Abstract
This article reports a simulation study conducted using a three-dimensional tractor-driving simulator to develop an implement control algorithm and evaluate the tillage coverage and field efficiencies of a virtual autonomous tractor following paths generated based on different headland turning methods. To minimize the no-tilled or unnecessarily tilled areas that occurred in our previous study, a tillage implement control algorithm was designed by enabling the raising or lowering of a three-point hitch with appropriate delay times. The effects of headland turning methods, i.e., X-shaped, R-shaped, and C-shaped turns, on path tracking and full-path simulation of autonomous tillage operations were studied. The results of the simulation studies were evaluated in terms of tracking error, skipped area, and field efficiency. The developed implement control algorithm effectively reduced both no-tilled and unnecessarily tilled areas in comparison with those obtained without the implement control algorithm. The magnitudes of changes in no-tilled and unnecessarily tilled areas were from 4.5 to 0.4 m2 and from 4.6 to 0.3 m2, respectively. In a study in which an autonomous tractor followed a desired path in a virtual field of 100 m × 40 m at a constant traveling speed of 4 km h−1 designed to investigate the influence of the three different headland turning methods, the simulator allowed a quantifiable comparison of tillage operation performance for the various headland turns by showing lateral deviations < 9 cm, heading angle errors < 16°, ratios of skipped area < 2%, and field efficiencies ranging from 81.3 to 86.6%. Full-path simulation tests of the autonomous tillage operation conducted in virtual rectangular fields with a length of 100 m and various widths showed that field efficiency was inversely proportional to the field width. The use of the 3D tractor-driving simulator was effective for designing tillage implement control algorithms and studying the effects of headland turning methods on path tracking. These results are applicable to the development of path generation and tracking algorithms suitable for autonomous tillage operations.
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References
Bochtis, D., Sørensen, C., & Vougioukas, S. (2010). Path planning for in-field navigation-aiding of service units. Computers and Electronics in Agriculture, 74(1), 80–90. https://doi.org/10.1016/j.compag.2010.06.008.
Dong, J., & He, B. (2019). Novel fuzzy PID-type iterative learning control for quadrotor UAV. Sensors, 19(1), 24. https://doi.org/10.3390/s19010024.
Edan, Y., Han, S., & Kondo, N. (2009). Automation in agriculture. In S. Y. Nof (Ed.), Springer handbook of automation (pp. 1095–1128). Berlin: Springer.
Giachetti, R. E., Marcelli, V., Cifuentes, J., & Rojas, J. A. (2013). An agent-based simulation model of human-robot team performance in military environments. Systems Engineering, 16(1), 15–28. https://doi.org/10.1002/sys.21216.
Han, X.-Z., Kim, H.-J., Moon, H.-C., Woo, H.-J., Kim, J.-H., & Kim, Y.-J. (2013). Development of a path generation and tracking algorithm for a Korean auto-guidance tillage tractor. Journal of Biosystems Engineering, 38(1), 1–8. https://doi.org/10.5307/JBE.2013.38.1.001.
Han, X., Kim, H.-J., Jeon, C. W., Moon, H. C., Kim, J. H., & Yi, S. Y. (2019). Application of a 3D tractor-driving simulator for slip estimation-based path-tracking control of auto-guided tillage operation. Biosystems Engineering, 178, 70–85. https://doi.org/10.1016/j.biosystemseng.2018.11.003.
Han, X. Z., Kim, H. J., Kim, J. Y., Yi, S. Y., Moon, H. C., Kim, J. H., et al. (2015). Path-tracking simulation and field tests for an auto-guidance tillage tractor for a paddy field. Computers and Electronics in Agriculture, 112, 161–171. https://doi.org/10.1016/j.compag.2014.12.025.
Hill, R. R., Miller, J. O., & McIntyre, G. A. (2001). Simulation analysis: applications of discrete event simulation modeling to military problems. Proceedings of the 33nd conference on Winter simulation (pp. 780-788). Arlington, IEEE Computer Society. https://doi.org/10.1109/WSC.2001.977367.
Huang, P., Luo, X., & Zhang, Z. (2009). Headland turning control method simulation of autonomous agricultural machine based on improved pure pursuit model. In D. Li, & C. Zhao (Eds), International Conference on Computer and Computing Technologies in Agriculture (pp. 176-184). Berlin: Springer. https://doi.org/10.1007/978-3-642-12220-0_27.
Jung, D., & Tsiotras, P. (2007). Modeling and hardware-in-the-loop simulation for a small unmanned aerial vehicle. AIAA Infotech@ Aerospace 2007 Conference and Exhibit (pp. 2768-2780). Reston, American Institute of Aeronautics and Astronautics. https://doi.org/10.2514/6.2007-2768.
