Analysis, design and implementation of cooperative control strategies for multi-robot systems under communication constraints is the topic of this thesis. Motivated by a rapidly growing number of applications with networked robots and other vehicles, fundamental limits on the achievable collaborative behavior are studied for large teams of autonomous agents. In particular, a problem is researched in detail in which the group of agents is supposed to agree on a common state without any centralized coordination. Due to the dynamics of the individual agents and their varying connectivity, this problemis an extension of the classical consensus problemin computer science. It captures a crucial component of many desirable features of multi-robot systems, such as formation, flocking, rendezvous, synchronizing and covering. Analytical bounds on the convergence rate to consensus are derived for several systemconfigurations. It is shown that static communication networks that exhibit particular symmetries yield slow convergence, if the connectivity of each agent does not scale with the total number of agents. On the other hand, some randomly varying networks allow fast convergence even if the connectivity is low. It is furthermore argued that if the data being exchanged between the agents are quantized, it may heavily degrade the performance. The extent to which certain quantization schemes are more suitable than others is quantified through relations between the number of agents and the required total network bit rate. The design of distributed coordination and estimation schemes based on the consensus algorithm is presented. A receding horizon coordination strategy utilizing subgradient optimization is developed. Robustness and implementation aspects are discussed. A new collaborative estimation method is also proposed. The implementation of multi-robot control systems is difficult due to the high systemcomplexity. In the final part of this thesis, a hierarchical control architecture appropriate for a class of ...