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EbruBaglan/README.md

🏗️ Under Construction

  • 🔭 Currently adding ROS+Gazebo tutorials here.

🙋‍♀️ About Me

  • 🏢 3 years of work experience in Defense Industry as R&D Engineer.
  • 🎓 MSc. within the scope of JEMARO (Japan-Europe Master's on Advanced Robotics, Erasmus+ Mundus Program)
    • 🎓 M.Sc. in Advanced Robotics in Keio University, Japan, 2nd year of JEMARO
    • 🎓 M.Sc. in Advanced Robotics in University of Genova(UniGe), Italy, 1st year of JEMARO
  • 🎓 BSc. in Mechanical Engineering in METU, Turkiye
  • 📫 How to reach me and connect with me
    • 📫 Send me an email gmail badge
    • 🤝 Connect me on linkedin badge

📁 About Repositories

  • final_assignment The Research Track course's last assignment to use ROS nodes, Python, CMake etc. The explanations to be added later.

  • ML4Rob-Labs: The repository which contains Machine Learning for Robotics course labs of UniGe. Implemented algorithms are:

    • Naive Bayes classifier,
    • Linear regression,
    • kNN classifier,
    • Neural Networks.

  • ARP-HW2_IPC_benchmark and ARP-HW2_IPC_benchmark_ALL: The second assignment of Advanced Robot Programming course(Inter-process Communication based) of UniGe. The IPC methods are compared for their transfer speeds. Compared IPC methods are:

    • unnamed pipes (in _ALL version),
    • named pipes,
    • sockets (in _ALL version),
    • shared memory.

  • ARP-HW1_Tower_Crane: The first assignment of Advanced Robot Programming course(Inter-process Communication based) of UniGe. In a tower crane scenario, two motors are supposed to order motion on the crane. The project is done using pipes. For details, please visit the repo.

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    The diary to register all my steps to revive the icub residing in ROMER @ METU.

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  2. RT1-assignment1-solution RT1-assignment1-solution Public

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  3. ARP-HW1_TowerCrane ARP-HW1_TowerCrane Public

    The group project for Advanced Robot Programming course of UniGe. Involves two motors and interprocess communication tools.

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  5. ML4Rob-Labs ML4Rob-Labs Public

    This repository consists of the Machine Learning for Robotics laboratory activities: a Naive Bayes classifier, linear regression, kNN classifier and Neural Networks, namely.

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    A benchmark to test the two methods of interprocess communications, shared memory and named pipes.

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