Optimization of Path Planning Algorithm

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dc.contributor.advisorAlmusawi, Husam
dc.contributor.authorEgharevba, Osasere
dc.contributor.departmentDE--Műszaki Kar
dc.date.accessioned2025-09-04T16:20:31Z
dc.date.available2025-09-04T16:20:31Z
dc.date.created2024-12-02
dc.description.abstractThis paper focuses on enhancing autonomous navigation through improved path planning techniques. It highlights the limitations of traditional algorithms like A* and Dijkstra's in dynamic and complex environments. The study proposes modifications to the Grey Wolf Optimizer (GWO), a bio-inspired metaheuristic algorithm, to address these challenges. By introducing nonlinear convergence factors and adaptive parameter tuning, the research aims to enhance GWO's exploration and exploitation balance, leading to better obstacle avoidance and path smoothness. The methodology includes testing in both simple and complex static environments, utilizing random maze generation and benchmark comparisons with other optimization algorithms. The results demonstrate GWO's superior performance in terms of convergence rate, computational efficiency, and path quality compared to methods like Particle Swarm Optimization and Ant Colony Optimization. The study concludes that the proposed enhancements make GWO a viable solution for real-world path planning problems in robotics and autonomous systems.
dc.description.courseMechatronical Engineeringen
dc.description.degreeBSc/BA
dc.format.extent69
dc.identifier.urihttps://hdl.handle.net/2437/397298
dc.language.isoen
dc.rights.accessHozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.
dc.subjectGrey wolf optimizer
dc.subjectPath planning
dc.subjectAutonomous navigation
dc.subject.dspaceEngineering Sciences::Engineering
dc.titleOptimization of Path Planning Algorithm
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