Karimi, D., & Mann, D. D. (2006). A study of tractor yaw dynamics for application in a tractor driving simulator. ASABE/CSBE North Central Intersectional Meeting (pp. 1-11). St. Joseph, American Society of Agricultural and Biological Engineers. https://doi.org/10.13031/2013.22362.
Karimi, D., & Mann, D. (2008). Role of motion cues in straight-line driving of an agricultural vehicle. Biosystems Engineering, 101(3), 283–292. https://doi.org/10.1016/j.biosystemseng.2008.09.006.
Kayacan, E., Kayacan, E., Ramon, H., & Saeys, W. (2014). Distributed nonlinear model predictive control of an autonomous tractor–trailer system. Mechatronics, 24(8), 926–933. https://doi.org/10.1016/j.mechatronics.2014.03.007.
Kim, H. K. (1995). Integrated rice farming mechanization system for large sized plots. PhD dissertation. Seoul. Rep. Korea: Seoul National University.
Kim, Y.-Y., Kim, B., Shin, S.-y., Kim, J., & Yum, S. (2014). Development of driving simulator for safety training of agricultural tractor operators. Journal of Biosystems Engineering, 39(4), 389–399. https://doi.org/10.5307/JBE.2014.39.4.389.
Lei, F., & Yong, H. (2005). Study on dynamic model of tractor system for automated navigation applications. Journal of Zhejiang University-SCIENCE A, 6(4), 270–275. https://doi.org/10.1631/jzus.2005.A0270.
Letherwood, M. D., & Gunter, D. D. (2001). Ground vehicle modeling and simulation of military vehicles using high performance computing. Parallel Computing, 27(1-2), 109–140. https://doi.org/10.1016/S0167-8191(00)00091-0.
Linz, A., Brunner, D., Fehrmann, J., Herlitzius, T., Keicher, R., Ruckelshausen, A., & Schwarz, H. P. (2017). Modelling environment for an electrical driven selective sprayer robot in orchards. Advances in Animal Biosciences, 8(2), 848–853. https://doi.org/10.1017/S2040470017000723.
Park, G. H., Chang, Y. S., Ryu, J. H., Jeong, S. G., Song, H. S., Park, S. H., Lee, C. H., Hong, S. P., & Lee, M. H. (2003). Estimation of vehicle cornering stiffness via GPS/INS. International Conference on Control, Automation and Systems (pp.1706-1709). Gyeongju, Institute of Control, Robotics and Systems.
Qian, W., Xia, Z., Xiong, J., Gan, Y., Guo, Y., Weng, S., Deng, H., Hu, Y., Zhang, J. (2014). Manipulation task simulation using ROS and Gazebo. 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014) (pp. 2594-2598). New York, IEEE. https://doi.org/10.1109/ROBIO.2014.7090732.
Shimizu, M., & Takahashi, T. (2013). Training platform for rescue robot operation and pair operations of multi-robots. Advanced Robotics, 27(5), 385–391. https://doi.org/10.1080/01691864.2013.763744.
Wang, B., Li, S., Guo, J., & Chen, Q. (2018). Car-like mobile robot path planning in rough terrain using multi-objective particle swarm optimization algorithm. Neurocomputing, 282, 42–51. https://doi.org/10.1016/j.neucom.2017.12.015.
Yung, I., Vázquez, C., & Freidovich, L. (2015). Automation of front end loaders: Self leveling task. IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA) (pp. 1-7). New York, IEEE. https://doi.org/10.1109/ETFA.2015.7301451.
Zhang, M., Ma, W., Liu, Z., & Liu, G. (2013). Fuzzy-adaptive control method for off-road vehicle guidance system. Mathematical and Computer Modelling, 58(3-4), 551–555. https://doi.org/10.1016/j.mcm.2011.10.066.
Zhang, Q., & Qiu, H. (2004). A dynamic path search algorithm for tractor automatic navigation. Transactions of ASAE, 47(2), 639–646. https://doi.org/10.13031/2013.16027.
Žlajpah, L. (2008). Simulation in robotics. Mathematics and Computers in Simulation, 79(4), 879–897. https://doi.org/10.1016/j.matcom.2008.02.017.
Funding
This research was supported in part by the Cooperative Research Programs of the Ministry of Trade, Industry and Energy (No. 10049017, 2014–2019) and the Agriculture, Forestry and Livestock Programs (No. 2018300866, 2018–2019), Republic of Korea.
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Han, X., Kim, HJ., Jeon, C.W. et al. Simulation Study to Develop Implement Control and Headland Turning Algorithms for Autonomous Tillage Operations. J. Biosyst. Eng. 44, 245–257 (2019). https://doi.org/10.1007/s42853-019-00035-9
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DOI: https://doi.org/10.1007/s42853-019-00035-9