Decentralized Path Planning And Coordination In Multi-Robot System Using ACO, A* and Wi-Fi-Based Peer-To-Peer Communication

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dc.contributor.advisorNeamah, Husam
dc.contributor.authorkhalil, Omar
dc.contributor.departmentDE--Műszaki Kar
dc.date.accessioned2025-09-04T16:11:03Z
dc.date.available2025-09-04T16:11:03Z
dc.date.created2024-12-14
dc.description.abstractIn the industrial, logistical, and service sectors, autonomous mobile robots are beginning to find value where their ability to negotiate changing surroundings is essential for operations including warehouse management, safety inspections, and product transportation. Path planning—that is, deciding ideal, collision-free trajectories inside complex surroundings—is one of the main challenges in robotics. Deterministic algorithms as A* are efficient in stationary, grid-based environments even if they lack flexibility inside dynamic or unpredictable contexts. By simulating the natural foraging activities of ants, Ant Colony Optimization (ACO) bio-inspired solutions provide extra flexibility and help to enable effective navigation in demanding conditions. Real-time coordination among multi-robot systems depends on distributed communication to enable actual coordination as well as to allow the sharing of data on paths, places, and environmental changes. But especially in large or dynamic contexts, traditional centralized communication systems have scalability and dependability problems. LoRa (Long Range) communication offers a low-power, long-range, distributed technology for data transfer therefore enabling real-time cooperation free from depending on a central server. To manage the complexity of dynamic path planning and communication inside multi-robot systems, this thesis explores the blend of Ant Colony Optimization (ACO) and Long Range (LoRa) technology. The objectives are integration of LoRa for peer-to--peer data sharing, implementation and evaluation of A* and ACO algorithms in both static and dynamic environments, and comparison of algorithmic performance in terms of computational efficiency, accuracy, and adaptability. The results indicate how successfully distributed communication protocols and adaptive path-planning algorithms mix to provide scalable, strong, and efficient multi-robot systems in relevant applications. This study offers a forum for further research on hybrid methods improving the interplay of intelligent algorithms with contemporary communication technology.
dc.description.correctorAdminnal egyeztetve áthelyeztem a megfelelő gyűjteménybe. PF
dc.description.courseMechatronikai mérnöki
dc.description.degreeBSc/BA
dc.format.extentMRS path planning
dc.identifier.urihttps://hdl.handle.net/2437/397292
dc.language.isoen
dc.rights.accessHozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.
dc.subjectPathfinding
dc.subjectAnt colony algorithm
dc.subjectA* algorithm
dc.subjectLoRa communication
dc.subject.dspaceMűszaki tudományok
dc.titleDecentralized Path Planning And Coordination In Multi-Robot System Using ACO, A* and Wi-Fi-Based Peer-To-Peer Communication
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