A GNSS-free navigation strategy for orchards
Resumo
This paper proposes a GNSS-free strategy allowing a robot to navigate through orchards autonomously. It relies on a perception system made of four cameras arranged to enlarge the robot’s field of view. Thanks to this choice, it becomes possible to perform complete navigation (both alley crossing and headland maneuvers) using only visual data, thus increasing the task’s robustness. Results validate the proposed strategy.Referências
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Burgard, W., Hebert, M., and Bennewitz, M. (2016). World modeling. Springer handbook of robotics, pages 1135–1152.
Cadenat, V., Souéres, P., and Hamel, T. (2006). A reactive path-following controller to guarantee obstacle avoidance during the transient phase. International Journal of Robotics and Automation, 21(4):256–265.
da Silva Júnior, M. R. and Araújo, A. F. R. (2022). Subspace clustering multi-module self-organizing maps with two-stage learning. In Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., and Aydin, M., editors, Artificial Neural Networks and Machine Learning – ICANN 2022, pages 285–296, Cham. Springer Nature Switzerland.
Durand-Petiteville, A., Le Flecher, E., Cadenat, V., Sentenac, T., and Vougioukas, S. (2018). Tree detection with low-cost three-dimensional sensors for autonomous navigation in orchards. IEEE Robotics and Automation Letters, 3(4):3876–3883.
Foley, J. A., Ramankutty, N., Brauman, K. A., Cassidy, E. S., Gerber, J. S., Johnston, M., Mueller, N. D., O’Connell, C., Ray, D. K., West, P. C., et al. (2011). Solutions for a cultivated planet. Nature, 478(7369):337.
Grüne, L. and Pannek, J. (2017). Nonlinear model predictive control. In Nonlinear Model Predictive Control, pages 45–69. Springer.
Lenain, R., Tricot, N., and Berducat, M. (2019). La robotique agricole: l’essor de nouveaux outils pour l’agro-écologie. Sciences Eaux et Territoires, 29:64–67.
Li, M., Imou, K., Wakabayashi, K., and Yokoyama, S. (2009). Review of research on agricultural vehicle autonomous guidance. International Journal of Agricultural and Biological Engineering, 2(3):1–16.
Li, Y., Ruichek, Y., and Cappelle, C. (2011). 3d triangulation based extrinsic calibration between a stereo vision system and a lidar. Conference Record IEEE Conference on Intelligent Transportation Systems, pages 797–802.
Lowry, S., Sünderhauf, N., Newman, P., Leonard, J. J., Cox, D., Corke, P., and Milford, M. J. (2016). Visual place recognition: A survey. IEEE Transactions on Robotics, 32(1):1–19.
Mur-Artal, R., Montiel, J. M. M., and Tardos, J. D. (2015). Orb-slam: a versatile and accurate monocular slam system. IEEE transactions on robotics, 31(5):1147–1163.
Piegl, L. and Tiller, W. (1996). The NURBS Book. Springer-Verlag, New York, NY, USA, second edition.
Pire, T., Mujica, M., Civera, J., and Kofman, E. (2019). The rosario dataset: Multisensor data for localization and mapping in agricultural environments. The International Journal of Robotics Research, 38(6):633–641.
Siegwart, R., Nourbakhsh, I., and Scaramuzza, D. (2011). Introduction to autonomous mobile robots. A bradford book, Intelligent robotics and autonomous agents series. The MIT Press, second edition.
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Verbiest, R. and Ruysen, K., Vanwalleghem, T., Demeester, E., and Kellens, K. (2020). Automation and robotics in the cultivation of pome fruit: Where do we stand today ? Journal of Field Robotics, 38(4):513–531.
Vougioukas, S. G. (2019). Agricultural robotics. Annual Review of Control, Robotics, and Autonomous Systems, 2:365–392.
Burgard, W., Hebert, M., and Bennewitz, M. (2016). World modeling. Springer handbook of robotics, pages 1135–1152.
Cadenat, V., Souéres, P., and Hamel, T. (2006). A reactive path-following controller to guarantee obstacle avoidance during the transient phase. International Journal of Robotics and Automation, 21(4):256–265.
da Silva Júnior, M. R. and Araújo, A. F. R. (2022). Subspace clustering multi-module self-organizing maps with two-stage learning. In Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., and Aydin, M., editors, Artificial Neural Networks and Machine Learning – ICANN 2022, pages 285–296, Cham. Springer Nature Switzerland.
Durand-Petiteville, A., Le Flecher, E., Cadenat, V., Sentenac, T., and Vougioukas, S. (2018). Tree detection with low-cost three-dimensional sensors for autonomous navigation in orchards. IEEE Robotics and Automation Letters, 3(4):3876–3883.
Foley, J. A., Ramankutty, N., Brauman, K. A., Cassidy, E. S., Gerber, J. S., Johnston, M., Mueller, N. D., O’Connell, C., Ray, D. K., West, P. C., et al. (2011). Solutions for a cultivated planet. Nature, 478(7369):337.
Grüne, L. and Pannek, J. (2017). Nonlinear model predictive control. In Nonlinear Model Predictive Control, pages 45–69. Springer.
Lenain, R., Tricot, N., and Berducat, M. (2019). La robotique agricole: l’essor de nouveaux outils pour l’agro-écologie. Sciences Eaux et Territoires, 29:64–67.
Li, M., Imou, K., Wakabayashi, K., and Yokoyama, S. (2009). Review of research on agricultural vehicle autonomous guidance. International Journal of Agricultural and Biological Engineering, 2(3):1–16.
Li, Y., Ruichek, Y., and Cappelle, C. (2011). 3d triangulation based extrinsic calibration between a stereo vision system and a lidar. Conference Record IEEE Conference on Intelligent Transportation Systems, pages 797–802.
Lowry, S., Sünderhauf, N., Newman, P., Leonard, J. J., Cox, D., Corke, P., and Milford, M. J. (2016). Visual place recognition: A survey. IEEE Transactions on Robotics, 32(1):1–19.
Mur-Artal, R., Montiel, J. M. M., and Tardos, J. D. (2015). Orb-slam: a versatile and accurate monocular slam system. IEEE transactions on robotics, 31(5):1147–1163.
Piegl, L. and Tiller, W. (1996). The NURBS Book. Springer-Verlag, New York, NY, USA, second edition.
Pire, T., Mujica, M., Civera, J., and Kofman, E. (2019). The rosario dataset: Multisensor data for localization and mapping in agricultural environments. The International Journal of Robotics Research, 38(6):633–641.
Siegwart, R., Nourbakhsh, I., and Scaramuzza, D. (2011). Introduction to autonomous mobile robots. A bradford book, Intelligent robotics and autonomous agents series. The MIT Press, second edition.
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Verbiest, R. and Ruysen, K., Vanwalleghem, T., Demeester, E., and Kellens, K. (2020). Automation and robotics in the cultivation of pome fruit: Where do we stand today ? Journal of Field Robotics, 38(4):513–531.
Vougioukas, S. G. (2019). Agricultural robotics. Annual Review of Control, Robotics, and Autonomous Systems, 2:365–392.
Publicado
08/11/2023
Como Citar
DURAND-PETITEVILLE, A.; CADENAT, V.; ARAÚJO, A. F. R.; HERBULOT, A.; VILLEMAZET, A.; SILVA, A. F. L. A. da; RIOU, C..
A GNSS-free navigation strategy for orchards. In: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA (SBIAGRO), 14. , 2023, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 366-373.
ISSN 2177-9724.
DOI: https://doi.org/10.5753/sbiagro.2023.26580.