Abstract
Robotic gras** detection, a specialized domain in robotics, focuses on enabling robots to autonomously identify optimal object - gras** positions through sensory data analysis. This innovation has transformative implications for robotic interactions in diverse environments, including agriculture. The integration of deep learning techniques has significantly advanced this field, allowing robots to learn from extensive datasets and adapt to varied contexts, enhancing their versatility. This article provides a comprehensive survey of methodologies for estimating gras** configurations, explores diverse sensor modalities for data acquisition, and categorizes robotic gras** into two paradigms: known and unknown objects, based on familiarity. The study culminates in outlining critical considerations for develo** robust gras** detection systems.
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Beguiel Bergor, B., Hadj Baraka, I., Zardoua, Y., El Mourabit, A. (2024). Recent Developments in Robotic Gras** Detection. In: Ezziyyani, M., Kacprzyk, J., Balas, V.E. (eds) International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD'2023). AI2SD 2023. Lecture Notes in Networks and Systems, vol 931. Springer, Cham. https://doi.org/10.1007/978-3-031-54288-6_4
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DOI: https://doi.org/10.1007/978-3-031-54288-6_4
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