Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-22T09:42:55.201Z Has data issue: false hasContentIssue false
  • English
  • Français

An Approximated, Control Affine Model for a Strawberry Field Scouting Robot Considering Wheel–Terrain Interaction

Published online by Cambridge University Press:  05 March 2019

Pablo Menendez-Aponte ,
Xiangling Kong  and
Yunjun Xu
Pablo Menendez-Aponte
Affiliation:
Graduate Research Assistant, Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL, 32816, USA. E-mail: olbap323@gmail.com
Xiangling Kong*
Affiliation:
Graduate Research Assistant, Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL, 32816, USA. E-mail: olbap323@gmail.com
Yunjun Xu
Affiliation:
Professor, Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL, 32816, USA. E-mail: yunjun.xu@ucf.edu
*
*Corresponding author. E-mail: xl.kong@knights.ucf.edu
Get access Rights & Permissions [Opens in a new window]

Summary

Recently, autonomous field robots have been investigated as a labor-reducing means to scout through commercial strawberry fields for disease detection or fruit harvesting. To achieve accurate over-bed and cross-bed motions, it is preferred to design the motion controller based on a precise dynamic model. Here, a dynamic model is developed for a custom-designed strawberry field robot considering terramechanic wheel–terrain interaction. Different from existing models, a torus geometry is considered for the wheels. In order to obtain a control affine model, the longitudinal force is curve-fitted using a polynomial function of the slip/skid ratio, while the lateral force is curve-fitted using an exponential function of both the slip/skid ratio and slip angle. An extended Kalman filter (EKF) is then developed to estimate the unknown parameters in the approximated model such that the state variables propagated by such a model can match experimental data. The approximated model and the EKF-based parameter estimation method are then validated in a commercial strawberry farm.

Keywords

Wheel–terrain interaction model (WTIM)Wheeled mobile robot (WMR)Field robot; Extended Kalman filter (EKF)
Type
Articles
Information
Robotica , Volume 37 , Issue 9 , September 2019 , pp. 1545 - 1561
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

