Search Results for "airsim"

...It is designed for research involving coordinated aerial and ground autonomous systems. Vehicles, pedestrians, drones, weather, and sensor streams can coexist within the same simulated world. Users can access both CARLA and AirSim-style Python APIs to control different agents and environments. The platform supports synchronized cameras, lidar, navigation sensors, trajectory recording, and joint air-ground scenarios. ROS 2 examples connect simulated vehicles and drones to external robotics workflows and RViz. Prebuilt packages for Linux and Windows reduce setup effort for researchers studying embodied intelligence and autonomous systems.

...AirSim's development is oriented towards the goal of creating a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles. For this purpose, AirSim also exposes APIs to retrieve data and control vehicles in a platform independent way. AirSim is fully enabled for multiple vehicles. This capability allows you to create multiple vehicles easily and use APIs to control them.

The PromptCraft-Robotics repository serves as a community for people to test and share interesting prompting examples for large language models (LLMs) within the robotics domain. We also provide a sample robotics simulator (built on Microsoft AirSim) with ChatGPT integration for users to get started. We currently focus on OpenAI's ChatGPT, but we also welcome examples from other LLMs (for example open-sourced models or others with API access such as GPT-3 and Codex).

...Localization components include NDT and ICP approaches, with GPU acceleration available for selected algorithms. Simulation support integrates lidar, stereo cameras, PX4, Gazebo, and AirSim environments. Its modular C++ architecture allows individual autonomy components to be replaced or extended. The repository targets research rather than production aviation and reflects an older robotics stack centered on Ubuntu 18.04 and ROS Melodic.

That project aims at studying the impact of IT systems interactions on traveller demand and airline revenues. Passenger demand is generated (Monte Carlo) and injected into simulated CRS and airline IT systems. Differential analysis is then performed on various changes compared to a bottom line scenario.