: The aim of this thesis is to design a Cloud Robotics architecture for multi-robot systems in a manufacturing environment, and to develop Rosconnect, a ROS-based Cloud Robotics framework that enables multiple robots to offload computationally intensive tasks and share knowledge through a cloud server . While the increasing complexity of robotic algorithms demands more powerful onboard processors, using a Cloud Robotics framework can make it possible to have cost-efficient and light-weight robots by computation offloading. Also, a single robot still lacks intelligence because it only has access to the knowledge it has acquired during its operation time. As the emerging robotic tasks require the cooperation of robots, developing a mechanism for data exchange and knowledge sharing is vital. Moreover, a Cloud Robotics framework enables the user to manage all the connected robots via a cloud server, which yields benefits such as faster deployment of a robotic team for a production task and online management of robots in the production line. Regarding the fact that the task of mapping an environment is the most fundamental task for an autonomous mobile robot(AMR), and warehouse automation relies on multiple AMRs, we have chosen to implement a cooperative mapping algorithm using the proposed framework. We have simulated the cooperative mapping task using multiple AMRs and utilized our framework for knowledge sharing and computation offloading to build a map of the environment. The resulting global map demonstrates the capabilities of our architecture and framework. Keywords: Cloud Robotics, Cloud Manufacturing, Industry 4.0, Internet of Things, Cooperative Mapping