This article investigates the problem of controlling the speed of robots in collaborative workcells for automated manufacturing. The solution is tailored to robotic cells for cooperative assembly of aircraft fuselage panels, where only structural elements are present and robots and humans can share the same workspace, but no physical contact is allowed, unless it happens at zero robot speed. The proposed approach addresses the problem of satisfying the minimal set of requirements of an industrial human-robot collaboration (HRC) task: precision and reliability of human detection and tracking in the shared workspace; correct robot task execution with minimum cycle time while assuring safety for human operators. These requirements are often conflicting with each other. The former does not only concern with safety only but also with the need of avoiding unnecessary robot stops or slowdowns in case of false-positive human detection. The latter, according to the current regulations, concerns with the need of computing the minimum protective separation distance between the human operator and the robots by adjusting their speed when dangerous situations happen. This article proposes a novel fuzzy inference approach to control robot speed enforcing safety while maximizing the level of productivity of the robot minimizing cycle time as well. The approach is supported by a sensor fusion algorithm that merges the images acquired from different depth sensors with those obtained from a thermal camera, by using a machine learning approach. The methodology is experimentally validated in two experiments: the first one at a lab-scale and the second one performed on a full-scale robotic workcell for cooperative assembly of aeronautical structural parts.