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Mobile robot navigation in hilly terrains

Tennety, Srinivas
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1313757135

Abstract Details

2011, MS, University of Cincinnati, Engineering and Applied Science: Computer Science.
Mobile robot navigation in hilly terrains is challenging since the environment is unstructured, ill-conditioned and complex. The features of the terrain cannot be easily classified as traversable or non-traversable and therefore identification of paths that pose minimum danger to the robot becomes difficult. One approach to navigation in hilly terrain is based on unsupervised learning where robot learns via interacting with the environment based on trial and error. This method can be implemented using reinforcement learning. However, this approach is not applicable to real world applications as the robot might incur unrecoverable damage while interacting with the environment. Another approach is using human expert knowledge. Humans learn from their past experiences and display an uncanny ability to identify safe paths even in the presence of uncertainties. Therefore, it is beneficial to use the human expert knowledge when available, to aid in navigation of robots in complex terrains. This thesis presents a framework where human expert assistance is used to guide the robot to the goal through reinforcement learning techniques. When a prior knowledge of the terrain such as low resolution aerial view is available, a human expert can identify one or more paths from start to goal that are relatively safe to traverse. These expert paths are used to approximate a value matrix that would steer the robot from any start position in the terrain to the goal avoiding any unsafe regions that pose obvious danger. This approach aids in global path planning and does not take local terrain information in to the consideration that might not be available to the expert. To facilitate incorporation of local terrain information, a fuzzy logic controller is designed which can be used to update the value matrix based on the local sensor data. Experiments have been carried out in simulated hilly terrains with and without the expert paths to show the effectiveness of the approach. Different scenarios have been discussed to demonstrate the advantages of specifying multiple expert paths over few and also the integration of the fuzzy logic controller.
Raj Bhatnagar, PhD (Committee Chair)
Ernest Hall, PhD (Committee Member)
Manish Kumar, PhD (Committee Member)
80 p.
Robots
Mobile robot; Reinforcement learning; Hilly terrains; Autonomous Navigation

Recommended Citations

Citations

  • Tennety, S. (2011). Mobile robot navigation in hilly terrains [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1313757135

    APA Style (7th edition)

  • Tennety, Srinivas. Mobile robot navigation in hilly terrains. 2011. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1313757135.

    MLA Style (8th edition)

  • Tennety, Srinivas. "Mobile robot navigation in hilly terrains." Master's thesis, University of Cincinnati, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1313757135

    Chicago Manual of Style (17th edition)

ucin1313757135
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© 2011, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.