Mohamed H.
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Scrum Master, Autonomous Robotics-IoT, ROS1/2, AWS, Web, Embedded

Autonomous Robotics Software R&D Engineer | Founder @ DevServers We have special offers for long-term collaboration! In the past 2 years, I managed to help many clients with their Robotics projects over the world (Canada, UK, USA, Russia, Australia, Romania, Norway, Turkey, Malaysia, Ukraine, Poland, Italy, India, Taiwan, KSA, and Egypt). My experience has been formed mainly by self-learning and working on several Robotics projects that gave me a huge hands-on experience with many software technologies and working on different types of robots(Mobile Robots and Humanoids). Recent Projects 🔥 ----------------------- • WebRTC • Web-based platform to teleoperate remote vehicles. • Vision-based ball-tracking ABB Robot-arm • Autonomous Agriculture tractor. • Autonomous Disinfection Robot. • AWS (IoT Core, GreenGrass, Lambda)-based Autonomous racing vehicle similar to MIT MuSHR vehicle. • Real-time 360 Video stitching for Autonomous Driving. • Multi-robots Gazebo simulation on shared machines {​​​​​​​​​​​​​ local(Gazebo+RVIZ) and AWS EC2 instance(SLAM +Navigation +DL-segmentation ROS pkg) }​​​​​​​​​​​​​ • Working on developing a generic SDK for any simulated/real stereo-based robot. • Building MuJoCo robotic models and control it from ROS1/2 pkgs. • 3D Lidar-SLAM open-source algorithms testing with different datasets. • Autonomous Mobile Wheeled Diff. Robot using D435i+T265. • Autonomous differential mobile robot with arm using NVidia Isaac SDK. • Reinforcement Learning for a humanoid model in Mujoco with ROS2 communication. • Autonomous steering mobile robot using ZED, Hokuyo, Xsens IMU, and RTK-GPS Ublox for the agriculture industry. • Autonomous Differential mobile robot for Snow Scrapping. • Reinforcement Learning for Robotics using OpenAI and Gazebo-gym ROS packages. • Humanoid robot(Gazebo simulation and real-world). • Mobile robot gazebo model with RealSense D415, D435, D435i, and RealSense TS265 cameras on AWS RoboMaker. • Mobile robot with researching about different SLAM algorithms. • Enhanced Visual SLAM ROS package that's derived from the RTAB-Map package. • Creating a complex Environment in GzWeb on EC2. • Multi-robot task allocation simulation for validating a research paper and it got published. Hands-on Expertise ----------------------- • Experienced with Docker and Kubernetes. • Experienced with Jetson Nano, TX2, Xavier, Xavier NX, and Raspberry PI3/4. • Experienced with Intel D435i, T265, and ZED1/2 cameras. • Experienced with Linux Development and Shell Scripting. • Experienced with move_base, EKF, gmapping, RTAB-Map, ROS planners, Urdf/Xacro robot descriptions, and different ROS wrappers for physical sensors/actuators. • Experienced with AWS and GCP services • Experienced with Git. More details --------------- My interest in Autonomous Robotic Systems requires me to get professional with many technologies such as Computer Vision, Machine Learning, ROS1/2, MuJoCo, Gazebo, SolidWorks, Control, Stage, RVIZ, Matlab robotics toolbox,... etc. I've taken many online courses to gather all the basics in a proper way. Recently, I've been Mastering several of the AWS services such as (VPN-connection, RoboMaker, GreenGrass, IoT, S3, EC2, Cloud9, FreeRTOS, Kinesis, Lambda, DaynmoDB, Glue, Athena, CloudWatch, Serverless application, IAM,... ) Programming Skills ------------------------- • C++: Experienced with Data structures, Algorithms, OOP • Python: Experienced with OOP, many packages, frameworks, and writing automation scripts • Image Processing using open cv in Python • ROS1/2 programming(ROS Nav-stack, Moveit, Gazebo APIs, services, actions, synchronization, ros_controllers, ...) • Stage and Gazebo(URDF/SDF, SolidWorks urdf_exporter, Joint transmission, Joint controllers, all sensor plugins, gazebo physics properties, Gazebo-gym and OpenAI, ....) ML, RL, DL ---------------------- • Understanding the basic concepts about the RL structure and different baselines. • Understanding GAN models • Tested many HandPose models. • Machine Learning course from Stanford University by Andrew NG from Coursera • Introduction into deep learning using Pytorch Nanodegree from Udacity • Deep Learning Specialization from via Coursera. Tools ------- VSCode, ClearCase, sniff++, notepad++, remote machine software, NoMachine, AWS, Anaconda, Visual Studio, CodeBlocks, Eclipse, Sublime, Atmel Studio, AVRDUDESS, JT link programmer, Qt Creator, Matlab /Octave & Simulink, Git /Github, Proteus, Gazebo, GZweb, Stage, LaTeX.

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  • Reinforcement Learning
  • Robot Operating System
  • AWS IoT Core
  • Control Engineering
  • Robotics
  • WebRTC
  • Ubuntu