Connectivity Constraint

Connectivity constraint research focuses on optimizing systems where maintaining communication links between agents is crucial, addressing challenges in areas like multi-agent robotics and network design. Current efforts employ diverse approaches, including graph neural networks for efficient solutions to NP-hard problems like graph coloring and model predictive control (MPC) techniques, often incorporating mixed-integer programming, to ensure robust connectivity in dynamic environments. This research is significant for advancing the capabilities of multi-agent systems in various applications, from autonomous UAV coordination to distributed robotic control, by providing efficient and reliable methods for maintaining communication despite complex constraints.