https://doi.org/10.4081/jae.2023.1539
An efficient headland-turning navigation system for a safflower picking robot
Published: 11 October 2023
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
This study proposes a navigation system for the headland autonomous turning of a safflower picking robot. The proposed system includes binocular cameras, differential satellites, and inertial sensors. The method of extracting the headland boundary line combining the hue, saturation, and value-fixed threshold segmentation method and random sample consensus algorithm and planning the headland-turning trajectory of a robot based on the multiorder Bezier curve are used as control methods. In addition, a headland-turning tracking model of a safflower picking robot is designed, and a path-tracking control algorithm is developed. A field test verifies the performance of the designed headland-turning navigation system. The test results show that the accuracy of the judgment result regarding the existence of a headland is higher than 96%. In headland boundary detection, the angle deviation is less than 1.5˚, and the depth value error is less than 50 mm. The headland-turning path tracking test result shows that at a turning speed of 0.5 km/h, the average lateral deviation is 37 mm, and the turning time is 24.2 seconds. Compared to the 1 km/h, the turning speed of 0.5 km/h provides a better trajectory tracking effect, but the turning time is longer. The test results verify that this navigation system can accurately extract the headland boundary line and can successfully realise the headland-turning path tracking of a safflower picking robot. The results presented in this study can provide a useful reference for the autonomous navigation of a field robot.
Abdelhakim G., Abdelouahab H. 2019. A new approach for controlling a trajectory tracking using intelligent methods. J. Electr. Eng. Technol. 14:1347-56.
DOI: https://doi.org/10.1007/s42835-019-00112-1
Backman J., Piirainen P., Oksanen T. 2015. Smooth turning path generation for agricultural vehicles in headlands. Biosyst. Eng. 139:76-86.
DOI: https://doi.org/10.1016/j.biosystemseng.2015.08.005
Bulgakov V., Pascuzzi S., Ivanovs S., Kuvachov V., Postol Y., Santoro F., Melnyk V. 2021. Study of the steering of a wide span vehicle controlled by a local positioning system. J. Agric. Eng. 52.
DOI: https://doi.org/10.4081/jae.2021.1144
Duraklı Z., Nabiyev V. 2022. A new approach based on Bezier curves to solve path planning problems for mobile robots. J. Comput. Sci-Neth. 58:101540.
DOI: https://doi.org/10.1016/j.jocs.2021.101540
Ericson S.K., Astrand B.S. 2018. Analysis of two visual odometry systems for use in an agricultural field environment. Biosyst. Eng. 166:116-25.
DOI: https://doi.org/10.1016/j.biosystemseng.2017.11.009
Fu L., Majeed Y., Zhang X., Karkee M., Zhang Q. 2020. Faster R-CNN-based apple detection in dense-foliage fruiting-wall trees using RGB and depth features for robotic harvesting. Biosyst. Eng. 197:245-56.
DOI: https://doi.org/10.1016/j.biosystemseng.2020.07.007
Gai J.Y., Xiang L.R., Tang L. 2021. Using a depth camera for crop row detection and mapping for under-canopy navigation of agricultural robotic vehicle. Comput. Electron. Agr. 188:106301.
DOI: https://doi.org/10.1016/j.compag.2021.106301
Garcia-Martinez J.R., Cruz-Miguel E.E., Carrillo-Serrano R.V., Mendoza-Mondragon F., Toledano-Ayala M., Rodriguez-Resendiz J. 2020. A PID-type fuzzy logic controller-based approach for motion control applications. Sensors 20:5323.
DOI: https://doi.org/10.3390/s20185323
Guevara C.L., Michalek M.M., Auat Cheein F. 2020. Headland turning algorithmization for autonomous N-trailer vehicles in agricultural scenarios. Comput. Electron. Agr. 175:105541.
DOI: https://doi.org/10.1016/j.compag.2020.105541
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. Biosyst. Eng. 178:70-85.
DOI: https://doi.org/10.1016/j.biosystemseng.2018.11.003
He Y., Zhang X.Y., Zhang Z.Q., Fang H. 2022. Automated detection of boundary line in paddy field using MobileV2-UNet and RANSAC. Comput. Electron. Agr. 194:106697.
DOI: https://doi.org/10.1016/j.compag.2022.106697
Heiß A., Paraforos D., Griepentrog H. 2019. Determination of cultivated area, field boundary and overlapping for a plowing operation using ISO 11783 communication and D-GNSS position data. Agriculture 9:38.
DOI: https://doi.org/10.3390/agriculture9020038
Huan P., Zhang Z., Luo X. 2020. Feedforward-plus-proportional-integral-derivative controller for agricultural robot turning in headland. Int. J. Adv. Robot. Syst. 17.
DOI: https://doi.org/10.1177/1729881419897678
Huang P., Zhu L., Zhang Z., Yang C. 2021. Row end detection and headland turning control for an autonomous banana-picking robot. Machines 9:103.
DOI: https://doi.org/10.3390/machines9050103
Jeon C., Kim H.J., Yun C., Han X., Kim J.H. 2021. Design and validation testing of a complete paddy field-coverage path planner for a fully autonomous tillage tractor. Biosyst. Eng. 208:79-97.
