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A Perception Skill for Herding with a 4-Legged Robot

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Robot 2023: Sixth Iberian Robotics Conference (ROBOT 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 978))

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Abstract

While predators are important for the health of ecosystems, they can also pose challenges in certain situations, especially when they interact with human activities such as agriculture and livestock farming. In this way, effective predator control in extensive livestock farming could be achieved. This work proposes the design, development and integration of a robotic skill to distinguish between different species, grouping them according to whether they are potential predators or harmless species for a flock of sheep. This skill is integrated into a cognitive architecture for helping in scene understanding and integrated with the planning layer of a robotic sheepdog. Thus, if the perception system detects a potential predator, the action to be taken is to scare the predators and make the herd flee. Initial experimental results on images taken by a 4-legged robot achieve a Top-1 Accuracy of 0.9145 and a Parent Accuracy of 0.9576.

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References

  1. Animal image dataset (90 different animals) | kaggle. https://www.kaggle.com/datasets/iamsouravbanerjee/animal-image-dataset-90-different-animals

  2. Animals | kaggle. https://www.kaggle.com/datasets/jerrinbright/cheetahtiger-wolf

  3. Animals-10 | kaggle. https://www.kaggle.com/datasets/alessiocorrado99/ani-mals10

  4. Animals detection images dataset | kaggle. https://www.kaggle.com/datasets/antoreepjana/animals-detection-images-dataset

  5. Anzai, H., Sakurai, H.: Preliminary study on the application of robotic herding to manipulation of grazing distribution: behavioral response of cattle to herding by an unmanned vehicle and its manipulation performance. Appl. Anim. Behav. Sci. 256, 105751 (2022)

    Article  Google Scholar 

  6. Aquilani, C., Confessore, A., Bozzi, R., Sirtori, F., Pugliese, C.: Review: precision livestock farming technologies in pasture-based livestock systems. Animal 16(1), 100429 (2022)

    Article  Google Scholar 

  7. Arkin, R., Balch, T.: AuRA: principles and practice in review. J. Exp. Theor. Artif. Intell. 9, 175–189 (1970)

    Google Scholar 

  8. Bechar, A., Vigneault, C.: Agricultural robots for field operations: concepts and components. Biosys. Eng. 149, 94–111 (2016)

    Article  Google Scholar 

  9. Brown, J., Qiao, Y., Clark, C., Lomax, S., Rafique, K., Sukkarieh, S.: Automated aerial animal detection when spatial resolution conditions are varied. Comput. Electron. Agric. 193, 106689 (2022)

    Article  Google Scholar 

  10. Bustos, P., Manso, L.J., Bandera, A.J., Bandera, J.P., Garcia-Varea, I., Martinez-Gomez, J.: The cortex cognitive robotics architecture: use cases. Cogn. Syst. Res. 55, 107–123 (2019)

    Article  Google Scholar 

  11. Riego del Castillo, V., Sánchez-González, L., Campazas-Vega, A., Strisciuglio, N.: Vision-based module for herding with a sheepdog robot. Sensors 22(14), 5321 (2022)

    Google Scholar 

  12. Drach, U., Halachmi, I., Pnini, T., Izhaki, I., Degani, A.: Automatic herding reduces labour and increases milking frequency in robotic milking. Biosys. Eng. 155, 134–141 (2017)

    Article  Google Scholar 

  13. Fox, M., Long, D.: PDDL2.1: an extension to PDDL for expressing temporal planning domains. J. Artif. Intell. Res. (JAIR) 20, 61–124 (2003)

    Google Scholar 

  14. Gat, E., Bonnasso, R.P., Murphy, R., et al.: On three-layer architectures. Artif. Intell. Mob. Robot. 195, 210 (1998)

    Google Scholar 

  15. Ginés, J., Rodríguez-Lera, F.J., Martín, F., Guerrero, Á.M., Matellán, V.: Depicting probabilistic context awareness knowledge in deliberative architectures. Nat. Comput. 21, 1–12 (2022)

