The Multi Agent Robotic System project offers a modular Python-based platform for developing, simulating, and deploying cooperative robotic teams. At its core, it implements decentralized control strategies, enabling robots to share state information and collaboratively allocate tasks without a central coordinator. The system includes built-in modules for path planning, collision avoidance, environment mapping, and dynamic task scheduling. Developers can integrate new algorithms by extending provided interfaces, adjust communication protocols via configuration files, and visualize robot interactions in simulated environments. Compatible with ROS, it supports seamless transitions from simulation to real-world hardware deployments. This framework accelerates research by providing reusable components for swarm behavior, collaborative exploration, and warehouse automation experiments.

RL Collision Avoidance provides a complete pipeline for developing, training, and deploying multi-robot collision avoidance policies. It offers a set of Gym-compatible simulation scenarios where agents learn collision-free navigation through reinforcement learning algorithms. Users can customize environment parameters, leverage GPU acceleration for faster training, and export learned policies. The framework also integrates with ROS for real-world testing, supports pre-trained models for immediate evaluation, and features tools for visualizing agent trajectories and performance metrics.

CASA is designed as a modular, plug-and-play autonomy framework built on the Robot Operating System (ROS) ecosystem. It features a decentralized architecture where each robot runs local planners and behavior tree nodes, publishing to a shared blackboard for world-state updates. Task allocation is handled via auction-based algorithms that assign missions based on robot capabilities and availability. The communication layer uses standard ROS messages over multirobot networks to synchronize agents. Developers can customize mission parameters, integrate sensor drivers, and extend behavior libraries. CASA supports scenario simulation, real-time monitoring, and logging tools. Its extensible design allows research teams to experiment with novel coordination algorithms and deploy seamlessly on diverse robotic platforms, from unmanned ground vehicles to aerial drones.

The F1Tenth Two-Agent Simulator is a specialized simulation framework built on ROS and Gazebo to emulate two 1/10th scale autonomous vehicles racing or cooperating on custom tracks. It supports realistic tire-model physics, sensor emulation, collision detection, and data logging. Users can plug in their own planning and control algorithms, adjust agent parameters, and run head-to-head scenarios to evaluate performance, safety, and coordination strategies under controlled conditions.