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Bayesian-Based Decision-Making Strategy

  • Yue WangEmail author
  • Islam I. Hussein
Chapter
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 427)

Abstract

In the previous chapter, the deterministic awareness-based decision-making strategies for search and classification are developed assuming perfect sensing under both centralized and decentralized system architectures. This chapter focuses on the development of real-time decision-making criteria given limited sensory resources under probabilistic frameworks. The uncertainties in sensor perception is taken into account during MAV decision-making. Bayesian-based and information-theoretic search versus classification decision-making strategies are developed that result in guaranteed detection and classification of all the unknown objects in the domain. The basic problem setup is first introduced in Section 4.1. In Section 4.2, the probabilistic counterparts of the task metrics are provided. Based on the problem formulation and task metrics, a Bayesian-based decision-making strategy is developed in Section 4.3. Both a numerical example and Monte-Carlo simulation experiments are presented in Section 4.4 to study the performance of the proposed decision-making strategy.

Keywords

Object Detection Sensory Range Uncertainty Level Unknown Object Nominal Control 
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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Copyright information

© Springer-Verlag London Ltd. 2012

Authors and Affiliations

  1. 1.Department of Electrical EngineeringUniversity of Notre DameNotre DameUSA
  2. 2.Department of Mechanical EngineeringWorcester Polytechnic InstituteWorcesterUSA

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