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Online Inference of Robot Navigation Parameters from a Semantic Map

Benjamin Kisliuk; Christoph Tieben; Nils Niemann; Christopher Bröcker; Kai Lingemann; Joachim Hertzberg
In: Proceedings of the 14th International Conference on Agents and Artificial Intelligence. International Conference on Agents and Artificial Intelligence (ICAART-2022), February 3-5, Online, SCITEPRESS, 2022.


Agriculture is becoming one of the key application fields for mobile robots. At the same time it poses serious challenges for true autonomous systems due to its heterogeneous and dynamic nature. To act robustly and reliably, robotic behaviour needs to be controlled by an intelligence, making explainable and informed decisions based on knowledge of its surroundings. However, this knowledge cannot only be derived from sensor data but has to be based on prior knowledge and external sources as well to comprehensively represent a robots deployment site. By representing this knowledge in formal and thus machine readable way, automated inference improves the handling of the complex nature of these requirements. In this paper, we show how quantitative and qualitative control parameters regarding a mobile robots navigation can be derived from a manually modelled semantic map of an agricultural deployment site. Also we describe how such a system can be integrated into a typical ROS system architecture. By making the derived knowledge easily available, the robotic system is enabled to dynamically adapt route planning on an agricultural deployment site and to switch between different local planning algorithms according to situational and prior knowledge.