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Object Identifier System for Autonomous UAV: A subsystem providing methods for detecting and descending to an object. The object is located in a specified area with a coverage algorithm.
Halmstad University, School of Information Technology.
Halmstad University, School of Information Technology.
2018 (English)Independent thesis Basic level (university diploma), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Using UAVs in everyday life has been increasing in recent years. UAV is an agile vehicle and often comes integrated with a camera and sensors which makes it suitable for object detection and tracking. In this thesis, we present a subsystem with a limited hardware setup only consisting of an on-board computer and a camera that is mounted on a UAV. The subsystem provides techniques to maneuver, detect and descend to an object, all executed autonomously. The system is implemented in Robotic Operating System (ROS). The object detection is implemented as a convolutional neural network provided by TensorFlow Object Detection API. This thesis covers the necessary steps to adopt a pre-trained TensorFlow model to specific needs and compares three different TensorFlow models considering accuracy, frames per second and energy efficiency. Additionally, methodologies to cover a predefined area and position an object in relation to the camera is proposed. Experiments are executed both in a real-world and simulated environment and the results are promising for the implemented system.

Abstract [sv]

Användandet av UAVs i det vardagliga livet har ökat markant de senaste åren. En UAV är ett agilt fordon som ofta kommer integrerat med en kamera samt sensorer som gör det till ett lämpligt fordon för objektigenkänning och spårning. I den här avhandligen presenterar vi ett delsystem med en hårdvaruplattform endast bestående av en inbyggd dator och en kamera. Delsystemet tillhandahåller metoder som gör det möjligt för UAV:en att styras, känna igen objekt och landa på det detekterade objektet autonomt. Systemet implementeras i Robotic Operating System (ROS). Objektigenkänningen är implementerat som ett konvolutionellt neuralt nätverk tillhandahållt av TensorFlow Object Detection API. Avhandlingen omfattar stegen nödvändiga att ta för att anpassa en TensorFlow model till sina egna behov och gör jämförelser mellan tre olika Tensorflow modeller med avseende på precision, bildrutor per sekund och energi effektivitet. Dessutom presenteras metoder för att söka av ett fördefinierat område och positionering av ett objekt relativt komeran. Under experiment, både i simulering och verkliga världen, har lovande resultat framkommit.

Place, publisher, year, edition, pages
2018.
National Category
Robotics
Identifiers
URN: urn:nbn:se:hh:diva-37072OAI: oai:DiVA.org:hh-37072DiVA, id: diva2:1218680
Subject / course
Computer science and engineering
Educational program
Computer Engineer, 180 credits
Supervisors
Examiners
Available from: 2018-06-19 Created: 2018-06-14 Last updated: 2018-06-19Bibliographically approved

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CiteExportLink to record
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