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Navigation Under Uncertainty Based on Active SLAM Concepts

  • Henry CarrilloEmail author
  • José A. Castellanos
Chapter
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 42)

Abstract

This chapter addresses the problem of path planning considering uncertainty criteria over the belief space. We propose a path planning algorithm that uses a determinant-based measure of uncertainty and a reduced representation of the environment, in order to obtain the minimum uncertainty path from a roadmap. The determinant-based measure of uncertainty is borrowed from the active SLAM literature. We also present in this chapter an overview of the active SLAM problem. Our path planning proposal does not require a priori knowledge of the environment due to the construction of the roadmap via a graph-based SLAM algorithm. We report experimental results of our proposal in four datasets that show its feasibility to obtain the minimum uncertainty path towards an autonomous navigation framework. We also show an improvement in the computation time with respect to the state of the art.

Keywords

Path Planning Model Predictive Control Decision Point Partially Observable Markov Decision Process Path Planner 
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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Authors and Affiliations

  1. 1.Escuela de Ciencias Exactas e IngenieríaUniversidad Sergio ArboledaBogotáColombia
  2. 2.Departamento de Informática e Ingeniería de Sistemas, Instituto de Investigación en Ingeniería de AragónUniversidad de ZaragozaZaragozaSpain

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