DOI: https://doi.org/10.1016/j.biosystemseng.2021.05.008
Jing Y., Liu G., Luo C. 2021. Path tracking control with slip compensation of a global navigation satellite system based tractor- scraper land levelling system. Biosyst. Eng. 212:360-77.
DOI: https://doi.org/10.1016/j.biosystemseng.2021.11.010
Li H., Luo Y., Wu J. 2019. Collision-free path planning for intelligent vehicles based on Bézier curve. Ieee Access. 7:123334-40.
DOI: https://doi.org/10.1109/ACCESS.2019.2938179
Luo Y., Wei L., Xu L., Zhang Q., Liu J., Cai Q., Zhang W. 2022. Stereo-vision-based multi-crop harvesting edge detection for precise automatic steering of combine harvester. Biosyst. Eng. 215:115-28.
DOI: https://doi.org/10.1016/j.biosystemseng.2021.12.016
Mani V., Lee S.K., Yeo Y., Hahn B.S. 2020. A metabolic perspective and opportunities in pharmacologically important safflower. Metabolites 10:253-70.
DOI: https://doi.org/10.3390/metabo10060253
Mao W., Liu H., Hao W., Yang F., Liu Z. 2022. Development of a combined orchard harvesting robot navigation system. Remote Sens. 14:675.
DOI: https://doi.org/10.3390/rs14030675
Oyedeji A.N., Ali Umar U., Shettima Kuburi L., Edet A.A., Mukhtar Y. 2022. Development and performance evaluation of an oil palm harvesting robot for the elimination of ergonomic risks associated with oil palm harvesting. J. Agric. Eng. 53.
DOI: https://doi.org/10.4081/jae.2022.1388
Ravankar A., Ravankar A.A., Kobayashi Y., Hoshino Y., Peng C.C. 2018. Path smoothing techniques in robot navigation: state-of-the-art, current and future challenges. Sensors 18:3170.
DOI: https://doi.org/10.3390/s18093170
Sabelhaus D., Röben F., Meyer zu Helligen L.P., Schulze Lammers P. 2013. Using continuous-curvature paths to generate feasible headland turn manoeuvres. Biosyst. Eng. 116:399-409.
DOI: https://doi.org/10.1016/j.biosystemseng.2013.08.012
Sain D., Mohan B.M. 2021. Modeling, simulation and experimental realization of a new nonlinear fuzzy PID controller using center of gravity defuzzification. ISA Trans. 110:319-27.
DOI: https://doi.org/10.1016/j.isatra.2020.10.048
Shalal N., Low T., McCarthy C., Hancock N. 2015. Orchard mapping and mobile robot localisation using on-board camera and laser scanner data fusion - part B: mapping and localisation. Comput. Electron. Agr. 119:267-78.
DOI: https://doi.org/10.1016/j.compag.2015.09.026
Tang W., Wang L., Gu J., Gu Y. 2020. Single neural adaptive PID control for small UAV micro-turbojet engine. Sensors 20:345.
DOI: https://doi.org/10.3390/s20020345
Wang Q., Liu H., Yang P., Meng Z. 2020. Detection method of headland boundary line based on machine vision. Trans. Chinese Soc. Agric. Mach. 51:18-27. [Article in Chinese].
Wang H., Noguchi N. 2018. Adaptive turning control for an agricultural robot tractor. Int. J. Agr. and Biol. Eng. 11:113-9.
DOI: https://doi.org/10.25165/j.ijabe.20181106.3605
Ye Y., He L., Wang Z., Jones D., Hollinger G.A., Taylor M.E., Zhang Q. 2018. Orchard manoeuvring strategy for a robotic bin-handling machine. Biosyst. Eng. 169:85-103.
DOI: https://doi.org/10.1016/j.biosystemseng.2017.12.005
Yin X., Du J., Noguchi N., Yang T., Jin C. 2018. Development of autonomous navigation system for rice transplanter. Int. J. Agr. Biol. Eng. 11:89-94.
DOI: https://doi.org/10.25165/j.ijabe.20181106.3023
Yin X., Wang Y., Chen Y., Jin C., Du J. 2020. Development of autonomous navigation controller for agricultural vehicles. Int. J. Agr. Biol. Eng. 13:70-6.
DOI: https://doi.org/10.25165/j.ijabe.20201304.5470
Yun G., Lixin Z., Ying Q., Xiaopan J., Yuanbo C. 2016. Dynamic model for sucking process of pneumatic cutting-type safflower harvest device. Int. J. Agr. Biol. Eng. 9:43-50.
Zhang T., Jiao X., Lin Z. 2022. Finite time trajectory tracking control of autonomous agricultural tractor integrated nonsingular fast terminal sliding mode and disturbance observer. Biosyst. Eng. 219:153-64.
DOI: https://doi.org/10.1016/j.biosystemseng.2022.04.020
Zhou M., Xia J., Yang F., Zheng K., Hu M., Li D., Zhang S. 2021. Design and experiment of visual navigated UGV for orchard based on Hough matrix and RANSAC. Int. J. Agr. Biol. Eng. 14:176-84.
DOI: https://doi.org/10.25165/j.ijabe.20211406.5953
How to Cite
Gao, G. (2023) “An efficient headland-turning navigation system for a safflower picking robot”, Journal of Agricultural Engineering, 54(3). doi: 10.4081/jae.2023.1539.
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