Browsing by Author "Kabore, Kader Monhamady"

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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.
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.