Context in Robotics and Information Fusion

  • Domenico D. Bloisi
  • Daniele Nardi
  • Francesco Riccio
  • Francesco Trapani
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)


Robotics systems need to be robust and adaptable to multiple operational conditions, in order to be deployable in different application domains. Contextual knowledge can be used for achieving greater flexibility and robustness in tackling the main tasks of a robot, namely mission execution, adaptability to environmental conditions, and self-assessment of performance. In this chapter, we review the research work focusing on the acquisition, management, and deployment of contextual information in robotic systems. Our aim is to show that several uses of contextual knowledge (at different representational levels) have been proposed in the literature, regarding many tasks that are typically required for mobile robots. As a result of this survey, we analyze which notions and approaches are applicable to the design and implementation of architectures for information fusion. More specifically, we sketch an architectural framework which enables for an effective engineering of systems that use contextual knowledge, by including the acquisition, representation, and use of contextual information into a framework for information fusion.


Context-awareness Autonomous robotics Context-dependent information fusion 



This work was supported by ONRG Grant N62909-14-1-N061.


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Copyright information

© Springer International Publishing Switzerland (outside the USA) 2016

Authors and Affiliations

  • Domenico D. Bloisi
    • 1
  • Daniele Nardi
    • 1
  • Francesco Riccio
    • 1
  • Francesco Trapani
    • 1
  1. 1.Department of Computer, Control, and Management EngineeringSapienza University of RomeRomeItaly

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