WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 45Citation - Scopus: 52Peer-to-Peer Relative Localization of Aerial Robots With Ultrawideband Sensors(IEEE-Inst Electrical Electronics Engineers Inc, 2021-09) Guler, Samet; Abdelkader, Mohamed; Shamma, Jeff S.Robots in swarms take advantage of localization infrastructure, such as a motion capture system or global positioning system (GPS) sensors to obtain their global position, which can then be communicated to other robots for swarm coordination. However, the availability of localization infrastructure needs not to be guaranteed, e.g., in GPS-denied environments. Likewise, the communication overhead associated with broadcasting locations may be undesirable. For reliable and versatile operation in a swarm, robots must sense each other and interact locally. Motivated by this requirement, we propose an onboard relative localization framework for multirobot systems. The setup consists of an anchor robot with three onboard ultrawideband (UWB) sensors and a tag robot with a single onboard UWB sensor. The anchor robot utilizes the three UWB sensors to estimate the tag robot's location by using its onboard sensing and computational capabilities solely, without explicit interrobot communication. Because the anchor UWB sensors lack the physical separation that is typical in fixed UWB localization systems, we introduce filtering methods to improve the estimation of the tag's location. In particular, we utilize a mixture Monte Carlo localization (MCL) approach to capture maneuvers of the tag robot with acceptable precision. We validate the effectiveness of our algorithm with simulations as well as indoor and outdoor field experiments on a two-drone setup. The proposed mixture MCL algorithm yields highly accurate estimates for various speed profiles of the tag robot and demonstrates superior performance over the standard particle filter and the extended Kalman filter.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.Article Citation - WoS: 20Citation - Scopus: 29Distributed Formation Control of Drones With Onboard Perception(IEEE-Inst Electrical Electronics Engineers Inc, 2022) Kabore, Kader Monhamady; Guler, SametWhile aerial vehicles offer enormous benefits in several application domains, multidrone localization and control in uncertain environments with limited onboard sensing capabilities remains an active research field. A formation control solution which does not rely on external infrastructure aids such as GPS and motion capture systems must be established based on onboard perception feedback. We address the integration of onboard perception and decision layers in a distributed formation control architecture for three-drone systems. The proposed algorithm fuses two sensor characteristics, distance, and vision, to estimate the relative positions between the drones. Particularly, we utilize the omnidirectional sensing property of the ultrawideband distance sensors and a deep learning-based bearing detection algorithm in a filter. The entire system leads to a closed-loop perception-decision framework, whose stability and convergence properties are analyzed exploiting its modular structure. Remarkably, the drones do not use a common reference frame. We verified the framework through extensive simulations in a realistic environment. Furthermore, we conducted real world experiments using two drones and proved the applicability of the proposed framework. We conjecture that our solution will prove useful in the realization of future drone swarms.