Path planning for agricultural robots in wild livestock farm environments
Keywords:
field-based livestock farming, agricultural robots, path planning, A* algorithm, artificial potential field, Bézier curve segmentationAbstract
Path planning for field agricultural robots must satisfy several criteria: establishing feeding routes, maintaining gentle slopes, approaching multiple livestock observation points, ensuring timely environmental monitoring, and achieving high efficiency. The complex terrain of outdoor farming areas poses a challenge. Traditional A* algorithms, which generate only the shortest path, fail to meet these requirements and often produce paths that lack smoothness. Therefore, identifying the most suitable path, rather than merely the shortest one, is essential. This study introduced a path-planning algorithm tailored to field-based livestock farming environments, building upon the traditional A* algorithm. It constructed a digital elevation model, integrated an artificial potential field for evaluating multiple target points, calculated terrain slope, optimized the search neighborhood based on robot traversability, and employed Bézier curve segmentation for path optimization. This method segmented the path into multiple curves by evaluating the slopes of the lines connecting adjacent nodes, ensuring a smoother and more efficient route. The experimental results demonstrate its superiority to traditional A*, ensuring paths near multiple target points, significantly reducing the search space, and resulting in over 69.4% faster search speeds. Bézier curve segmentation delivers smoother paths conforming to robot trajectories. Key words: field-based livestock farming; agricultural robots; path planning; A* algorithm; artificial potential field; Bézier curve segmentation DOI: 10.25165/j.ijabe.20241704.8632 Citation: Qi H X, Jiang J Z, Wang C H. Path planning for agricultural robots in wild livestock farm environments. Int J Agric& Biol Eng, 2024; 17(4): 207–216.References
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[2] Zhou J B. Animal welfare farming and the development of modern animal husbandry in Jinzhong City. The Chinese Livestock and Poultry Breeding, 2021; 17(5): 6–7. (in Chinese)
[3] Temple D, Manteca X, Velarde A, Dalmau A. Assessment of animal welfare through behavioural parameters in Iberian pigs in intensive and extensive conditions. Applied Animal Behaviour Science, 2011; 131(1-2): 29–39.
[4] Wang T, Xu X B, Wang C, Li Z, Li D L. From smart farming towards unmanned farms: A new mode of agricultural production. Agriculture, 2021; 11(2): 145.
[5] Li D L, Yang H. State-of-the-art review for internet of thingsin agriculture. Transactions of the CSAM, 2018; 49(1): 1–20. (in Chinese)
[6] Nie P C, Zhang H, Geng H L, Wang Z, He Y. Current situation and development trend of agricultural internet of things technology. Journal of Zhejiang University (Agriculture & Life Sciences), 2021; 47(2): 135–146. (in Chinese)
[7] Sun J L, Li D H, Xu S W, Wu W B, Yang Y P. Development strategy of agricultural big data and information infrastructure. Strategic Study of CAE, 2021; 23(4): 10–18. (in Chinese)
[8] Lan Y B, Wang T W, Chen S D, Deng X L. Agricultural artificial intelligence technology: Wings of modern agricultural science and technology. Journal of South China Agricultural University, 2020; 41(6): 1–13. (in Chinese)
[9] Tian N, Yang X W, Shan D L, Wu J C. Status and prospect of digital agriculture in China. Journal of Chinese Agricultural Mechanization, 2019; 40(4): 210–213. (in Chinese)
[10] Zheng D R. Smart agriculture development in China: Present situation, problems and countermeasures. Agricultural Economy, 2020; 1: 12–14.
[11] Poultry Patrol, 2019. Available: https://poultrypatrol.com/?page_id=472. Accessed on [2021-08-06].
[12] ChickenBoy, 2021. Available: https://faromatics.com/. Accessed on [2021-08-06].
[13] Feng Q C, Wang X. Design of disinfection robot for livestock breeding. Procedia Computer Science, 2020; 166: 310–314.
[14] Wu T Y, Xu J H, Liu J Y. Cross-country path planning based on improved ant colony algorithm. Journal of Computer Applications, 2013; 33(4): 1157–1160. (in Chinese)
[15] Ji Y, Tanaka Y, Tamura Y, Kimura M, Umemura A, Kaneshima Y, et al. Adaptive motion planning based on vehicle characteristics and regulations for off-road UGVs. IEEE Transactions on Industrial Informatics, 2018; 15(1): 599–611.
[16] Tian H Q, Wang J Q, Huang H Y, Ding F. Probabilistic road-map method for path planning of intelligent vehicle based on artificial potential field model in China off-road environment. Acta Armamentarii, 2021; 42(7): 1496–1505. (in Chinese)
[17] Zhao M T. Path planning method for ground unmanned plat-form in cross-country environment. Master dissertation. Beijing: North China University of Technology, 2021; 59p. (in Chinese)
[18] Wang K, Wang H, Wu B. Optimal off-road path planning based on tabu table. In: Proceedings of the 8th China Command and Control Conference, Beijing, 2020; pp.380–385. (in Chinese)
[19] Hart P E, Nilsson N J, Raphael B. A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics, 1968; 4(2): 100–107.
[20] Li D F, Liu M, Zhang J P, Cheng E L. An improved A* algorithm applicable for campus navigation system. In: 2015 International Conference on Network and Information Systems for Computers, Wuhan: IEEE, pp.588–591. doi: 10.1109/ICNISC.2015.72.
[21] Yan X D, Chang T Q. Guo L B. Research on path planning of unmanned vehicle in off-road battlefield environment. Journal of Ordnance Equipment Engineering, 2022; 43(10): 288–293. (in Chinese)
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Published
2024-09-06
How to Cite
Qi, H., Jiang, J., & Wang, C. (2024). Path planning for agricultural robots in wild livestock farm environments. International Journal of Agricultural and Biological Engineering, 17(4), 207–216. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/8632
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Information Technology, Sensors and Control Systems
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