Browsing by Author "Guler, Samet"

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Distributed Coverage Control with Mobile Robots: A Potential Game Approach
(2023) Guler, Samet
Endüstriyel uygulamalarda mobil robotların kullanımı, gürbüz ve dağıtık algoritma içeren otonom çoklu-robot sistemlerine bir gereksinim oluşturmuştur. Bir robot takımının sınırlı bir alanda uzaysal-zamansal olaylara cevap vermesi anlamına gelen kapsama kontrolü bu tür sistemlerde kritik bir hedeftir. Bu çalışmada, bir grup mobil robotun doğrusal bir iş istasyonunun iki tarafında belirli lokasyonları kapsamakla görevli olduğu özel bir kapsama problemini ele alıyoruz. Problemi iyi kurgulanmış oyuncu stratejileri ile mobil robotlar arasında oynanan bir oyun olarak formalize ediyor ve ortaya çıkan yapının eşit paylaşılan fayda temelli bir potansiyel oyun olduğunu gösteriyoruz. Sunulan yapı, robotlarda anonim kimlikler ve kısıtlı algılama yeteneklerine izin veren dağıtık ve merkezi olmayan bir yapıdır. Bir grup simülasyon çalışması yaklaşımımızı doğrulamaktadır.
Citation - WoS: 1
Citation - Scopus: 1
Peer-to-Peer Localization via On-Board Sensing for Aerial Flocking
(Institute of Electrical and Electronics Engineers Inc., 2020) Omar Rajab, Fat Hy; Guler, Samet; Shamma, Jeff S.
The performance of mobile multi-robot systems dramatically depends on the mutual awareness of individual robots, particularly the positions of other robots. GPS and motion capture cameras are commonly used to acquire and ultimately communicate positions of robots. Such sensing schemes depend on infrastructure and restrict the capabilities of a multi-robot system, e.g., the robots cannot operate in both indoor and outdoor environments. Conversely, peer-to-peer localization algorithms can be used to free the robots from such infrastructures. In such systems, robots use on-board sensing to infer the positions of nearby robots. In this approach, it is essential to have a model of the motion of other robots. We introduce a flocking localization scheme that takes into account motion behavior exhibited by the other robots. The proposed scheme depends only on the robots' on-board sensors and computational capabilities and yields a more accurate localization solution than the peer-to-peer localization algorithms that do not take into account the flocking behavior. We verify the performance of our scheme in simulations and demonstrate experiments on two unmanned aerial vehicles. © 2022 Elsevier B.V., All rights reserved.
Citation - WoS: 2
Citation - Scopus: 2
A Distributed Relative Localization Approach for Air-Ground Robot Formations With Onboard Sensing
(Pergamon-Elsevier Science Ltd, 2023) 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.
Practical Formation Acquisition Mechanism for Nonholonomic Leader-Follower Networks
(Scitepress, 2022) Kabore, Kader Monhamady; Guler, 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 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.
Efficient Relative Localization and Coordination System for Unmanned Ground Vehicle Formations Under Directed Graph Structure
(Cambridge Univ Press, 2025) Kabore, Kader M.; Guler, Samet
Onboard 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.
Range-Based Wireless Sensor Network Localization by a Circumnavigating Mobile Anchor Without Position Information
(IEEE, 2024) Guler, Samet
Typical range-based wireless sensor network (WSN) localization approaches aim at estimating the sensor node positions by using a set of anchors with known positions. In some applications, assuming the knowledge of the anchors' positions may be impractical, and estimation of the sensors' positions in an arbitrary fixed frame may be sufficient. Considering such scenarios, we propose a WSN localization algorithm by single mobile anchor without self location information. The mobile anchor obtains distance measurements from the sensors while tracking a custom trajectory which is shown to improve the localization performance over time for high signal-to-noise ratio cases. By utilizing two stationary reference nodes within the WSN, the proposed framework generates sensor node position estimation up to translation and rotation with sufficient precision in the absence of global positioning aids. We foresee that the proposed framework can demonstrate benefits in several WSN applications ranging from internet-of-things to service robotics.
Citation - WoS: 18
Citation - Scopus: 29
Distributed Formation Control of Drones With Onboard Perception
(IEEE-Inst Electrical Electronics Engineers Inc, 2022) 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.
Citation - Scopus: 1
Optimal Target Capture and Station Keeping Control of Mobile Agents Without Global Position Information
(IEEE, 2023) Mostafa, Ahmed Fahim; Fidan, Baris; Guler, 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.
Citation - WoS: 43
Citation - Scopus: 49
Peer-to-Peer Relative Localization of Aerial Robots With Ultrawideband Sensors
(IEEE-Inst Electrical Electronics Engineers Inc, 2021) 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.
Citation - Scopus: 3
Deep Learning Based Formation Control of Drones
(Springer Science and Business Media Deutschland GmbH, 2021) Kabore, Kader Monhamady; Guler, 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 Elsevier B.V., All rights reserved.
Citation - WoS: 1
Citation - Scopus: 1
Mutual Relative Localization in Heterogeneous Air-Ground Robot Teams
(Scitepress, 2022) Guler, Samet; Yildirim, I. 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.