Applying Optimization Algorithms for Robot Path Planning

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dc.contributor.advisorAl Musawi , Husam
dc.contributor.authorHassan, Mohamed
dc.contributor.departmentDE--Műszaki Kar
dc.date.accessioned2025-09-04T15:25:19Z
dc.date.available2025-09-04T15:25:19Z
dc.date.created2025-05-17
dc.description.abstractThis thesis presents a hybrid optimization algorithm combining Genetic Algorithm and Particle Swarm Optimization to improve robot path planning in static and dynamic environments. The algorithm was tested in ROS 2 and Gazebo simulations using a two-wheeled robot with a LiDAR sensor. Results showed that the hybrid approach outperformed individual GA and PSO by generating shorter paths, better obstacle avoidance, and smoother motion. While effective, future work could enhance the algorithm by incorporating machine learning for real-time decision-making and obstacle prediction in complex environments.
dc.description.courseMechatronical Engineeringen
dc.description.degreeMSc/MA
dc.format.extent91
dc.identifier.urihttps://hdl.handle.net/2437/397245
dc.language.isoen
dc.rights.infoHozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.
dc.subjectOptimization Algorithms
dc.subjectPath Planning
dc.subjectGenetic Algorithm
dc.subjectParticle swarm optimization
dc.subject.dspaceEngineering Sciences::Engineering
dc.titleApplying Optimization Algorithms for Robot Path Planning
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