The paper proposes a knowledge-based framework for mobile autonomous robots. It exploits data annotation for semantic-based context description. High-level event/situation detection and action decision are performed through a semantic matchmaking approach, supporting approximate matches and relevance-based ranking. The framework was fully implemented in a prototype built with off-the-shelf components, validated in a Search And Rescue (SAR) case study and evaluated in an early performance analysis, supporting the feasibility of the proposal. The work demonstrates novel analysis methods on data extracted by inexpensive sensors can yield useful results without requiring hefty computational resources.

Knowledge-based sensing/acting in mobile autonomous robots

LOSETO, Giuseppe;
2017

Abstract

The paper proposes a knowledge-based framework for mobile autonomous robots. It exploits data annotation for semantic-based context description. High-level event/situation detection and action decision are performed through a semantic matchmaking approach, supporting approximate matches and relevance-based ranking. The framework was fully implemented in a prototype built with off-the-shelf components, validated in a Search And Rescue (SAR) case study and evaluated in an early performance analysis, supporting the feasibility of the proposal. The work demonstrates novel analysis methods on data extracted by inexpensive sensors can yield useful results without requiring hefty computational resources.
978-1-5090-6724-4
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12572/7891
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
social impact