Planning Guidance and Navigation for Autonomous and Distributed Aerospace Platforms

Many current and future aerial and space missions are based on the paradigm of distributing the tasks among several platforms to overcome the limits of the single vehicle. This thesis tackles navigation planning and guidance of both distributed spacecraft and cooperative UAVs. Cooperation among UAV platforms improves reliability and reconfigurability of the formation and allows to accomplish the mission in a reduced time. Not only does cooperation enhance reliability and overall mission time, but it enables mission and performance that would not be achievable in a single vehicle configuration. In this thesis the advantage of using cooperation to improve navigation performance is analysed, highlighting the potential of cooperative formations with respect to the single platform and the benefits related to having more than one platform to perform the mission. Specifically, two scenarios are taken into account. Navigation performance with cooperation among platforms is analysed either under GNSS nominal or non-nominal coverage. The latter can be also referred as GNSS challenging condition. In case of non-nominal GNSS coverage, the flight of UAV in the GNSS challenging area can be enabled only using one or more cooperative platforms, whose absolute position along with relative measurement is shared with the formation and used to improve the navigation performance of the vehicle under non-nominal GNSS coverage. A cooperative navigation filter is developed for this purpose and planning and guidance technique are developed for the cooperative platforms in order to guarantee satisfactory navigation performance for the vehicle in the GNSS challenging area. In addition, due to the heterogenous nature of the GNSS coverage in an urban scenario a task assignment and path planning technique has been developed for a swarm of UAV operating in a urban scenario, exploiting cooperation in the GNSS challenging areas, and allowing UAVs to act independently under nominal coverage, in order to optimize the available resources. When under nominal GNSS coverage, all the UAVs of the formation show satisfactory positioning performance, that could be improved by using differential or carrier phase differential GNSS. Nevertheless, integration of low cost IMUs, GNSS and magnetometers allows real time stabilization and flight control but may not be suitable for applications requiring fine sensor pointing. In these scenarios, cooperation is used to improve attitude accuracy using as additional measurement an Inertial- and Magnetometer- independent measurement, that is related to carrier phase differential GNSS and visual measurements. This concept extends the paradigm of multi-antenna GNSS attitude estimation to a distributed aircraft scenario. The independent measurement allows to have a fine pointing that is compliant with the requirements of mapping mission. The enhancement of attitude accuracy produces also improved performance in positioning estimation. As regards space, many new generation missions will rely on distributed platform, to optimize the reconfigurability, maintainability and mission performance of the systems. Satellite formation flying requires the knowledge of the relative navigation between the platforms in real time, with very high accuracy. This thesis uses GNSS relative navigation based on carrier phase double differences to estimate with high precision the 3D components of the distance of two Low Earth Orbit spacecraft with large baseline. In case of large baseline, the ionosphere delay could dramatically affect the correct estimation of carrier phase double difference ambiguity. In this thesis a new model for real time estimation of the ionospheric delay is proposed, which accounts for the spatial difference of the ionosphere. This model is tested in an EKF that uses real flight data coming from the Gravity Recovery And Climate Experiment mission, and its performance is compared with the classic model for ionosphere estimation