Ewing, L., “Strong growth predicted for ground robots designed for agriculture,” In: Mission Critical: Agriculture 2014 by Association for Unmanned Vehicle Systems, vol. 4, issue no. 2 (2014) pp. 13–15.Google Scholar
Dong, F., Heinemann, W. and Kasper, R., “Development of a row guidance system for an autonomous robot for white asparagus harvesting,” Comput. Electron. Agric. 79(2), 216–225 (2011).CrossRefGoogle Scholar
Fentanes, J. P., Gould, I., Duckett, T., Pearson, S. and Cielniak, G., “3D soil compaction mapping through kriging-based exploration with a mobile robot,” arXiv preprint arXiv:1803.08069 (2018).CrossRefGoogle Scholar
Hayashi, S., Shigematsu, K., Yamamoto, S., Kobayashi, K., Kohno, Y., Kamata, J. and Kurita, M., “Evaluation of a strawberry-harvesting robot in a field test,” Biosyst. Eng. 105(2), 160–171 (2010).CrossRefGoogle Scholar
Asefpour, K. V. and Massah, J., “Design, development and performance evaluation of a robot to early detection of nitrogen deficiency in greenhouse cucumber (Cucumis sativus) with machine vision,” Int. J. Agric.: Res. Rev. 2(4), 448–454 (2012).Google Scholar
Sankaran, S., Mishra, A., Ehsani, R. and Davis, C., “A review of advanced techniques for detecting plant diseases,” Comput. Electron. Agric. 72(1), 1–13 (2010).CrossRefGoogle Scholar
Campion, G., Bastin, G. and D’Andréa-Novel, B., “Structural properties and classification of kinematic and dynamic models of wheeled mobile robots,” IEEE Trans. Robot. Autom. 12(1), 47–62 (1996).CrossRefGoogle Scholar
Ton, C., Kan, Z. and Mehta, S. S., “Obstacle avoidance control of a human-in-the-loop mobile robot system using harmonic potential fields,” Robotica 36(4), 463–483 (2018).CrossRefGoogle Scholar
Caracciolo, L., Luca, A. D. and Iannitti, S., “Trajectory tracking control of a four-wheel differentially driven mobile robot,” In: Proceedings of IEEE International Conference on Robotics and Automation (1999) pp. 2632–2638.CrossRefGoogle Scholar
Yi, J., Wang, H., Zhang, J., Song, D., Jayasuriya, S. and Liu, J., “Kinematic modeling and analysis of skid-steered mobile robots with applications to low-cost inertial-measurement-unit-based motion estimation,” IEEE Trans. Robot. 25(5), 1087–1097 (2009).Google Scholar
Wu, F., Guan, Z. and Whidden, A., “Strawberry industry overview and outlook,” Unpublished manuscript, Gulf Coast Research and Education Center, University of Florida, Gainesville, Florida. Retrieved from http://www.fred.ifas.ufl.edu/pdf/webinar/Strawberry.pdf (2012).Google Scholar
Bekker, M. G., Theory of Land Locomotion (University of Michigan Press, 1956).Google Scholar
Wong, J. Y., Theory of Ground Vehicles (John Wiley & Sons, Hoboken, New Jersey, 2001).Google Scholar
Jia, Z., Smith, W. and Peng, H., “Terramechanics-based wheel–terrain interaction model and its applications to off-road wheeled mobile robots,” Robotica 30(3), 491–503 (2011).CrossRefGoogle Scholar
Ishigami, G., Miwa, A., Nagatani, K. and Yoshida, K., “Terramechanics-based model for steering maneuver of planetary exploration rovers on loose soil,” J. Field Robot. 24(3), 233–250 (2007).CrossRefGoogle Scholar
Gray, A., Abbena, E. and Salamon, S., Modern Differential Geometry of Curves and Surfaces with Mathematica, 3rd edn. (CRC press, 2006).Google Scholar
Wong, J. Y. and Reece, A. R., “Prediction of rigid wheel performance based on the analysis of soil-wheel stresses part I. performance of driven rigid wheel,” J. Terramech. 4(1), 81–88 (1967).CrossRefGoogle Scholar
Tran, T. H., Kwok, N. M., Scheding, S. and Ha, Q. P., “Dynamic modeling of wheel-terrain interaction of a UGV,” In: Proceedings of the 3rd IEEE International Conference on Automation Science and Engineering (2007) pp. 369–374.Google Scholar
Ward, C. C. and Iagnemma, K., “A dynamic-model-based wheel slip detector for mobile robots on outdoor terrain,” IEEE Trans. Robot. 24(4), 821–831 (2008).CrossRefGoogle Scholar
Onafeko, O. and Reece, A. R., “Soil stresses and deformations beneath rigid wheels,” J. Terramech. 4(1), 59–80 (1967).CrossRefGoogle Scholar
Yi, J., Song, D., Zhang, J. and Goodwin, Z., “Adaptive trajectory tracking control of skid-steered mobile robots,” In: Proceedings of IEEE International Conference on Robotics and Automation (2007) pp. 2605–2610.CrossRefGoogle Scholar
Simon, D., Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches (John Wiley & Sons, Inc., Hoboken, New Jersey, 2006).CrossRefGoogle Scholar
Freese, D. and Xu, Y., “Nonlinear robust path control for a field robot scouting in strawberry orchards,” In: Proceedings of the ASME Dynamic Systems and Controls Conference (2017) pp. V002T21A006.Google Scholar
Willmott, C. J. and Matsuura, K., “Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance,” Climate Research 30(1), 79–82 (2005).CrossRefGoogle Scholar
Gujarati, D. N. and Porter, D. C., Basic Econometrics, 5th edn. (The McGraw–Hill, New York, United States, 2008) pp. 73–78.Google Scholar