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.

OpenMAS is designed to facilitate the development and evaluation of decentralized AI agents and multi-agent coordination strategies. It features a modular architecture that allows users to define custom agent behaviors, dynamic environment models, and inter-agent messaging protocols. The framework supports physics-based simulation, event-driven execution, and plugin integration for AI algorithms. Users can configure scenarios via YAML or Python, visualize agent interactions, and collect performance metrics through built-in analytics tools. OpenMAS streamlines prototyping in research areas such as swarm intelligence, cooperative robotics, and distributed decision-making.

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.

Flocking Multi-Agent offers a modular library for simulating autonomous agents exhibiting swarm intelligence. It encodes core steering behaviors—cohesion, separation and alignment—alongside obstacle avoidance and dynamic target pursuit. Using Python and Pygame for visualization, the framework allows adjustable parameters such as neighbor radius, maximum speed, and turning force. It supports extensibility through custom behavior functions and integration hooks for robotics or game engines. Ideal for experimentation in AI, robotics, game development, and academic research, it demonstrates how simple local rules lead to complex global formations.

Multi-Agent Drone Environment is a Python package offering a customizable multi-agent simulation for UAV swarms, built on OpenAI Gym and PyBullet. Users define multiple drone agents with kinematic and dynamic models to explore cooperative tasks such as formation flying, target tracking, and obstacle avoidance. The environment supports modular task configuration, realistic collision detection, and sensor emulation, while allowing custom reward functions and decentralized policies. Developers can integrate their own reinforcement learning algorithms, evaluate performance under varied scenarios, and visualize agent trajectories and metrics in real time. Its open-source design encourages community contributions, making it ideal for research, teaching, and prototyping advanced multi-agent control solutions.

NavGround Learning provides a comprehensive toolkit for developing and benchmarking reinforcement learning agents in navigation tasks. It supports multi-agent simulation, collision modeling, and customizable sensors and actuators. Users can select from predefined policy templates or implement custom architectures, train with state-of-the-art RL algorithms, and visualize performance metrics. Its integration with OpenAI Gym and Stable Baselines3 simplifies experiment management, while built-in logging and visualization tools allow in-depth analysis of agent behavior and training dynamics.