Open-source alternatives to ROSIntegration

Computational framework for reinforcement learning in traffic control

A ROS/ROS2 Multi-robot Simulator for Autonomous Vehicles

The source code for the Menge crowd simulation framework

AirSim is a high-fidelity simulation platform designed for the development and testing of autonomous vehicles. Built as a plugin for game engines, it provides a physics-based environment that models vehicle dynamics and sensor data, serving as a foundation for robotics research, computer vision training, and reinforcement learning. The platform distinguishes itself through its support for hardware-in-the-loop and software-in-the-loop testing, allowing developers to validate control logic and firmware against real-world signals or concurrent processes. It offers extensive programmatic control

Trick Simulation Environment. Trick provides a common set of simulation capabilities and utilities to build simulations automatically.

OpenAI Gym environments for an open-source quadruped robot (SpotMicro)

OpenSceneGraph git repository

A generic interface for the environmental perception of automated driving functions in virtual scenarios.

Drake is a robotics simulation framework and control system modeling tool used for designing, simulating, and verifying the dynamics of complex robotic systems. It functions as a multibody dynamics simulator and a mathematical optimization library, providing a suite of algorithms for trajectory optimization and the simulation of articulated robots. The framework is distinguished by its block-diagram system for composing dynamical subsystems and its ability to formulate and solve diverse mathematical programs, including linear, quadratic, and nonconvex nonlinear problems. It supports specializ

High-performance C++ multibody dynamics/physics library for simulating articulated biomechanical and mechanical systems like vehicles, robots, and the human skeleton.

The open source air traffic simulator

A generalized framework for prototyping full-stack cooperative driving automation applications under CARLA+SUMO.

This project is an autonomous vehicle simulator designed to validate self-driving logic and train deep learning algorithms within a virtual environment. It functions as a training platform for developing neural networks that control vehicle movement and steering based on visual sensor data. The simulator uses the Unity game engine to provide a physics-based world where autonomous driving algorithms can be tested on virtual road courses without the use of physical hardware. The system integrates a C# scripting backend with a physics engine for collision detection and vehicle dynamics. It util

Central repository for tools, tutorials, resources, and documentation for robotics simulation in Unity.

PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control

PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and RL

An Open Flexible Quadrotor Simulator

👉 CARLA resources such as tutorial, blog, code and etc https://github.com/carla-simulator/carla

Generate random crop fields for Ignition Gazebo

CARLA is an autonomous driving simulator and research environment designed for developing and validating self-driving software. It functions as an urban traffic simulator that generates realistic vehicle and pedestrian behavior and as a synthetic sensor data generator producing LiDAR, Radar, and camera data. The platform distinguishes itself through its deep integration with robotics frameworks, specifically providing native connectivity to ROS2 nodes for robotic control and data processing. It supports the training of driving models via imitation and reinforcement learning within a controlle

ROS bridge for CARLA Simulator

MIT Cheetah I Implementation

An OpenAI gym wrapper for CARLA simulator

Support for ROS-enabled RGBD data acquisition in Unreal Engine Projects