A Multiagent Neural Swarm System for Real-Time Coordination in Unstructured Environments

Real-time coordination of agents is notably complex in dynamic, partially observable, and communication-constrained environments. Our current work presents the Multiagent Neural Swarm System (MANSS), which builds upon previous research and implements agents with adaptive and scalable deep reinforcement learning, decentralized neural networks, and swarm intelligence. Our approach considers neural communication protocols and embedded systems so that machines can make decisions independently and systems can attain group decisions autonomously. In simulation exercises with dense obstacles and uncertainties, MANSS outperformed traditional swarm algorithms in adaptability, failure tolerance, and time needed to complete given tasks. These results highlight the system's potential for real-world applications such as autonomous search and rescue missions, unmanned vehicle patrols, and environmental monitoring.