Çoklu robot sistemleri için lokalizasyon algoritması tasarımı ve gerçekleştirilmesi

Multi-robot systems (MRS) can accomplish complex tasks extremely challenging for a single robot. For instance, in applications such as cooperative transportation, area coverage and search and rescue operations, MRS can be the best choice. MRS divides tasks into simpler commands and assigns them to individual robots. This structure favored decentralized approaches, which provide significant advantages, including scalability and robustness to single points of failure. Formation control in MRS relies on strong robot positioning, which is particularly challenging in environments where GPS is not available and external infrastructure does not exist. In outdoor environment, GPS can provide absolute positioning but may be insufficient for swarm robotics in environments such as indoors or tunnels. Indoor localization solutions, such as motion capture systems, come at a high cost and require additional infrastructure setup procedure. These limitations highlight the need for robust, onboard localization systems suitable for swarm robotics applications. This work presents a novel decentralized, marker-free localization framework for MRS that relies entirely on onboard capabilities, eliminating dependence on external infrastructure. To find a localization solution for MRS, our method combines cooperative localization algorithms powered by deep learning detections with formation control mechanisms. We validate the framework through extensive simulations and real-world experiments to validate the effectiveness of the proposed framework. We explore the system's scalability by adapting to varying team sizes, showcasing its effectiveness in applications. This work also provides an open-source dataset for ground robots, thereby promoting further research in MRS.