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Research on Navigation Line Extraction of Garden Mobile Robot Based on Edge Detection

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Abstract

The autonomous navigation of garden mobile robot plays an important role in garden monitoring and maintenance, in which the extraction of navigation line is the key technology. Due to the complexity of garden environment, the current algorithms of extracting navigation lines are easily affected by interference, relying on image preprocessing. In order to solve these problems, this paper proposed a cascade anti-interference algorithm based on edge detection. Firstly, the improved vegetation index combined with Otsu method is used to segment the road and background. Then, an edge detection algorithm is proposed to overcome the noise interference and get the road edge points. Lastly, the cascade anti-interference algorithm eliminates the interference points and extracts navigation lines. Experimental results show that the average processing time of the proposed algorithm is 36.25 ms, and the error is less than 0.8°, which is better than the traditional least square method and Hough transform method.

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References

  1. Jim, C.Y.: Managing urban trees and their soil envelopes in a contiguously developed City environment. Environ. Manag. 28, 819–832 (2001). https://doi.org/10.1007/s002670010264

    Article  Google Scholar 

  2. Lee, H.-Y., Tseng, H.-H., Zheng, M.-C., Li, P.-Y.: Decision support for the maintenance management of green areas. Expert Syst. Appl. 37, 4479–4487 (2010). https://doi.org/10.1016/j.eswa.2009.12.063

    Article  Google Scholar 

  3. Putra, B.T.W.: New low-cost portable sensing system integrated with on-the-go fertilizer application system for plantation crops. Measurement. 155, 107562 (2020). https://doi.org/10.1016/j.measurement.2020.107562

    Article  Google Scholar 

  4. Ji, C., Zhou, J.: Current situation of navigation technologies for agricultural machinery. Nongye Jixie Xuebao/Trans. Chin. Soc. Agric. Mach. 45, 44–54 (2014). https://doi.org/10.6041/j.issn.1000-1298.2014.09.008

    Article  Google Scholar 

  5. Karunanithy, K., Bhanumathi, V.: Energy efficient cluster and travelling salesman problem based data collection using WSNs for intelligent water irrigation and fertigation. Measurement. 161, 107835 (2020). https://doi.org/10.1016/j.measurement.2020.107835

    Article  Google Scholar 

  6. Li, Y., Wang, X., Liu, D.: 3D autonomous navigation line extraction for field roads based on binocular vision. J. Sensors. 2019, 6832109 (2019). https://doi.org/10.1155/2019/6832109

    Article  Google Scholar 

  7. Gong, J., Wang, X., Zhang, Y., Lan, Y., Mostafa, K.: Navigation line extraction based on root and stalk composite locating points. Comput. Electr. Eng. 92, 107115 (2021). https://doi.org/10.1016/j.compeleceng.2021.107115

    Article  Google Scholar 

  8. Chen, Z., Niu, R., Wang, J., Zhu, H., Yu, B.: Navigation Line Extraction Method for Ramie Combine Harvester Based on U-Net, 2021 6th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS), pp. 1–7 (2021). https://doi.org/10.1109/ACIRS52449.2021.9519315

  9. Lu, W., Zeng, M., Wang, L., Luo, H., Mukherjee, S., Huang, X., Deng, Y.: Navigation Algorithm Based on the Boundary Line of New and Old Soil Combined Using Guided Filtering and Improved Anti-noise Morphology. Sensors. 19, 3918 (2019). https://doi.org/10.20944/preprints201908.0282.v1

    Article  Google Scholar 

  10. Li, Y., Ding, W., Zhang, X., Ju, Z.: Road detection algorithm for autonomous navigation systems based on dark channel prior and vanishing point in complex road scenes. Robot. Auton. Syst. 85, 1–11 (2016). https://doi.org/10.1016/j.robot.2016.08.003

    Article  Google Scholar 

  11. Lihui, W., Yu, Y., Jiachen, S.: Measurement of harvesting width of intelligent combine harvester by improved probabilistic Hough transform algorithm. Measurement. 151, 107130 (2019). https://doi.org/10.1016/j.measurement.2019.107130

    Article  Google Scholar 

  12. Chen, J., Qiang, H., Wu, J., Xu, G., Wang, Z., Liu, X.: Extracting the navigation path of atomato-cucumbergreenhouse robot based on a median point Hough transform. Comput. Electron. Agric. 174, 105472 (2020). https://doi.org/10.1016/j.compag.2020.105472

    Article  Google Scholar 

  13. Chen, J., Qiang, H., Wu, J., Xu, G., Wang, Z.: Navigation path extraction for greenhouse cucumber-picking robots using the prediction-point Hough transform. Comput. Electron. Agric. 180, 105911 (2021). https://doi.org/10.1016/j.compag.2020.105911

