IMPLEMENTATION OF PATH PLANNING ALGORITHMS ON A MOBILE ROBOT IN DYNAMIC INDOOR ENVIRONMENTS

Search for this publication on Google Scholar
Panchpor, A. (2018). IMPLEMENTATION OF PATH PLANNING ALGORITHMS ON A MOBILE ROBOT IN DYNAMIC INDOOR ENVIRONMENTS. Unc Charlotte Electronic Theses And Dissertations.
Download PDF

Analytics

199 views ◎
118 downloads ⇓


Abstract

The objective of this thesis was to review and analyze existing algorithms for robot localization and mapping in dynamic indoor environments. Some of the existing algorithms include occupancy grid approach, artificial intelligence approach, dense scene flow approach for localization and mapping in dynamic environments. The occupancy grid approach maintains static and dynamic occupancy grids in parallel. The artificial intelligence approach uses efficient path planning algorithms like Reinforcement Learning along with Simultaneous Localization and Mapping (SLAM) to find a path and map the unknown dynamic environment. The dense scene flow approach detects moving objects to improve the visual SLAM process.The review phase of this work included identifying different approaches and classifying them. The classification of the different approaches was based on sensors used (in the data acquisition process), localization method used, and map management techniques used. These approaches were categorized into fixed sets. The review of these algorithms lead to a comparison of the already obtained results of these algorithms.The analysis phase of this work included implementing path planning algorithms on TurtleBot2 with real-time obstacle detection and avoidance. The results obtained were evaluated in terms of a set of fixed parameters. These parameters were accuracy of the algorithm, total time for execution, and errors in planning the final path for the robot.

Details
Author

Panchpor, Aishwarya

Title

IMPLEMENTATION OF PATH PLANNING ALGORITHMS ON A MOBILE ROBOT IN DYNAMIC INDOOR ENVIRONMENTS

Physical Description

1 online resource (92 pages) : PDF

Date

2018

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

The objective of this thesis was to review and analyze existing algorithms for robot localization and mapping in dynamic indoor environments. Some of the existing algorithms include occupancy grid approach, artificial intelligence approach, dense scene flow approach for localization and mapping in dynamic environments. The occupancy grid approach maintains static and dynamic occupancy grids in parallel. The artificial intelligence approach uses efficient path planning algorithms like Reinforcement Learning along with Simultaneous Localization and Mapping (SLAM) to find a path and map the unknown dynamic environment. The dense scene flow approach detects moving objects to improve the visual SLAM process.The review phase of this work included identifying different approaches and classifying them. The classification of the different approaches was based on sensors used (in the data acquisition process), localization method used, and map management techniques used. These approaches were categorized into fixed sets. The review of these algorithms lead to a comparison of the already obtained results of these algorithms.The analysis phase of this work included implementing path planning algorithms on TurtleBot2 with real-time obstacle detection and avoidance. The results obtained were evaluated in terms of a set of fixed parameters. These parameters were accuracy of the algorithm, total time for execution, and errors in planning the final path for the robot.

Genre

masters theses

Subjects--Topics

Electrical engineering
Robotics

Degree

M.S.

Keywords

Autonomous System
Dynamic Indoor Environments
Obstacle Avoidance
Path Planning and Navigation
Simultaneous Localization and Mapping (slam)

Subject Area

Electrical Engineering

Advisor(s)

Conrad, James

Committee Members

Willis, Andrew
Sass, Ronald

Degree Note

Thesis (M.S.)--University of North Carolina at Charlotte, 2018.

Rights Statement

This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). For additional information, see http://rightsstatements.org/page/InC/1.0/.

Rights Holder Information

Copyright is held by the author unless otherwise indicated.

Identifier

Panchpor_uncc_0694N_11784

Permalink

http://hdl.handle.net/20.500.13093/etd:877