MultiVehicle Simulator (MVSim): lightweight dynamics simulator for multiagents and mobile robotics research

TL;DR

MVSim is a lightweight, GPU-accelerated simulator for multi-vehicle and mobile robotics research, offering realistic physics and sensor models for efficient development and testing of autonomous systems.

Contribution

Introduces MVSim, a physics-grounded, multi-agent simulator with GPU acceleration, customizable sensors, and ROS integration, optimized for mobile robotics and autonomous vehicle research.

Findings

Supports dozens of agents in simple scenarios

Provides accurate sensor simulation including depth and LiDAR

Enables efficient multi-vehicle dynamics research

Abstract

Development of applications related to closed-loop control requires either testing on the field or on a realistic simulator, with the latter being more convenient, inexpensive, safe, and leading to shorter development cycles. To address that need, the present work introduces MVSim, a simulator for multiple vehicles or robots capable of running dozens of agents in simple scenarios, or a handful of them in complex scenarios. MVSim employs realistic physics-grounded friction models for tire-ground interaction, and aims at accurate and GPU-accelerated simulation of most common modern sensors employed in mobile robotics and autonomous vehicle research, such as depth and RGB cameras, or 2D and 3D LiDAR scanners. All depth-related sensors are able to accurately measure distances to 3D models provided by the user to define custom world elements. Efficient simulation is achieved by means of focusing on ground vehicles, which allows the use of a simplified 2D physics engine for body collisions while solving wheel-ground interaction forces separately. The core parts of the system are written in C++ for maximum efficiency, while Python, ROS 1, and ROS 2 wrappers are also offered for easy integration into user systems. A custom publish/subscribe protocol based on ZeroMQ (ZMQ) is defined to allow for multiprocess applications to access or modify a running simulation. This simulator enables and makes easier to do research and development on vehicular dynamics, autonomous navigation algorithms, and simultaneous localization and mapping (SLAM) methods.