    Google Scholar 

  16. González-Santamarta, M.Á., Rodríguez-Lera, F.J., Matellán-Olivera, V., Fernández-Llamas, C.: Yasmin: yet another state machine. In: Tardioli, D., Matellán, V., Heredia, G., Silva, M.F., Marques, L. (eds.) ROBOT2022: Fifth Iberian Robotics Conference, pp. 528–539. Springer International Publishing, Cham (2023)

    Chapter  Google Scholar 

  17. González-Santamarta, M.A., Rodríguez-Lera, F.J., Fernández-Llamas, C., Matellán-Olivera, V.: MERLIN2: MachinEd Ros 2 pLaniNg. Softw. Impacts 15, 100477 (2023)

    Article  Google Scholar 

  18. Herlin, A., Brunberg, E., Hultgren, J., Högberg, N., Rydberg, A., Skarin, A.: Animal welfare implications of digital tools for monitoring and management of cattle and sheep on pasture. Animals 11(3), 829 (2021)

    Google Scholar 

  19. Ingrand, F., Ghallab, M.: Deliberation for autonomous robots: a survey. Artif. Intell. 247, 10–44 (2017)

    Article  MathSciNet  Google Scholar 

  20. Kellenberger, B., Marcos, D., Tuia, D.: Detecting mammals in UAV images: best practices to address a substantially imbalanced dataset with deep learning. Remote Sens. Environ. 216, 139–153 (2018)

    Article  Google Scholar 

  21. Laporte, I., Muhly, T.B., Pitt, J.A., Alexander, M., Musiani, M.: Effects of wolves on elk and cattle behaviors: Implications for livestock production and wolf conservation. PLoS ONE 5(8), e11954 (2010)

    Google Scholar 

  22. Lindqvist, B., et al.: Multimodality robotic systems: integrated combined legged-aerial mobility for subterranean search-and-rescue. Robot. Auton. Syst. 154, 104134 (2022)

    Google Scholar 

  23. Macenski, S., Martín, F., White, R., Ginés Clavero, J.: The marathon 2: a navigation system. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2020). https://github.com/ros-planning/navigation2

  24. Mansbridge, N., et al.: Feature selection and comparison of machine learning algorithms in classification of grazing and rumination behaviour in sheep. Sensors 18(10), 3532 (2018)

    Google Scholar 

  25. Matheson, C.A.: iNaturalist. Ref. Rev. 28(8), 36–38 (2014)

    Google Scholar 

  26. Muñoz, P., R-Moreno, M.D., Barrero, D.F., Ropero, F.: MoBAr: a hierarchical action-oriented autonomous control architecture. J. Intell. Robot. Syst. 94, 745–760 (2019)

    Google Scholar 

  27. Nakhaeinia, D., Tang, S.H., Noor, S.M., Motlagh, O.: A review of control architectures for autonomous navigation of mobile robots. Int. J. Phys. Sci. 6(2), 169–174 (2011)

    Google Scholar 

  28. Odintsov Vaintrub, M., Levit, H., Chincarini, M., Fusaro, I., Giammarco, M., Vignola, G.: Review: precision livestock farming, automats and new technologies: possible applications in extensive dairy sheep farming. Animal 15(3), 100143 (2021)

    Article  Google Scholar 

  29. Peter Bonasso, R., James Firby, R., Gat, E., Kortenkamp, D., Miller, D.P., Slack, M.G.: Experiences with an architecture for intelligent, reactive agents. J. Exp. Theor. Artif. Intell. 9(2–3), 237–256 (1997)

    Article  Google Scholar 

  30. Rejeb, A., Abdollahi, A., Rejeb, K., Treiblmaier, H.: Drones in agriculture: a review and bibliometric analysis. Comput. Electron. Agric. 198, 107017 (2022)

    Article  Google Scholar 

  31. Rivas, A., Chamoso, P., González-Briones, A., Corchado, J.M.: Detection of cattle using drones and convolutional neural networks. Sensors 18(7), 2048 (2018)

    Google Scholar 

  32. Rodríguez-Lera, F.J., Matellán-Olivera, V., Conde-González, M.Á., Martín-Rico, F.: HiMoP: a three-component architecture to create more human-acceptable social-assistive robots: motivational architecture for assistive robots. Cogn. Process. 19, 233–244 (2018)

