Adaptive multi-model and entropy-based localization on context-aware robotic system

Author

Kun Wang ; Liu, Peter X.

Author_Institution

Dept. of Syst. & Comput. Eng., Carleton Univ., Ottawa, ON, Canada

fYear

2014

fDate

5-8 Oct. 2014

Firstpage

3755

Lastpage

3760

Abstract

This paper presents an algorithm for robotic self-localization implemented on a context-aware robotic system. The self-localization algorithm is developed using the Particle Filtering (PF) method, with the enhancement from the technique of adaptive multi-model and entropy-based active sensing. The proposed solution is then utilized in the scenarios of robotic self-localization on a mobile robot platform. The feasibility and effectiveness of the adaptive multi-model and entropy based self-localization method is demonstrated in the experimental results.

Keywords

control engineering computing; mobile robots; particle filtering (numerical methods); ubiquitous computing; PF method; adaptive multimodel localization; adaptive multimodel sensing; context-aware robotic system; entropy-based active sensing; entropy-based localization; mobile robot platform; particle filtering method; robotic self-localization; self-localization algorithm; Adaptation models; Entropy; Filtering; Robot sensing systems; Standards; Particle filtering; adaptive multi-model; context-aware; entropy; self-localization;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics (SMC), 2014 IEEE International Conference on

Conference_Location

San Diego, CA

Type

conf

DOI

10.1109/SMC.2014.6974515

Filename

6974515