The Multi-Agent Autonomous Waste Collection System is a research-driven platform that employs multi-agent reinforcement learning to train individual waste-collecting robots to collaborate on route planning. Agents learn to avoid redundant coverage, minimize travel distance, and respond to dynamic waste generation patterns. Built in Python, the system integrates a simulation environment for testing and refining policies before real-world deployment. Users can configure map layouts, waste drop-off points, agent sensors, and reward structures to tailor behavior to specific urban areas or operational constraints.

Analog AI is a leading provider of edge computing solutions designed to leverage AI technology to connect people, places, and things. The product offers state-of-the-art artificial intelligence capabilities, ensuring efficient, real-time processing at the network edge. This significantly improves performance and reduces latency, making it suitable for various applications, from smart cities to industrial automation.

Currux Vision integrates advanced AI technologies to create autonomous systems for monitoring and optimizing urban infrastructure. Their solutions encompass traffic management, safety predictions, and anomaly detection, providing real-time analytics and actionable insights. The platform supports infrastructure developers and government agencies in ensuring safety and efficiency across varied environments.