This paper presents a Virtual Leader-Based distributed model predictive control (DMPC) framework enhanced with sequential quadratic programming (SQP) for the coordinated navigation of omnidirectional mobile robot formations in static obstacle environments with measurement noise. The proposed approach enables a team of robots arranged in a V-shaped formation to maintain their relative positions while tracking a leader robot’s trajectory. The DMPC scheme integrates unified cost functions that balance formation maintenance, trajectory tracking, control effort, and obstacle avoidance. Static obstacles are represented on a grid map, and the control algorithm incorporates real-time obstacle filtering to ensure collision-free motion. Measurement noise is considered to improve robustness in state estimation and control. Simulation results demonstrate that the method effectively preserves the formation shape and avoids collisions with static obstacles under noisy conditions. This work contributes a scalable and robust control strategy for multi-robot systems operating in realistic environments, providing a foundation for future extensions to dynamic obstacle scenarios.