mobile robot navigation algorithm with reinforcement learning method enhanced.

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dc.contributor.advisorTaleb abdullah abdo, Mayar
dc.contributor.authorGmar, Ghofrane
dc.contributor.departmentDE--Műszaki Kar
dc.date.accessioned2025-12-18T09:22:00Z
dc.date.available2025-12-18T09:22:00Z
dc.date.created2025
dc.description.abstractThis thesis studies the challenges of mobile navigation in a dynamic environment. In this thesi,s different categories of algorithms (graph-based, sample-based,...) are tested in static and dynamic environments to study their functionality, and then finally, I integrate reinforcement learning and potential field methods for mobile navigation enhancement. The final navigation approach is a hybrid approach that consists of a global planner( Hybrid A*and D*lite), a local planner( Q-learning and potential field method). The global planner provides the robot with a pre-defined path based on the given map and the local planner helps the robot to react in real-time to unexpected changes in the environment. The hybrid approach is a more stable and adaptable method, although it should be refined to ensure stability and consistency in performance.
dc.description.courseMechatronical Engineeringen
dc.description.degreeBSc/BA
dc.format.extent56
dc.identifier.urihttps://hdl.handle.net/2437/400976
dc.language.isoen
dc.rights.infoHozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.
dc.subjectReinforcement learning
dc.subjectGlobal and local path planning
dc.subjectMobile navigation
dc.subject.dspaceEngineering Sciences::Engineering
dc.titlemobile robot navigation algorithm with reinforcement learning method enhanced.
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