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Energy efficient path planning: the effectiveness of Q-learning algorithm in saving energy
Includes bibliographical references.
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| Main Author: | |
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
Department of Electrical Engineering
2015
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| _version_ | 1867613144306679808 |
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| access_status_str | Open Access |
| author | Ogunniyi, Samuel |
| author2 | Tsoeu, Mohohlo Samuel |
| author_browse | Ogunniyi, Samuel Tsoeu, Mohohlo Samuel |
| author_facet | Tsoeu, Mohohlo Samuel Ogunniyi, Samuel |
| author_sort | Ogunniyi, Samuel |
| collection | Thesis |
| description | Includes bibliographical references. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/13308 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:31:28.055Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2015 |
| publishDateRange | 2015 |
| publishDateSort | 2015 |
| publisher | Department of Electrical Engineering |
| publisherStr | Department of Electrical Engineering |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/13308 Energy efficient path planning: the effectiveness of Q-learning algorithm in saving energy Ogunniyi, Samuel Tsoeu, Mohohlo Samuel Electrical engineering Includes bibliographical references. In this thesis the author investigated the use of a Q-learning based path planning algorithm to investigate how effective it is in saving energy. It is important to pursue any means to save energy in this day and age, due to the excessive exploitation of natural resources and in order to prevent drops in production in industrial environments where less downtime is necessary or other applications where a mobile robot running out of energy can be costly or even disastrous, such as search and rescue operations or dangerous environment navigation. The study was undertaken by implementing a Q-learning based path planning algorithm in several unstructured and unknown environments. A cell decomposition method was used to generate the search space representation of the environments, within which the algorithm operated. The results show that the Q-learning path planner paths on average consumed 3.04% less energy than the A* path planning algorithm, in a square 20% obstacle density environment. The Q-learning path planner consumed on average 5.79% more energy than the least energy paths for the same environment. In the case of rectangular environments, the Q-learning path planning algorithm uses 1.68% less energy, than the A* path algorithm and 3.26 % more energy than the least energy paths. The implication of this study is to highlight the need for the use of learning algorithm in attempting to solve problems whose existing solutions are not learning based, in order to obtain better solutions. 2015-07-03T07:54:57Z 2015-07-03T07:54:57Z 2014 Master Thesis Masters MSc http://hdl.handle.net/11427/13308 eng application/pdf Department of Electrical Engineering Faculty of Engineering and the Built Environment University of Cape Town |
| spellingShingle | Electrical engineering Ogunniyi, Samuel Energy efficient path planning: the effectiveness of Q-learning algorithm in saving energy |
| thesis_degree_str | Master's |
| title | Energy efficient path planning: the effectiveness of Q-learning algorithm in saving energy |
| title_full | Energy efficient path planning: the effectiveness of Q-learning algorithm in saving energy |
| title_fullStr | Energy efficient path planning: the effectiveness of Q-learning algorithm in saving energy |
| title_full_unstemmed | Energy efficient path planning: the effectiveness of Q-learning algorithm in saving energy |
| title_short | Energy efficient path planning: the effectiveness of Q-learning algorithm in saving energy |
| title_sort | energy efficient path planning the effectiveness of q learning algorithm in saving energy |
| topic | Electrical engineering |
| url | http://hdl.handle.net/11427/13308 |
| work_keys_str_mv | AT ogunniyisamuel energyefficientpathplanningtheeffectivenessofqlearningalgorithminsavingenergy |