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.

The OpenCV AI Kit (OAK) is an innovative platform designed for spatial AI applications. It incorporates advanced features such as real-time object detection, depth sensing, and visual tracking, allowing AI models to better understand and interact with their environments. This hardware-accelerated solution includes a powerful camera system that supports machine learning capabilities, enabling a wide range of applications from robotics to smart surveillance and beyond.

The V5 Vision Sensor is a state-of-the-art tool designed for robotics applications, capable of detecting up to seven individual colors simultaneously. It features advanced algorithms for analyzing objects, which empowers robots to execute complex tracking and path planning tasks. With built-in Wi-Fi, the sensor seamlessly integrates with smartphones and tablets, allowing users to configure color signatures and analyze live video feeds. This technology opens up new possibilities for educational settings and competitions, making robotics more interactive and engaging.