Browsing by Subject "UAV"

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    Collision resilient foldable micro aerial robot
    (2019-09) Dilaveroğlu, Levent
    Collision management strategies are integral part of micro air vehicles for the reliability of their operation. Collision avoidance strategies require enhanced environmental and situational awareness for generating evasive maneuver trajectories. Simpler and more adaptable option is to prepare for collisions and design the physical frame around predicted collision patterns. In this work, a mechanically compliant frame design collaborating origami-inspired foldable robotics methods with protective shock absorbing or guiding elements has been proposed for a collision resilient quad-rotor UAV. General workings and mathematical model of quadrotor has been explained to inform the reader further about the quadrotor mechanics. 2D design of the foldable structure and the manufacturing process, including electronic hardware elements and software has been discussed. Control scheme, communication and operation is explained in detail to be an informative guideline for the future air vehicle projects of the Bilkent Miniature Robotics Lab.
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    Design, fabrication, and soft impact modeling and simulation of a collision-resilient foldable micro quadcopter
    (2022-09) Abazari, Amirali
    Despite the appreciable advancements in mobile robot navigation and obstacle avoidance algorithms using an abundance of sensors and sensor fusion methods, the navigation of moving robots through confined and cluttered spaces is still a great challenge. The physical interaction and collision between mobile robots and surrounding obstacles in these environments are unavoidable. This becomes more concerning for the case of Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicles (UAV) that the system is naturally unstable, and a minor fault or disturbance may result in severe crashes. In this thesis, a Collision-Resilient Quad-rotor Micro Aerial Vehicle (MAV) is designed using the Origami-inspired design fabrication techniques. The quadcopter is lightweight (220g max) and is designed for outdoor inspection and surveillance missions. This compliant drone provides an stable flight for a duration of 5 - 10 min, depends on the flight condition. A dynamic model is derived for the quadcopter to represent the realistic features designed for this specific UAV. In addition to that, the impact of the compliant body to surrounding obstacles is modeled as visco-elastic contact force and is added to the quadcopter’s dynamic model. The contact dynamic friction force between the protective soft bumpers and the surface is also modeled. The developed dynamic model is then used to simulate the impact of the collisionresilient quadcopter in two different simulation environments; MATLAB Simulink and ROS Gazebo. A cascaded PID control scheme is suggested for low-level (attitude) and high-level (global position) control of the drone in experiments and simulation. The result of these soft impact simulations closely imitate the collision-resilient properties of the actual quadcopter in experiments. Coefficient of Restitution (CoR) for the compliant drone impact, both in simulations and experiments, is in the interval [0.5 0.6]. This shows a great capacity for the drone to dampen the collisions.
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    Facility location decisions for drone delivery with riding: a literature review
    (Elsevier Ltd, 2024-07) Dükkancı, O.; Campbell, J.F.; Yetiş Kara, Bahar
    This study presents a comprehensive literature survey on facility location problems for drone (uncrewed vehicle) delivery in situations where drones can ride in or on other vehicles. This includes facilities visited by only one type of vehicle, as well as facilities visited by both drones and other vehicles. Unlike traditional facility location problems for delivery systems with one vehicle type, hybrid vehicle-drone delivery systems usually require determining locations where the two vehicle types meet and separate. The main goals of this paper are to review the large volume of drone delivery literature with riding from a facility location perspective to provide a connection between the studies from different research areas that cover similar problems, and to highlight future research directions in this area. We first review the functions of drones, including aerial and ground drones, and the different types of facilities used for hybrid vehicle-drone delivery systems. The literature is categorized based on the presence of resupply operations, the locations of drone launch and retrieval points, the types of drones (aerial or ground) and the location space (discrete or continuous). Each category is analyzed in terms of the modeling approach, decision(s), objective function(s), constraints and additional features. The paper concludes with promising future research directions.
