Browsing by Author "Güler, Samet"

Now showing 1 - 4 of 4

Çoklu Robot Sistemleri için Lokalizasyon Algoritması Tasarımı ve Gerçekleştirilmesi
(2024) Kabore, Kader Monhamady; Güler, Samet
Çok robotlu sistemler (MRS), tek bir robot için son derece zorlayıcı olan karmaşık görevleri gerçekleştirebilir. Örneğin, iş birliğiyle taşıma, alan kapsama ve arama-kurtarma operasyonları gibi uygulamalarda, MRS en iyi seçenek olabilir. MRS, görevleri daha basit komutlara bölerek bireysel robotlara atar. Bu yapı, ölçeklenebilirlik ve tek bir hata noktasına karşı dayanıklılık gibi önemli avantajlar sağlayan merkezi olmayan yaklaşımlara ilgiyi artırmıştır. MRS'deki formasyon kontrolü, özellikle GPS'in bulunmadığı ve dış altyapının olmadığı ortamlarda güçlü robot konumlandırmasına dayanır. Dış ortamlarda GPS mutlak konumlandırma sağlayabilir ancak kapalı alanlar veya tüneller gibi ortamlarda sürü robotları için yetersiz kalabilir. Hareket yakalama sistemleri gibi kapalı alan konumlandırma çözümleri, yüksek maliyetli olup ek altyapı kurulum prosedürleri gerektirir. Bu sınırlamalar, sürü robotikleri uygulamaları için uygun, dayanıklı ve dahili konumlandırma sistemlerine olan ihtiyacı vurgulamaktadır. Bu çalışma, tamamen dahili yeteneklere dayanan, dış altyapıya bağımlılığı ortadan kaldıran yeni bir merkezi olmayan, işaretleyicisiz konumlandırma çerçevesi sunmaktadır. MRS için bir konumlandırma çözümü bulmak amacıyla, yöntemimiz, derin öğrenme ile güçlendirilmiş iş birliği temelli konumlandırma algoritmalarını formasyon kontrol mekanizmalarıyla birleştirmektedir. Önerilen çerçevenin etkinliğini doğrulamak için kapsamlı simülasyonlar ve gerçek dünya deneyleri gerçekleştirilmiştir. Sistem ölçeklenebilirliği, farklı ekip boyutlarına uyum sağlayarak test edilmiştir ve uygulamalardaki etkinliği gösterilmiştir. Bu çalışma ayrıca yer robotları için açık kaynaklı bir veri seti sunarak MRS alanında daha fazla araştırmayı teşvik etmektedir.
Erken Orman Yangını Tespiti için Otonom Heterojen Çoklu Robot Sistemi Tasarımı
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2022) Serin, Ömer Faruk; Güler, Samet
The usage of autonomous multi-robot systems for human life-endangering applications is emerging. Early wildfire detection and firefighting are two example applications. In this study, a heterogenous multi-robot system is proposed for both fire detection and response. The system employs an unmanned aerial vehicle for beyond-visual line-of-sight observations and an unmanned ground robot for fire extinguisher carrying. The proposed method uses ultrawideband (UWB) communication and ranging modules for the relative localization of robots during their movements. A specially trained YOLOv7 object detection model is used for robustly detecting forest fires and smoke while a modified version of the Vector Field Histogram Plus (VFH+) algorithm on the ground robot is used for obstacle avoidance while navigating. The structural design of the system requires no odometry or mapping of the environment hence improving the applicability of the system while decreasing system complexity. Additionally, the proposed UWB localization system is shown to be robust in long-lasting operations unlike many odometry-based approaches which accumulate errors with time. Moreover, localization of the UAV is realized with only three independent UWB-based range measurements and the altitude information of the UAV. The system is tested both in a realistic simulation environment and in real experimental setups with multiple runs. Results showed that the proposed system is improvable for better detection and practical to implement even in a dense forest environment without the need for GPS sensors, odometer data, or magnetometer.
Mesafe ve Görüntü Kullanan Dronlar ile Koordine Hedef Teşhisi ve Takibi
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2022) Alabay, Hüsnü Halid; Güler, Samet
Robot autonomy refers to the ability to carry out objectives by perceiving the environment and deciding on the actions required without human interruption. Although autonomous aerial robots offer big advantages in our daily life, online localization and control remain the biggest challenge lying ahead of aerial robot implementations. For single robot applications, GPS, and motion capture (mocap) systems can be utilized for outdoor and indoor applications, respectively. However, when it comes to multi-robot systems, the relative localization problem needs to be solved beyond the single robot localization problem. Furthermore, GPS signals are not available everywhere, and mocap systems limit the application space of multi-robot systems. Motivated by the industrial application scenarios, we address the relative localization and docking problem in multi-drone systems where drones do not utilize any external infrastructure for localization. We consider a two-drone system that aims at docking a target object which consists of an ultrawideband (UWB) distance sensor. The drones are equipped with UWB sensors and cameras and try to localize the target object and dock around it in a pre-defined configuration in the absence of GPS and magnetometer sensors and external infrastructures. We design an extended Kalman filter based on the dynamic model of the drone-target configuration that fuses the distance and vision sensor outputs. Particularly, we use the YOLO algorithm for the bearing detection between the drones and the target. Next, we devise and implement a switching-based distributed formation control algorithm and integrate it into the estimation algorithm. We demonstrate the performance of our algorithm in several simulation studies in a realistic Gazebo environment. Finally, we provide primary experimental results and a roadmap to the full implementation of the system.
Hava-Yer Robot Ekipleri için Bağıl Lokalizasyon ve Koordinasyon
(Abdullah Gül Üniversitesi Fen Bilimleri Enstitüsü, 2021) Yıldırım, İsa Emre; Yıldırım, İsa Emre; Güler, Samet
Recently, autonomous robot teams have been implemented broadly in many social and military applications such as firefighting, agriculture, search and rescue, mapping, target tracking, and docking. A mix of different types of ground robots and aerial vehicles can be employed in a robot team to accomplish tasks efficiently and robustly. Such heterogeneous systems show unparalleled benefits in complex tasks compared to teams composed of identical robot types. In a heterogeneous robot team, precise relative localization, i.e., estimating a robot's position with respect to its neighbor robots, plays a key role. We develop a relative localization system for air-ground robot teams where an aerial vehicle and multiple ground robots work in coordination to perform a reliable relative position estimation. The aerial vehicle is employed to detect special patterns on the ground robots by an onboard monocular camera, while the ground robots perform relative position estimation based on inter-robot distances acquired by ultrawideband sensors and the bearing and heading angles received from the aerial vehicle by communication. Thus, the aerial vehicle serves as an absolute frame provider for the entire team. Notably, each robot in the team uses onboard communication and computation capabilities solely without any need for an external localization infrastructure, making the team realizable in all conditions including GNSS-denied environments. We propose a multi-rate extended Kalman filter algorithm to handle different data rates of the sensor measurements. We carried out an extensive simulation study with a drone and five ground robots in a leader-first follower formation. Simulation results showed a successful estimation performance with an error rate of up to five centimeters in the relative position estimations in both axes. Keywords: Relative localization, Heterogeneous multi-robot systems, estimation algorithms, ultrawideband sensors