Year: 2016
Type: conference-paper
Abstract: We present the integration of heterogeneous knowledge bases and reasoning mechanisms in the SHERPA project. SHERPA is about a complex search and rescue scenario that requires planning and reasoning about actions of mixed human-robot teams in space and in time. This has been achieved by integrating different sources of knowledge and data, namely, OpenStreetMap for static map data, the Robot Scene Graph for composing the OpenStreetMap data with dynamic data from the environment and the rescue team, and KnowRob with the SHERPA ontology for abstract reasoning in the application domain. This work explains how these knowledge bases were integrated by model composition and model to model transformations and an application dependent bridge. Design decisions are discussed and an example is given that explains the usage of the heterogeneous knowledge and reasoning methods.
Source: Lirias (KU Leuven)
Institutions: KU Leuven, University of Bremen
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: European Commission, KU Leuven
Awards: Web-enabled and Experience-based Cognitive Robots that Learn Complex Everyday Manipulation Tasks, G3207356259, Best Practice in Robotics, RObot control for Skilled ExecuTion of Tasks in natural interaction with humans; based on Autonomy, cumulative knowledge and learning, G393177772, G5702163051, Smart collaboration between Humans and ground-aErial Robots for imProving rescuing activities in Alpine environments
OpenAlex ID: W2583550995