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Evaluating ToRCH Structure for Characterizing Robots

  • Manal LinjawiEmail author
  • Roger K. Moore
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11650)

Abstract

Robots are increasingly used in different scenarios, depending on the development of their capabilities and performance. The accelerating growth of robotics applications requires a tool that can comprehensively capture a wide range of robot capabilities. In this study, we evaluate robot capabilities using a structure known as “Towards Robot Characterization” (ToRCH) recently developed to meet this need. This structure defines robot capabilities and consequently enables capabilities and applications to be mapped against each other. An experiment was conducted to obtain the capabilities of two scenarios presented by the NAO robot. The method used to capture the capabilities was performed via the ToRCH structure. ToRCH implicitly illustrates the scenarios in a simple capability profile. This research assesses two aspects of the ToRCH capabilities capturing process. First, it verifies the moderate agreement level among roboticists in using ToRCH to capture the robot’s capabilities. Second, it demonstrates the richness of the ToRCH structure for capturing robot capabilities compared to the Multi-Annual Roadmap (MAR) levels. This initial study evaluates the ToRCH method in extracting different capability levels and illustrating them in a robot capability profile. It therefore highlights the potential of ToRCH in classifying robots.

Keywords

Robot capabilities Capabilities profile Robot characterization 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of SheffieldSheffieldUK

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