    Article  Google Scholar 

  33. Stygar, A.H., Gómez, Y., Berteselli, G.V., Dalla Costa, E., Canali, E., Niemi, J.K., Llonch, P., Pastell, M.: A systematic review on commercially available and validated sensor technologies for welfare assessment of dairy cattle. Front. Vet. Sci. 8, 634338 (2021)

    Google Scholar 

  34. Su, J., Zhu, X., Li, S., Chen, W.H.: AI meets UAVs: a survey on AI empowered UAV perception systems for precision agriculture. Neurocomputing 518, 242–270 (2023)

    Article  Google Scholar 

  35. Tedeschi, L.O., Greenwood, P.L., Halachmi, I.: Advancements in sensor technology and decision support intelligent tools to assist smart livestock farming. J. Anim. Sci. 99(2), skab038 (2021)

    Google Scholar 

  36. Wiklund, E., Malmfors, G., Lundstrom, K., Rehbinder, C.: Pre-slaughter handling of reindeer bulls (Rangifer Tarandus Tarandus l.)-effects on technological and sensory meat quality, blood metabolites and muscular and Abomasal lesions. Rangifer 16(3), 109–117 (1996)

    Article  Google Scholar 

  37. Xu, B., et al.: Automated cattle counting using mask R-CNN in quadcopter vision system. Comput. Electron. Agric. 171, 105300 (2020)

    Article  Google Scholar 

  38. Yamasaki, Y., Morie, M., Noguchi, N.: Development of a high-accuracy autonomous sensing system for a field scouting robot. Comput. Electron. Agric. 193, 106630 (2022)

    Article  Google Scholar 

  39. Ye, P., Wang, T., Wang, F.Y.: A survey of cognitive architectures in the past 20 years. IEEE Trans. Cybern. 48(12), 3280–3290 (2018)

    Article  Google Scholar 

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Acknowledgements

Miguel Á. González-Santamarta acknowledges an FPU fellowship provided by the Spanish Ministry of Universities (FPU21/01438).

Funding

Grant TED2021-132356B-I00 funded by MCIN/AEI/10.13039/501100011033 and by the “European Union NextGenerationEU/PRTR”.

Author information

Authors and Affiliations

  1. Department of Mechanical, Computer Science and Aerospace Engineering, Universidad de León, Campus de Vegazana s/n, 24071, León, Spain

    Virginia Riego, Miguel Á González-Santamarta, Lidia Sánchez-González, Francisco J. Rodríguez-Lera & Vicente Matellán

Authors
  1. Virginia Riego
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  2. Miguel Á González-Santamarta
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  3. Lidia Sánchez-González
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  4. Francisco J. Rodríguez-Lera
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  5. Vicente Matellán
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Corresponding author

Correspondence to Lidia Sánchez-González .

Editor information

Editors and Affiliations

  1. Dep. de Eng. Electrotecnica e de Computadores, University of Coimbra, Coimbra, Portugal

    Lino Marques

  2. Department of Electrónica Industrial, University of Minho, Escola de Engenharia, Guimarães, Portugal

    Cristina Santos

  3. Department of Electrical Engineering, ESTIG, Polytechnic Institute of Bragança, Bragança, Portugal

    José Luís Lima

  4. Centro Universitario de la Defensa, Zaragoza, Spain

    Danilo Tardioli

  5. Centre for Automation and Robotics UPM-CSIC, Universidad Politécnica de Madrid, Madrid, Spain

    Manuel Ferre

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Riego, V., González-Santamarta, M.Á., Sánchez-González, L., J. Rodríguez-Lera, F., Matellán, V. (2024). A Perception Skill for Herding with a 4-Legged Robot. In: Marques, L., Santos, C., Lima, J.L., Tardioli, D., Ferre, M. (eds) Robot 2023: Sixth Iberian Robotics Conference. ROBOT 2023. Lecture Notes in Networks and Systems, vol 978. Springer, Cham. https://doi.org/10.1007/978-3-031-59167-9_29

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