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Mobile Robot Localization with Active Landmark Deployment

Kulkarni, Suyash M
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535702460399878

Abstract Details

2018, MS, University of Cincinnati, Engineering and Applied Science: Mechanical Engineering.
This thesis focuses on localization of mobile robots in indoor environments without the use of pre-deployed sensor networks. The localization of mobile robots in indoor environment is very difficult due to the absence of Global Positioning System (GPS) signals. The problem of localization in indoor environments is usually solved using Simultaneous Localization and Mapping (SLAM) algorithms. However, these algorithms often prove to be insufficient in complex and dynamic environments. An example of such environment is a tunnel which does not provide distinguishing environmental features for the SLAM algorithms to work properly. The absence of visible light makes it difficult to use visual sensors such as cameras. In such environments, without the use of pre-deployed sensor networks, it is very difficult to obtain localization of the robot. This thesis proposes the use of active deployment of landmarks by the robot itself. The robot is assumed to have a physical capacity of carrying Radio Frequency (RF) Beacons which are deployed in the environment based on the calculations of the predicted co-variance of position error. The robot tries to achieve its goal based on the combination of data from the encoder and RF beacons. The system of transmitting RF beacons is deployed by the mobile robot which carries the receiver beacon as it moves through the environment. Using a combination of Dead Reckoning and tri-lateration of position using the RF beacons in the framework of Extended Kalman filter, the robot localized in the environment. As the RF beacons are deployed by the mobile robot, their locations are approximated using Levenberg- Marquardt algorithm. The mobile robot monitors the estimate of its localization error which is then used to make decisions to deploy successive beacons. The operative structure of the mobile robot is provided in the thesis which could be used to achieve desired navigation.
Manish Kumar, Ph.D. (Committee Chair)
Rui Dai, Ph.D. (Committee Member)
David Thompson, Ph.D. (Committee Member)
58 p.
Robots
Localization; Extended Kalman Filter; Trilateration; Mobile Robot; Active Deployment; Dead Reckoning

Recommended Citations

Citations

  • Kulkarni, S. M. (2018). Mobile Robot Localization with Active Landmark Deployment [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535702460399878

    APA Style (7th edition)

  • Kulkarni, Suyash. Mobile Robot Localization with Active Landmark Deployment. 2018. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535702460399878.

    MLA Style (8th edition)

  • Kulkarni, Suyash. "Mobile Robot Localization with Active Landmark Deployment." Master's thesis, University of Cincinnati, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535702460399878

    Chicago Manual of Style (17th edition)

ucin1535702460399878
502
© 2018, some rights reserved.Creative Commons License Mobile Robot Localization with Active Landmark Deployment by Suyash M Kulkarni is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.