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A Comparison of Decision Making Criteria and Optimization Methods for Active Robotic Sensing

Part of the Lecture Notes in Computer Science book series (LNCS,volume 2542)

Abstract

This work presents a comparison of decision making criteria and optimization methods for active sensing in robotics. Active sensing incorporates the following aspects: (i ) where to position sensors, and (ii ) how to make decisions for next actions, in order to maximize information gain and minimize costs. We concentrate on the second aspect: “Where should the robot move at the next time step?”. Pros and cons of the most often used statistical decision making strategies are discussed. Simulation results from a new multisine approach for active sensing of a nonholonomic mobile robot are given.

Keywords

  • Mobile Robot
  • Information Gain
  • Relative Entropy
  • Markov Decision Process
  • Task Execution

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Mihaylova, L., Lefebvre, T., Bruyninckx, H., Gadeyne, K., De Schutter, J. (2003). A Comparison of Decision Making Criteria and Optimization Methods for Active Robotic Sensing. In: Dimov, I., Lirkov, I., Margenov, S., Zlatev, Z. (eds) Numerical Methods and Applications. NMA 2002. Lecture Notes in Computer Science, vol 2542. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36487-0_35

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  • DOI: https://doi.org/10.1007/3-540-36487-0_35

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