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A Legislation-Aware Robotic Framework for Autonomous Fertilization Near Protected Water Bodies

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Bibliographic Details
Title: A Legislation-Aware Robotic Framework for Autonomous Fertilization Near Protected Water Bodies
Authors: Müller, Nikolas, Kadi, Ahmad, Mugnier, Marie-Laure, Pérution-Kihli, Guillaume, Ulliana, Federico, Atzmueller, Martin
Contributors: Ulliana, Federico
Source: 2025 European Conference on Mobile Robots (ECMR). :1-8
Publisher Information: IEEE, 2025.
Publication Year: 2025
Subject Terms: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [SDV.SA.AGRO] Life Sciences [q-bio]/Agricultural sciences/Agronomy
Description: Putting autonomous mobile robots into practice typically requires the integration of background information such as considering and including both legal as well as regulatory aspects into the process. This document presents a novel AI framework for such an integration. In particular, the framework features (I) a declarative formalization of environmental regulations for an integration into explainable processes using AI, (II) a learning-based method to fuse realtime sensor data from the mobile robot into an implicit spatial map, which can be queried for environmental measurements, and (III) a logical knowledge base, which leverages reasoning to detect and explain violations of the legislation. The resulting process is validated on simulated data as a proof-of-concept. Its context is autonomous fertilization in the agricultural domain near protected water bodies.
Document Type: Article
Conference object
File Description: application/pdf
DOI: 10.1109/ecmr65884.2025.11163145
Access URL: https://hal-lirmm.ccsd.cnrs.fr/lirmm-05165190v1
Rights: STM Policy #29
Accession Number: edsair.doi.dedup.....078c12f29888969b8f2219b4e565d2c8
Database: OpenAIRE
Description
Abstract:Putting autonomous mobile robots into practice typically requires the integration of background information such as considering and including both legal as well as regulatory aspects into the process. This document presents a novel AI framework for such an integration. In particular, the framework features (I) a declarative formalization of environmental regulations for an integration into explainable processes using AI, (II) a learning-based method to fuse realtime sensor data from the mobile robot into an implicit spatial map, which can be queried for environmental measurements, and (III) a logical knowledge base, which leverages reasoning to detect and explain violations of the legislation. The resulting process is validated on simulated data as a proof-of-concept. Its context is autonomous fertilization in the agricultural domain near protected water bodies.
DOI:10.1109/ecmr65884.2025.11163145