizationMotion planningTrajectory generation
Type
Articles
Information
Robotica , Volume 38 , Issue 8 , August 2020 , pp. 1400 - 1414
Copyright
© Cambridge University Press 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bischoff, R., Huggenberger, U. and Prassler, E., “KUKA youBot—A Mobile Manipulator for Research and Education,” IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China (IEEE, 2011) pp. 1–4.Google Scholar
Adascalitei, F. and Doroftei, I., “Practical Applications for Mobile Robots based on Mecanum Wheels – a Systematic Survey,” Proceedings of International Conference on Innovations, Recent Trends and Challenges in Mechatronics, Mechanical Engineering and New High-Tech Products Development – MECAHITECH’11, Bucharest, Romania, vol. 3, (2011) pp. 112–123.Google Scholar
Diegel, O., Badve, A., Bright, G., Potgieter, J. and Tlale, S., “Improved Mecanum Wheel Design for Omni-Directional Robots,” Proceedings of Australasian Conference on Robotics and Automation, Auckland, New Zealand (2002) pp. 117–121.Google Scholar
Mei, Y., Lu, Y. H., Hu, Y. C. and Lee, C. G., “Energy-Efficient Motion Planning for Mobile Robots,” IEEE International Conference on Robotics and Automation, Proceedings, New Orleans, LA, USA, vol. 5 (IEEE, 2004) pp. 4344–4349.Google Scholar
Sun, Z. and Reif, J. H., “On finding energy-minimizing paths on terrains,” IEEE Trans. Rob. 21(1), 102–114 (2005).Google Scholar
Liu, S. and Sun, D., “Minimizing energy consumption of wheeled mobile robots via optimal motion planning,” IEEE/ASME Trans. Mechatron. 19(2), 401–411 (2014).10.1109/TMECH.2013.2241777CrossRefGoogle Scholar
Yang, J., Qu, Z., Wang, J. and Conrad, K., “Comparison of optimal solutions to real-time path planning for a mobile vehicle,” IEEE Trans. Syst. Man Cybern. Part A Syst. Humans 40(4), 721–731 (2010).10.1109/TSMCA.2010.2044038CrossRefGoogle Scholar
Duleba, I. and Sasiadek, J. Z., “Nonholonomic motion planning based on Newton algorithm with energy optimization,” IEEE Trans. Control Syst. Technol. 11(3), 355–363 (2003).CrossRefGoogle Scholar
Kim, C. H. and Kim, B. K., “Minimum-energy translational trajectory generation for differential-driven wheeled mobile robots,” J. Intell. Rob. Syst. 49(4), 367–383 (2007).CrossRefGoogle Scholar
Kim, H. and Kim, B. K., “Online minimum-energy trajectory planning and control on a straight-line path for three-wheeled omnidirectional mobile robots,” IEEE Trans. Ind. Electron. 61(9), 4771–4779 (2014).10.1109/TIE.2013.2293706CrossRefGoogle Scholar
Paul, R. P. and Zhang, H., “Robot Motion Trajectory Specification and Generation,” 2nd International Symposium on Robotics Research, Kyoto, Japan (1984).Google Scholar
Shintaku, E., “Minimum energy trajectory for an underwater manipulator and its simple planning method by using a genetic algorithm,” Adv. Rob. 13(2), 115–138 (1998).10.1163/156855399X00171CrossRefGoogle Scholar
Tian, L. and Collins, C., “An effective robot trajectory planning method using a genetic algorithm,” Mechatronics 14(5), 455–470 (2004).CrossRefGoogle Scholar
Yang, L., Luo, Z., Tang, Z. and Lv, W., “Path Planning Algorithm for Mobile Robot Obstacle Avoidance Adopting Bezier Curve Based on Genetic Algorithm,” Control and Decision Conference (CCDC) (2008) pp. 3286–3289.Google Scholar
Xie, L., Henkel, C., Stol, K. and Xu, W., “Power-minimization and energy-reduction autonomous navigation of an omnidirectional Mecanum robot via the dynamic window method local trajectory planning,” Int. J. Adv. Rob. Syst. 15(1), 1729881418754563 (2018).Google Scholar
Xie, L., Herberger, W., Xu, W. and Stol, K. A., “Experimental Validation of Energy Consumption Model for the Four-Wheeled Omnidirectional Mecanum Robots for Energy-Optimal Motion Control,” IEEE 14th International Workshop on Advanced Motion Control (AMC), Auckland, New Zealand (IEEE, 2016) pp. 565–572.10.1109/AMC.2016.7496410CrossRefGoogle Scholar