A hover system for Microsoft AirSim using reinforcement learning (Q-learning and SARSA)

Autonomous Drone for Object Tracking

Deep Q learning with AirSim Simulator

Using Airsim API and UE4 to auto collect labeled images for deep learning.

This is an example of navigating drones in AirSim simulator by multithreading

A keras model for airsim city-environment

GitHub for Deep Learning on Jetson TX2

OpenAI Gym environment for AirSim

A basic implementation of imitation learning using Stable Baselines 2.10

Generates bounding boxes around objects in the scene in Airsim. Can also add noise to the bounding boxes to emulate a real object detector.

A Novelty Approach to Emulate Field Data Captured by Unmanned Aerial Vehicles for Training Deep Learning Algorithms Used for Search-and-Rescue Activities at Sea

Decentralized Control Methods were applied to multiple UAV's to form a required shape and sustain it throughout the motion

Master's thesis about autonomous navigation of a drone in indoor environments carried out to obtain the degree of Master of Science in Computer Science Engineering (University of Liège, academic year 2020-2021).

Telecopter: controlling a simulated quadcopter using webcam eye-tracking. See the demonstrations and poster in the website link provided. This was an academic computer engineering project.

A reinforcement lerning application for selfdriving cars using openai's gym, stablebaselines3 and Microsoft AirSim.

Precision Landing implmented in AirSim with python and openCV, window identification neural network in dron using tensorflow

Multi-Agent Deep Recurrent Q-Learning with Bayesian epsilon-greedy on AirSim simulator

Autonomous UAV Navigation without Collision using Visual Information in Airsim

Based on the data generated from FRCNN test, this program shows how the drone will fly according to it.

Drone Racing @ NeurIPS 2019, built on Microsoft AirSim