In this work, we address the design of tightly integrated control, estimation, and allocation algorithms allowing a group of robots to move collectively. For doing so, we leverage a modular framework that allows us to define precisely the needed functional components and thus consider and compare multiple algorithmic solutions for the same module. We demonstrate the effectiveness of such a framework through multiple spatial coordination challenges carried out both in simulation and reality and leveraging different distributed control laws (graph-based and behavior-based controllers). Moreover, we investigate the impact of different localization and communication constraints as well as that of real-time switching of control laws on selected coordination metrics. Finally, we also introduce additional algorithmic components for demonstrating further the modularity of the framework. We find that defining the modularity based on functionality is a very effective way to enable algorithm benchmarking and discover possible improvements of the overall software stack while at the same time being agnostic to the underlying hardware and middleware resources. This is an especially welcome feature in case of severely resource-constrained multi-robot systems. Moreover, an important benefit of such design process is that the resulting distributed control algorithms are very robust to the considered noise sources and amplitudes as well as to the diverse types of challenges considered.