Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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Author: search.filters.author.Guler, SametScopus Q: search.filters.scopusQuartile.N/A

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  • Conference Object
    Range-Based Wireless Sensor Network Localization by a Circumnavigating Mobile Anchor Without Position Information
    (IEEE, 2024-06-11) 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.
  • Conference Object
    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.
  • Conference Object
    Citation - WoS: 1
    Citation - Scopus: 1
    Peer-to-Peer Localization via On-Board Sensing for Aerial Flocking
    (Institute of Electrical and Electronics Engineers Inc., 2020-06) Omar Rajab, Fat Hy; Guler, Samet; Shamma, Jeff S.; Rajab, Fat-Hy Omar
    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.
  • Conference Object
    Citation - Scopus: 1
    Optimal Target Capture and Station Keeping Control of Mobile Agents Without Global Position Information
    (IEEE, 2023-06-13) 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.
  • Conference Object
    Citation - WoS: 2
    Citation - Scopus: 2
    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.