In this work, we consider a group of differential-wheeled robots endowed with noisy relative positioning capabilities. We develop a decentralized approach based on a receding horizon controller to generate, in real-time, trajectories that guarantee the convergence of our robots to a common location (i.e. rendezvous). Our receding horizon controller is tailored around two numerical optimization methods: the hybrid-state A* and trust-region algorithms. To validate both methods and test their robustness to computational delays, we perform exhaustive experiments on a team of four real mobile robots equipped with relative positioning hardware.