Localization of Indoor Mobile Robot Using Monte Carlo Localization Algorithm (MCL)

Authors

  • Ali Khaleel Mahmood Hay al-jamiaa, Baghdad 14001,Iraq
  • Robert Bicker Newcastle University, Address, Newcastle Upon Tyne NE1 7RU, UK

Keywords:

Localization, particles, importance weight, Pose, Differential drive, Kinematic, Global map.

Abstract

One of the challenging issues in robotics is to give a mobile robot the ability to recognize its initial pose ( position and orientation) without any human help. In this paper, the components of a mobile robot will be described in addition to the specification of the sensor that will be used. Then, the map of the environment  will be defined since it is pre-defined and stored in the memory of the robot. After that, a localization algorithm has been designed, analysed and implemented to develop the ability of a mobile robot to  recognize its initial pose. Finally, the final results that have been taken practically will discussed. These result will be divided into two main sub-sections; the first section describes the particles distribution over the working environment and their position update over a number of iterations. Second section will shows the update in the importance weight values over a number of iterations and for three different number of particles.  

References

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[8] F. K. Scott Anderson, Pam Lawhead, and Myles McNally, “Monte Carlo Localization” 2004.

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Published

2016-10-02

How to Cite

Mahmood, A. K., & Bicker, R. (2016). Localization of Indoor Mobile Robot Using Monte Carlo Localization Algorithm (MCL). American Scientific Research Journal for Engineering, Technology, and Sciences, 26(1), 108–126. Retrieved from https://www.asrjetsjournal.org/index.php/American_Scientific_Journal/article/view/2188

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Articles