Design and optimisation of guidance and control algorithms and development of a 6-DOF simulator for the autonomous lunar flying drone LuNaDrone

The launch of NASA’s Artemis Program stimulated the development of several technologies and mission projects concerning lunar exploration and its colonisation, supported by the numerous collaboration agreements signed by NASA with countries all around the world, including Italy. In this context, in 2020 from an idea of Gen. Roberto Vittori the LuNaDrone mission concept was launched at Politecnico di Torino as an academic project and then, in December 2022, Evolunar was founded to pursue the project and build the prototype of an autonomously guided lunar nano drone capable of flying above the lunar surface. This is what lies behind the subject presented in the Thesis. The work aims to design a guidance and control algorithm and to develop a six-degree-of-freedom simulator capable of simulating the dynamics of the drone and the entire planned mission. In the introductory part, a brief summary is presented regarding the Artemis Programme and about the emergence of new frontiers with the development of the Lunar Economy. This is followed by an in-depth analysis of lunar lava tubes, the target of Evolunar’s mission considered in this Thesis, and an object of study of considerable importance for the scientific community. Next, the LuNaDrone project is examined in depth with regard to the mission planning and flight trajectories, and with particular attention to the technical aspects, analysing the various systems of which the drone is composed. Then, the simulator is presented in all its parts, with a greater focus on the development of the entire system architecture and the design of the guidance and control algorithm. Furthermore, a strategy for optimizing the simulation parameters and tuning the controller parameters is proposed. The approach relies on a statistical analysis of data obtained from numerous simulations, in order to provide the better conditions for the implementation of an optimisation algorithm. Finally, the algorithm's response is tested and analysed in various simulation scenarios and with different boundary conditions, summarising the results obtained and highlighting eventual conclusions and problems that have arisen from the simulations.