Mobile manipulators, which integrate a robotic arm on a mobile base, are increasingly being explored and deployed in sectors such as healthcare, logistics, and aerospace. While motion planning for these systems has been studied in single-agent scenarios, the use of multiple robots to enhance efficiency and accelerate task completion in multi-agent settings remains largely unexplored, particularly in real-world environments. Extending motion planning to multi-mobile manipulators introduces challenges in real-time performance, collision avoidance, and coordination. To address these, this thesis proposes a decentralized Model Predictive Control (MPC) framework with a double integrator as dynamic model, denoted as MPC-d, tailored for multi-mobile manipulators operating in shared workspaces. It integrates optimization-based planning with robust state estimation, ensuring effective collision avoidance. Furthermore, a prioritized heuristic is introduced, leveraging the prediction horizon of MPC to resolve potential livelocks. The framework is validated through simulations and real-world experiments. Simulations compare MPC-d with MPC using a triple-integrator model (MPC-t) and a state-of-the-art geometric planner, called Geometric Fabrics (GF). Results demonstrate that MPC-d achieves comparable task success rates and collision avoidance compared to GF in pick-and-place scenarios while requiring less computation time than MPC-t. Real-world experiments confirm the framework’s viability, showcasing effective collision avoidance, enhanced efficiency from the prioritized heuristic, and consistency with simulation outcomes. Although MPC-d incurs higher computational costs than reactive geometric methods, it provides reliable performance and motion prediction of other agents in multi-agent settings.