Browsing by Author "Guler, Samet"

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Distributed Formation Control of Drones With Onboard Perception
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2022) Kabore, Kader Monhamady; Guler, Samet; 0000-0001-5388-9649; 0000-0002-9870-166X; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı; Kabore, Kader Monhamady; Guler, Samet
While 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.
A distributed relative localization approach for air-ground robot formations with onboard sensing
(PERGAMON-ELSEVIER SCIENCE, 2023) Guler, Samet; Yildirim, Isa E; 0000-0002-9870-166X; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Guler, Samet; Yildirim, Isa E
In a multi-robot system, diversity in the sensing and motion models of robotic entities can improve the overall performance. While such heterogeneous systems offer peculiar advantages in terms of robustness and resiliency, positioning and situational awareness of individual robots in these systems remain a challenge. In this paper, the problem of relative localization in a system composed of a drone and multiple unmanned ground vehicles which are desired to move in formation is addressed. By utilizing a leader-follower formation graph, a distance-based relative localization algorithm based on an extended Kalman filter is proposed for online estimation of the relative positions among the ground vehicles. The necessary conditions to satisfy the observability of the unmeasured states are provided. In the proposed framework, the robots exchange a limited amount of information only and do not rely on an external infrastructure, GPS, or magnetometer. Furthermore, an application of the proposed localization framework integrated to custom formation control schemes is proposed. The performance of the proposed approach is evaluated through a set of simulation and real life experiments, and its advantages and limitations are discussed by means of a comparative study.
Peer-to-Peer Relative Localization of Aerial Robots With Ultrawideband Sensors
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC445 HOES LANE, PISCATAWAY, NJ 08855-4141, 2021) Guler, Samet; Abdelkader, Mohamed; Shamma, Jeff S.; 0000-0002-9870-166X; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Guler, Samet
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.