This course presents the key concepts of the integrated Guidance, Navigation, and Control systems of autonomous vehicles and builds their most common algorithms. It defines the objectives of each GNC component and the combined closed-loop system. First, it develops a linear controller capable of vehicle stabilization and reference-following. The feedback design utilizes the concept of a noise-additive sensor model that is represented by a Gaussian probability density function. The course teaches how to design a calibration experiment that identifies a sensor. The multiplexing of qualitatively different measurements is introduced using the Bayesian inference concept. The propagation of Gaussian noise through the linear and nonlinear systems develops the prediction-correction mechanism of the linear and extended Kalman filters and builds the path to modern unscented and particle filters. The path-planning considers a trajectory as a combination of the path and the velocity along the path. The trajectory generation assumes the partially known environment with obstacles.

The laboratory work utilizes general-purpose programming (Python/MatLab) and intermediate techniques specific to robotics, e.g., objects, multithreading, event-based programming, cyber-physical interface, real-time execution, etc. Students successfully completing the class will have an understanding and skills of onboard software design typical of real-life robotic applications. Since the basic programming review is accelerated, to succeed in the course, students should have previously learned a procedural programming language.