multirotor

* Documentation: https://multirotor.readthedocs.io/en/latest/ * Source code: https://github.com/hazrmard/multirotor * Demo/Quickstart: https://multirotor.readthedocs.io/en/latest/Quickstart.html

Documentation: https://multirotor.readthedocs.io/en/latest/ Source code: https://github.com/hazrmard/multirotor Demo/Quickstart: https://multirotor.readthedocs.io/en/latest/Quickstart.html There are many simulation libraries out there. For example AirSim using Unreal Engine, several implementations in Unity3D, Matlab toolboxes. I wanted a simple hackable codebase with which to experiment. So, I wrote this. Propellers, motors, batteries, airframe are their own components and can be mixed and matched. The code lets you create any number of propellers, and an optimization function learns a PID controller for that vehicle. Additionally, there are convenience functions to visualize in 3D and sensor measurements. Please let me know what you think :)

Location: Nashville, TN Remote: Yes (preferred) Willing to relocate: Yes Visa/sponsorship: No need Technologies: - python (pytorch/pandas/sklearn/django/flask etc.), - C/C++ (openmpi, cuda, petsc, gazebo), - matlab (simulink, several toolboxes), - javascript (d3, AngularJS), - T-SQL - prior experience with Go, Julia, VHDL, Assembly, LTSpice, Cadence Résumé/CV: https://iahmed.me/resume Email: ibrahim[dot]ahmed[at]vanderbilt.edu github: https://github.com/hazrmard I'm a PhD candidate (electrical engineering) intending to defend in early 2023. My work is in adaptive control using reinforcement learning. I combine classical control theory with machine learning. I apply my work to Unmanned Aerial Vehicles and smart buildings. Previously I've interned in research roles at big companies (AWS), start-ups, an