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MultiVessel_Simulation

  • Description
  • MultiVessel_Simulation project has been developed to test and verify motion planning algorithms for unmanned surface vehicles (USVs) under realistic scenarios where multiple vessels travel around as they do in the real world. If you use this software, please include the following citation in your publication:
  • COLREG-Compliant Simulation Environment for Verifying USV Motion Planning Algorithms.  pdf 

  • Link
  • To download the code, click here. The documentation for using the package is provided along with the code.

  • License
  • Copyright 2023. Istanbul Medeniyet University. All rights reserved.

  • Acknowledgment
  • MultiVessel_Simulation project was granted scholarship by TUBITAK 2210/C National MSc Scholarship Program in the Priority Fields in Science and Technology.  Application Number: 1649B022205096.
    • Also, presentation of this project at OCEANS 2023 Conference was supported by 2224-A Grant Program for Participation in Scientific Meetings Abroad.  Application Number: 1919B022302606.

FixedWing_UAV_for_Aerial_Mapping

  • Description
  • This project presents the codes used in the article titled as "Aerial Mapping with a Low-Cost and Open-Source Fixed-Wing UAV". This project consists of three different packages:
  • GroundStation : Ground control station for UAV.
  • ardupilot_sim : ROS simulation package includes plugins necessary for SITL/Gazebo simulations with the APM stack.
  • ardupilot_mavros : Connecting Ardupilot to ROS. pdf 

  • Link
  • To download the code, click here. The documentation for using the package is provided along with the code.

  • License
  • Copyright 2022. Istanbul Medeniyet University. All rights reserved.

  • Acknowledgment
  • FixedWing_UAV_for_Aerial_Mapping project was granted scholarship by TUBITAK 2209-A - Research Project Support Programme for Undergraduate Students.  Application Number: 9999BugraVeUmut.

Dataset and CNN-based Models for Indoor Surface Classification

  • Description
  • In this project, we generated a dataset that contains three different types of indoor floor surfaces: carpet, tile and wood. Then, we used this dataset to train eight CNN-based models, including our proposed model, MobileNetV2-modified. If you use this software, please include the following citation in your publication:
  • Indoor surface classification for mobile robots.  pdf 

  • Link
  • To download the code, click here. The documentation for using the package is provided along with the code.

  • License
  • Copyright 2023. Istanbul Medeniyet University. All rights reserved.

  • Acknowledgment
  • This work was supported by Scientific Research Projects (BAP) through the Istanbul Sabahattin Zaim University (No. BAP-1000-88)

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