Energy efficient path planning: the effectiveness of Q-learning algorithm in saving energy

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dc.contributor.advisorTsoeu, Mohohlo Samuelen_ZA
dc.contributor.authorOgunniyi, Samuelen_ZA
dc.date.accessioned2015-07-03T07:54:57Z
dc.date.available2015-07-03T07:54:57Z
dc.date.issued2014en_ZA
dc.descriptionIncludes bibliographical references.en_ZA
dc.description.abstractIn 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.en_ZA
dc.identifier.apacitationOgunniyi, S. (2014). <i>Energy efficient path planning: the effectiveness of Q-learning algorithm in saving energy</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/13308en_ZA
dc.identifier.chicagocitationOgunniyi, Samuel. <i>"Energy efficient path planning: the effectiveness of Q-learning algorithm in saving energy."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2014. http://hdl.handle.net/11427/13308en_ZA
dc.identifier.citationOgunniyi, S. 2014. Energy efficient path planning: the effectiveness of Q-learning algorithm in saving energy. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Ogunniyi, Samuel AB - 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. DA - 2014 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2014 T1 - Energy efficient path planning: the effectiveness of Q-learning algorithm in saving energy TI - Energy efficient path planning: the effectiveness of Q-learning algorithm in saving energy UR - http://hdl.handle.net/11427/13308 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/13308
dc.identifier.vancouvercitationOgunniyi S. Energy efficient path planning: the effectiveness of Q-learning algorithm in saving energy. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2014 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/13308en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Electrical Engineeringen_ZA
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherElectrical engineeringen_ZA
dc.titleEnergy efficient path planning: the effectiveness of Q-learning algorithm in saving energyen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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