Deep Reinforcement Learning Framework for Coordinated Multi-Robot Flocking
Leveraging advanced deep reinforcement learning (DRL) methodologies, this technology combines a symmetric self-play mechanisms with sophisticated communication refinement and enhancement techniques. By training flocking robots alongside learnable adversarial interferers, the system fosters resilient flocking strategies capable of withstanding unpredictable and hostile conditions.
Multi-robot flocking systems' robustness is bad, adaptability is week, and communication efficiency is low in complex and dynamic environments. Traditional control system has high risk and high cost.
Urban Drone Delivery: Enhancing the safety and efficiency of drone swarms for package delivery in densely populated urban areas.
Logistics and Supply Chain Management: Streamlining warehouse operations and automated inventory management through robust multi-robot systems.
Search and Rescue Operations: Providing resilient and adaptable robotic teams capable of navigating and operating in disaster-stricken or hazardous environments.
Mobile Surveillance and Security: Deploying coordinated robot swarms for comprehensive monitoring and security tasks in dynamic and obstacle-rich settings.