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    Facility location decisions for drone delivery: A literature review
    (Elsevier BV, 2023-10-30) Dükkancı, Okan; Campbell, James F.; Yetiş Kara, Bahar
    This study presents a comprehensive literature survey on facility location problems for drone (uncrewed vehicle) delivery, where either (i) drones are the only vehicles, or (ii) drones and other vehicles (e.g., trucks) work together for delivery, but drones do not ride in or on the other vehicles. The main goals of this review are to identify and categorize fundamental facility location problems associated with drone delivery, to document the large volume of research in this area, to provide a connection between the studies from different research fields that consider similar location problems, and to highlight promising areas for future research. We first discuss and classify the functions of drones and the various types of facilities used for drone and hybrid vehicle-drone (e.g., truck and drone, or transit and drone) delivery systems, including drone bases (fixed or temporary), other vehicle bases, recharging stations, (re)supply points, and platooning points. The literature is reviewed and categorized based on the types of vehicles involved and their interactions, the types of facilities located, the types of drones and the location space (discrete or continuous). Each category is analyzed in terms of the modeling approach, decision(s), the objective function(s), constraint(s) and additional feature(s). The paper concludes with some promising future research directions.
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    Relative localization for swarm aerial vehicles
    (IEEE, 2022-08-29) Aytekin, Alptuğ; Bağcı, Furkan; Bozdağ, Mustafa; Camuzcu, Nisanur; Duru, Alperen; Elma, Mehmet Saim; Maghsoudi, Amirhossein; Barshan, Billur; Köse, Serdar; Akman, Çağlar
    In Unmanned Aerial Vehicle (UAV) swarm formation, it is required that UAVs in the swarm should have the location information of other swarm members and all UAVs should be a part of the decision process. Global Navigation Satellite Systems, GNSS, is not a viable option for dense formation flight due to its low accuracy and susceptibility to jamming. To overcome these issues, ultra-wideband (UWB) signals can be used. UWB signals are resilient to jamming and can be used for highly accurate localization algorithms, thanks to their wide bandwidth. In this paper, two techniques on relative localization of the swarm members by utilizing UWB signals are proposed and tested. Their localization performance results are presented.
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    Simultaneous localization and mapping for unmanned aerial vehicles
    (2008) Kök, Mehmet
    Most mobile robot applications require the robot to be able to localize itself in an unknown environment without prior information so that the robot can navigate and accomplish tasks. The robot must be able to build a map of the unknown environment while simultaneously localizing itself in this environment. The Simultaneous Localization and Mapping (SLAM) is the formulation of this problem which has drawn a considerable amount of interest in robotics research for the past two decades. This work focuses on the SLAM problem for single and multiple agents equipped with vision sensors. We develop a vision-based 2-D SLAM algorithm for single and multiple Unmanned Aerial Vehicles (UAV) flying at constant altitude. Using the features of images obtained from an on-board camera to identify different landmarks, we apply different approaches based on the Extended Kalman Filter (EKF), the Information Filter (IF) and the Particle Filter (PF) to the SLAM problem. We present some simulation results and provide a comparison between the different implementations. We find Particle Filter implementations to perform better in estimations when compared to EKF and IF, however EKF and IF present more consistent results.
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    Skydatanet: an object detection algorithm with 2d gaussian loss for uav-based aerial ımages
    (2025-01-09) Özkanoğlu, Mehmet Akif; Beğen, Ali C.; Özer, Sedat
    In this paper, we introduce a novel object detection algorithm based on the center-point detection. In our architecture, we introduce using two HourGlass architecture as the backbone, and we introduce using a new module to unify the predictions made after each backbone. Furthermore, since bounding boxes are in varying aspect ratios, as opposed to using a scalar Gaussian variance, we introduce using 2D variance in the Gaussian loss function to predict center-points in our network. We present the performance of our proposed improvements on three aerial datasets by comparing them to center-point based detection algorithms.
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    YOLODrone+: improved YOLO architecture for object detection in UAV images
    (IEEE, 2022-08-29) Şahin, Öykü; Özer, Sedat
    The performance of object detection algorithms running on images taken from Unmanned Aerial Vehicles (UAVs) remains limited when compared to the object detection algorithms running on ground taken images. Due to its various features, YOLO based models, as a part of one-stage object detectors, are preferred in many UAV based applications. In this paper, we are proposing novel architectural improvements to the YO-LOv5 architecture. Our improvements include: (i) increasing the number of detection layers and (ii) use of transformers in the model. In order to train and test the performance of our proposed model, we used VisDrone and SkyData datasets in our paper. Our test results suggest that our proposed solutions can improve the detection accuracy.