Résumé
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 real-time 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.