Japan’s rapidly aging population and shrinking workforce are creating serious challenges, especially in jobs that require long hours outdoors. To solve this, Japan urgently needs innovative solutions, including automation technologies for outdoor work such as farming, construction, and maintenance. One promising approach is the application of model predictive control (MPC) to outdoor mobile robots. Although MPC has been widely studied in the context of mobile robotics, there remains a paucity of practical research specifically targeting cleaning robots operating in outdoor environments. Alternative approaches, such as geometric path-following methods like pure pursuit, are frequently employed in simpler applications, but often encounter limitations in achieving high-precision trajectory tracking. This study proposes a model predictive contouring control (MPCC) framework for trajectory tracking in outdoor cleaning robots. The proposed method offers the capability to balance the trade-off between execution time and trajectory accuracy. Both simulation and experimental results validate the effectiveness of the proposed MPCC approach.

1. [1] M. Farooq, A. Eizad, and H.-K. Bae, “Power solutions for autonomous mobile robots: A survey,” Robot. and Auton. Syst., Vol.159, Article No.104285, 2022. https://doi.org/10.1016/j.robot.2022.104285
2. [2] “Outdoor Cleaning Robot Hakuro-kun.” https://www.robotatta.com/products/502 [Accessed December 27, 2024]
3. [3] M. Schwenzer, M. Ay, T. Bergs, and D. Abel, “Review on model predictive control: An engineering perspective,” Int. J. Adv. Manuf. Technol., Vol.117, No.5, pp. 1327-1349, 2021. https://doi.org/10.1007/s00170-021-07682-3
4. [4] P. Krupa, et al., “Model predictive control for tracking using artificial references: Fundamentals, recent results and practical implementation,” arXiv:2406.06157, 2024. https://doi.org/10.48550/arXiv.2406.06157
5. [5] T. P. Nascimento, C. E. Dórea, and L. M. G. Gonçalves, “Nonholonomic mobile robots’ trajectory tracking model predictive control: A survey,” Robotica, Vol.36, No.5, pp. 676-696, 2018. https://doi.org/10.1017/S0263574717000637
6. [6] G. Bai, et al., “Path tracking of wheeled mobile robots based on dynamic prediction model,” IEEE Access, Vol.7, pp. 39690-39701, 2019. https://doi.org/10.1109/ACCESS.2019.2903934
7. [7] H. Lim, Y. Kang, C. Kim, J. Kim, and B.-J. You, “Nonlinear model predictive controller design with obstacle avoidance for a mobile robot,” in Proc. IEEE/ASME Int. Conf. Mechatronic Embed. Sys. Appl. (MESA), pp. 494-499, 2008. https://doi.org/10.1109/MESA.2008.4735699
8. [8] R. Findeisen and F. Allgöwer, “An introduction to nonlinear model predictive control,” Proc. 21st Benelux Meet. on Syst. Control, Vol.11, pp. 119-141, 2002.
9. [9] F. Imamura and H. Kaufman, “Time optimal contour tracking for machine tool controllers,” IEEE Control Syst. Mag., Vol.11, No.3, pp. 11-17, 1991. https://doi.org/10.1109/37.75573
10. [10] D. Lam, C. Manzie, and M. Good, “Model predictive contouring control,” Proc. 49th IEEE Conf. Decis. Control (CDC), pp. 6137-6142, 2010. https://doi.org/10.1109/CDC.2010.5717042
11. [11] A. Liniger, A. Domahidi, and M. Morari, “Optimization-based autonomous racing of 1:43 scale RC cars,” Optim. Control Appl. Methods, Vol.36, No.5, pp. 628-647, 2015. https://doi.org/10.1002/oca.2123
12. [12] “Ichimill GPS.” https://www.softbank.jp/biz/services/analytics/ichimill/ [Accessed December 27, 2024]
13. [13] “Robot Operating System.” https://ros.org/ [Accessed August 4, 2025]
14. [14] E. Bakker, L. Nyborg, and H. Pacejka, “Tire modelling for use in vehicle dynamics studies,” SAE Technical Paper, No.870421, 1987.
15. [15] K. Erkorkmaz and Y. Altintas, “Quintic spline interpolation with minimal feed fluctuation,” J. Manuf. Sci. Eng., Vol.127, No.2, pp. 339-349, 2005. https://doi.org/10.1115/1.1830493
16. [16] “CasADI.” https://web.casadi.org/docs/ [Accessed August 4, 2025]
17. [17] M. Samuel, M. Hussein, and M. B. Mohamad, “A review of some pure pursuit-based path tracking techniques for control of autonomous vehicles,” Int. J. Comput. Appl., Vol.135, No.1, pp. 35-38, 2016. https://doi.org/10.5120/ijca2016908314
18. [18] R. C. Coulter, “Implementation of the pure pursuit path tracking algorithm,” Carnegie Mellon University, Techical Report, No.CMU-RI-TR-92-01, 1992.
19. [19] J. Giesbrecht, D. Mackay, J. Collier, and S. Verret, “Path tracking for unmanned ground vehicle navigation,” DRDC Suffield, Technical Memo, Accession No.ADA599492 2005.