VMAS is a vectorized differentiable simulator designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modular interface.

SocialGym 2: A lightweight benchmark and simulator for multi-robot social navigation using ROS and the OpenAI gym.

[CoRL 2022] Multi Robot Scene Completion

Implementation of Consensus Based Bundle Algorithm (CBBA) with python

ROS Package for Simultaneous Exploration and Localization for Multi-Robot Applications

A research toolkit for Swarm Robotics.

Software codes for running the Game-theoretic Utility Tree (GUT) algorithm for the multi-robot Pursuit-Evasion problem in the Robotarium's simulator-hardware multi-robot testbed.

DGORL: Distributed Graph Optimization based Relative Localization of Multi-Robot Systems

Rule-Based Lloyd (RBL) Multi-robot motion planning and control

CQLite: Coverage-biased Q-Learning Lite for Efficient Multi-Robot Exploration

Rescue Virtual Robot Project with ROS2 Packages

"Game-theoretic utility tree for multi-robot cooperative pursuit strategy" Paper with Code for 2022 the 54th international symposium on robotics (ISR europe). IEEE. Furthermore, include the codes of Explore Domain implementing in Robotarium.

Swarm Formation from Hand-Drawn Patterns using Signed Distance Fields

Hierarchical Gaussian Processes based Multi-Robot Relative Localization

System setup for multi robot navigation using tb2. The localization algorithm can choose AMCL or EKF.