    Article  Google Scholar 

  14. Zhang, Q., Chen, M.E., Li, B.: A visual navigation algorithm for paddy field weeding robot based on image understanding. Comput. Electron. Agric. 143, 66–78 (2017). https://doi.org/10.1016/j.compag.2017.09.008

    Article  Google Scholar 

  15. Li-Si, F., Yong-You, X.: The Visual Extraction of the Autonomous Lawnmower Navigation Route, International Conference on Mechatronics & Applied Mechanics (2011). https://doi.org/10.4028/www.scientific.net/AMM.157-158.1458

  16. Asif, M., Hussain, S., Israr, A., Shaikh, M.F.: A Vision System for Autonomous Weed Detection Robot. Int. J. Comput. Electr. Eng. 2(3), 486 (2010). https://doi.org/10.7763/IJCEE.2010.V2.182

    Article  Google Scholar 

  17. Ji, R., Qi, L.: Crop-row detection algorithm based on random Hough transformation. Math. Comput. Model. 54, 1016–1020 (2011). https://doi.org/10.1016/j.mcm.2010.11.030

    Article  Google Scholar 

  18. Montalvo, M., Pajares, G., Guerrero, J.M., Romeo, J., Guijarro, M., Ribeiro, A., Ruz, J.J., Cruz, J.M.: Automatic detection of crop rows in maize fields with high weeds pressure. Expert Syst. Appl. 39, 11889–11897 (2012). https://doi.org/10.1016/j.eswa.2012.02.117

    Article  Google Scholar 

  19. Li, M., Zhang, M., Huan, H., Liu, G.: A new navigation line extraction method for agriculture implements guidance system. In: Li, D., Chen, Y. (eds.) Computer and Computing Technologies in Agriculture VI, pp. 299–308. Springer, Berlin (2013). https://doi.org/10.1007/978-3-642-36137-1_36

    Chapter  Google Scholar 

  20. Zhang, X., Li, X., Zhang, B., Zhou, J., Tian, G., Xiong, Y., Gu, B.: Automated robust crop-row detection in maize fields based on position clustering algorithm and shortest path method. Comput. Electron. Agric. 154, 165–175 (2018). https://doi.org/10.1016/j.compag.2018.09.014

    Article  Google Scholar 

  21. Xiang, H., Tian, L.J.C.: An automated stand-alone in-field remote sensing system (SIRSS) for in-season crop monitoring. Comput. Electron. Agric. 78, 1–8 (2011). https://doi.org/10.1016/j.compag.2011.04.006

    Article  Google Scholar 

  22. Ribeiro, A., Fernandez-Quintanilla, C., Barroso, J., García-Alegre, M.: Development of an image analysis system for estimation of weed pressure, precision agriculture 2005. ECPA. 2005, 169–174 (2005)

    Google Scholar 

  23. Suriyakoon, S., Ruangpayoongsak, N.: Leading point based interrow robot guidance in corn fields, International Conference on Control & Robotics Engineering, pp. 8–12 (2017). https://doi.org/10.1109/ICCRE.2017.7935032

  24. Camargo Neto, J.: A Combined Statistical-Soft Computing Approach for Classification and Mapping Weed Species in Minimum -Tillage Systems (2004)

  25. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9, 62–66 (1979). https://doi.org/10.1109/TSMC.1979.4310076

    Article  Google Scholar 

  26. Dong, J., Lv, H.: A new method of extraction of Mobile robot navigation line based on edge detection. Adv. Mater. Res. 143-144, 482–486 (2010). https://doi.org/10.4028/www.scientific.net/AMR.143-144.482

    Article  Google Scholar 

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Availability of Data and Materials

The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.

Funding

The work was supported by National Natural Science Foundation of China (grant number 62163005, 51765005), Guangxi Natural Science Foundation (grant number 2022GXNSFAA035633), Guangxi Major scientific and technological project (grant number AA19254021).

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Authors and Affiliations

Authors

Contributions

Jiqing Chen: Conceptualization, Resources, Writing - Review & Editing, Project administration, Funding acquisition, Supervision.

Zhikui Wang: Conceptualization, Resources, Formal analysis, Writing - Original Draft, Methodology, Software.

Teng Long: Writing - Review & Editing, Formal analysis, Validation, Software, Resources.

Jiahua Wu: Writing -Review & Editing Formal analysis, Software, Resources.

Ganwei Cai: Supervision, Resources.

Hongdu Zhang: Formal analysis.

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Chen, J., Wang, Z., Long, T. et al. Research on Navigation Line Extraction of Garden Mobile Robot Based on Edge Detection. J Intell Robot Syst 105, 27 (2022). https://doi.org/10.1007/s10846-022-01648-7

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  • DOI: https://doi.org/10.1007/s10846-022-01648-7

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