arXiv:2611.07565v1 Announce Type: new Abstract: Here we introduce dynamicalnodes, a framework for rapid iterative prototyping of control and estimation algorithms within the Robot Operating System, or ROS. The framework leverages the theory of discrete-time dynamical systems to encode each component of a feedback system (e.g. reference generator, controller, plant, observer) as a pair of functions, denoted $(f,\,h) $, where $f$ is an evolution function and $h$ is an output function. With appropriate choice of domain and codomain, the $ (f,\,h) $ pairs can be composed to form feedback systems of arbitrary complexity. Moving from theory to practice, the user implements each $(f,\,h)$ pair in Python and encapsulates them in objects that facilitate composition and simulation. ROS executables are generated from these objects via rclpy, where relevant metadata such as network topology and publishing frequency are written automatically. The final result is a collection of rclpy files from which validated feedback systems can be spun up as live ROS graphs. We discuss the relevant theory behind the framework and then demonstrate the prototyping workflow applied to a canonical controls problem, a car with cruise control.