There are 0 repository under airsim-simulator topic.
This code is the result of the collaboration of RL Turkey team.
A Rust client library for Airsim.
Reinforcement learning for an AirSim quadrotor implemented in Unity
Multi-Agent Deep Recurrent Q-Learning with Bayesian epsilon-greedy on AirSim simulator
Open source simulator for autonomous vehicles built on Unreal Engine / Unity, from Microsoft AI & Research
A System to allow integration of unsupported Radio Controllers with Microsoft AirSim Simulator for Unreal Engine 4 without any coding required, as well as provide general APIs for handling Real Drones.
This repository, "Autonomous Driving System On Various Platforms", details the exploration and implementation of autonomous driving systems across platforms such as AirSim, Android, Raspberry Pi 4, and Nvidia's Jetson Nano, utilizing Lidar, image processing, and machine learning technologies.
Drone navigation and obstacle avoidance by deep reinforcement learning and transfer learning (IN PROCESS)
These are two Interfaces to fly drones in AirSim simulator.
Optimal drone aeronautical route calculation for making emergency delivery system using drones.
Deep Q-learning in AirSim quadrotor environment.
A 3d cloud mesh is generated from a, set of photos taken from a drone, in airsim environment.
Based on the data generated from FRCNN test, this program shows how the drone will fly according to it.
Entrenament d’un model de IA en un entorn virtual per a la seva aplicació en la extinció d’incendis forestals: Prova de concepte - Universitat Oberta de Catalunya - Treball Final de Grau
Python scripts that flies a drone in AirSim environment and generates data.
Modified ROS packages to integrate Microsoft AirSim, a high-fidelity simulator for autonomous vehicles, with Autoware, an open-source self-driving car platform.
DROPEX (Disaster Response Operation and Probing with EXpert Drones) Simualtion
"Create a virtual environment for agriculture UAVs with AirSim in Unreal Engine 4.27, enabling realistic simulation and analysis of crop management techniques in a digital context."