Flocking Control

Flocking control research focuses on designing algorithms that enable groups of robots or agents to move collectively and achieve desired formations, often inspired by natural flocking behaviors. Current research emphasizes developing robust and efficient control strategies using various approaches, including Gibbs Random Fields, Markov Random Fields, and graph neural networks, often incorporating predictive control and reinforcement learning techniques to handle dynamic environments and optimize performance. This field is significant for its potential applications in areas such as autonomous drone swarms, robotics, and traffic management, offering improvements in scalability, efficiency, and adaptability for complex multi-agent systems.