A knowledge base for robots to model the real-world as a hypergraph
conference paper
Robotic systems operating in the real world would benefit from a clear semantic model to understand the real world. Such semantics are typically captured in a knowledge structure. The speed at which robots need to update their real-world knowledge (because of new sensory observations)
prevented the use of complex knowledge structures, due to which they typically rely on hierarchical structures and triples. However, the shift from automated to autonomous robotics implied that more knowledge about the real-world is necessary, thereby requiring many work-arounds to maintain such a hierarchical knowledge base. To remove such work-arounds, this article adopts the richer hypergraph model for creating an actual knowledge base implemented on a real robotic system. Experiments show that the use of a hypergraph, without any work-arounds, still allows real-time updates of the knowledgebase with actual information extracted from the robot’s sensors.
prevented the use of complex knowledge structures, due to which they typically rely on hierarchical structures and triples. However, the shift from automated to autonomous robotics implied that more knowledge about the real-world is necessary, thereby requiring many work-arounds to maintain such a hierarchical knowledge base. To remove such work-arounds, this article adopts the richer hypergraph model for creating an actual knowledge base implemented on a real robotic system. Experiments show that the use of a hypergraph, without any work-arounds, still allows real-time updates of the knowledgebase with actual information extracted from the robot’s sensors.
Topics
TNO Identifier
968062
ISBN
9781665434164
Publisher
Institute of Electrical and Electronics Engineers Inc.
Source title
Proceedings - 2021 5th IEEE International Conference on Robotic Computing, IRC 2021, 5th IEEE International Conference on Robotic Computing, IRC 2021, 15 November 2021 through 17 November 2021
Pages
119-120
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