WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Conference Object Practical Formation Acquisition Mechanism for Nonholonomic Leader-Follower Networks(Scitepress, 2022) Kabore, Kader Monhamady; Guler, SametA grand challenge lying ahead of the realization of multi-robot systems is the lack of an adequate coordination mechanism with reliable localization solutions. In some workspaces, external infrastructure needed for precise localization may not be always available to the MRS, e.g., GPS-denied environments, and the robots may need to rely on their onboard resources without explicit communication. We address the practical formation control of nonholonomic ground robots where external localization aids are not available. We propose a systematic framework for the formation maintenance problem that is composed of a localization module and a control module. The onboard localization module relies on heterogeneity in sensing modality comprised of ultrawideband, 2D LIDAR, and camera sensors. Particularly, we apply deep learning-based object detection algorithm to detect the bearing between robots and fuse the outcome with ultrawideband distance measurements for precise relative localization. Integration of the localization outcome into a distributed formation acquisition controller yields high performance. Furthermore, the proposed framework can eliminate the magnetometer sensor which is known to produce unreliable heading readings in some environments. We conduct several realistic simulations and real world experiments whose results validate the competency of the proposed solution.Article Efficient Relative Localization and Coordination System for Unmanned Ground Vehicle Formations Under Directed Graph Structure(Cambridge Univ Press, 2025-02-24) Kabore, Kader M.; Guler, SametOnboard localization for multi-robot systems stands as a critical area of research with wide-ranging applications. This paper introduces an innovative framework for multi-robot localization, uniquely characterized by its onboard capability, thereby negating the dependency on external infrastructure. Our approach harnesses the inherent capabilities of each robot, enabling them to localize and synchronize their movements independently. The integration of cooperative localization algorithms with formation control mechanisms empowers a group of robots to sustain a predefined formation while following a linear trajectory. The efficacy of our framework is substantiated through comprehensive simulations and real-world experimental validations. We rigorously assess the system's resilience to localization inaccuracies and external disturbances, demonstrating its adaptability and consistency in maintaining formation under diverse conditions. Furthermore, we explore the scalability of our approach, highlighting its potential to manage varying numbers of robots and its applicability in tasks such as collaborative transportation.