Browsing by Author "Güler, Samet"

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Aerial Swarms: Recent Applications and Challenges
(Springer, 2021) Mohamed Abdelkader; Samet Güler; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Güler, Samet
Purpose of review: Currently, there is a large body of research on multi-agent systems addressing their different system theoretic aspects. Aerial swarms as one type of multi-agent robotic systems have recently gained huge interest due to their potential applications. However, aerial robot groups are complex multi-disciplinary systems and usually research works focus on specific system aspects for particular applications. The purpose of this review is to provide an overview of the main motivating applications that drive the majority of research works in this field, and summarize fundamental and common algorithmic components required for their development. Recent findings: Most system demonstrations of current aerial swarms are based on simulations, some have shown experiments using few 10 s of robots in controlled indoor environment, and limited number of works have reported outdoor experiments with small number of autonomous aerial vehicles. This indicates scalability issues of current swarm systems in real world environments. This is mainly due to the limited confidence on the individual robot's localization, swarm-level relative localization, and the rate of exchanged information between the robots that is required for planning safe coordinated motions. Summary: This paper summarizes the main motivating aerial swarm applications and the associated research works. In addition, the main research findings of the core elements of any aerial swarm system, state estimation and mission planning, are also presented. Finally, this paper presents a proposed abstraction of an aerial swarm system architecture that can help developers understand the main required modules of such systems.
Deep Learning Based Formation Control of Drones
(Springer Science and Business Media Deutschland GmbH, 2021) Kabore, Kader M.; Güler, Samet; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Güler, Samet
Robot swarms can accomplish demanding missions fast, efficiently, and accurately. For a robust operation, robot swarms need to be equipped with reliable localization algorithms. Usually, the global positioning system (GPS) and motion capture cameras are employed to provide robot swarms with absolute position data with high precision. However, such infrastructures make the robots dependent on certain areas and hence reduce robustness. Thus, robots should have onboard localization capabilities to demonstrate a swarm behavior in challenging scenarios such as GPS-denied environments. Motivated by the need for a reliable onboard localization framework for robot swarms, we present a distance and vision-based localization algorithm integrated into a distributed formation control framework for three-drone systems. The proposed approach is established upon the bearing angles and the relative distances between the pairs of drones in a cyclic formation where each drone follows its coleader. We equip each drone with a monocular camera sensor and derive the bearing angle between a drone and its coleader with the recently developed deep learning algorithms. The onboard measurements are then relayed back to the formation control algorithm in which every drone computes its control action in its own frame based on its neighbors only, forming a completely distributed architecture. The proposed approach enables three-drone systems to perform in coordination indepen- dent of any external infrastructure. We validate the performance of our approach in a realistic simulation environment. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Distributed coverage control with mobile robots: A potential game approach
(Niğde Ömer Halisdemir Üniversitesi, 2023) Güler, Samet; 0000-0002-9870-166X; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Güler, Samet
The use of mobile robots in industrial applications has led to a demand for autonomous multi-robot systems with robust and distributed algorithms. A critical objective in such systems is coverage control, where a team of mobile robots need to respond to spatiotemporal events in a bounded region. Here, we address a specific coverage problem, where a group of mobile robots are tasked with responding to events by covering specific locations on two sides of a linear workstation. We formulate the problem as a game played by the mobile robots with well-designed player strategies, and we demonstrate that the resulting framework is a potential game based on equally shared utilities among the robots. The proposed framework is distributed and decentralized, allowing for anonymous identities and constrained sensing capabilities in the robots. A set of simulation studies verify our approach.
Mutual Relative Localization in Heterogeneous Air-ground Robot Teams
(Science and Technology Publications, Lda, 2022) Güler, Samet; Yıldırım, İ. Emre; Alabay, H. Halid; 0000-0002-9870-166X; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Güler, Samet; Yıldırım, İ. Emre; Alabay, H. Halid
Air and ground robots with distinct sensing characteristics can be combined in a team to accomplish demanding tasks robustly. A key challenge in such heterogeneous systems is the design of a local positioning methodology where each robot estimates its location with respect to its neighbors. We propose a filtering-based relative localization algorithm for air-ground teams composed of vertical-take-off-and-landing drones and unmanned aerial vehicles. The team members interact through a sensing/communication mechanism relying on onboard units, which results in a mutual connection between the air and ground components. Exploiting the supplementary features of omnidirectional distance sensors and monocular cameras, the framework can function in all environments without fixed infrastructures. Various simulation and experiment results verify the competency of our approach.
Optimal Target Capture and Station Keeping Control of Mobile Agents without Global Position Information
(Institute of Electrical and Electronics Engineers Inc., 2023) Mostafa, Ahmed Fahim; Fidan, Barış; Güler, Samet; 0000-0002-9870-166X; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Güler, Samet
The target capture problem, i.e., the problem of reaching a target zone, by a mobile robotic agent that cannot sense its own global position requires reactive motion control algorithms based on onboard sensor data. Although the existing solutions to the target capture problem provide robust convergence guarantees, they do not address the mobile agent's path and motion optimality. We address the agent path and motion optimality in target capture control and its extension to station keeping, i.e., steering the agent to a location that is pre-defined with respect to a set of beacons, in global positioning system (GPS)-denied environments. We formulate optimal control problems aiming to minimize the agent-target distance for target capture, and the difference of desired and actual agent-station distances for station keeping. We design and analyze a linear quadratic optimal control scheme involving a Luenberger observer based state estimator, for each of the target capture and station keeping problems. The proposed schemes outperform the previous approaches in numerical simulations in terms of agent path length and smoothness.
Practical Formation Acquisition Mechanism for Nonholonomic Leader-follower Networks
(Science and Technology Publications, Lda, 2022) Kabore, Kader Monhamady; Güler, Samet; 0000-0002-9870-166X; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı; Kabore, Kader Monhamady; Güler, Samet
A 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 mag-netometer 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.