Speaker
Description
Future science and exploration missions will implement innovative mission concepts to embark on daring endeavors exploiting cooperating intelligent systems. Two future applications are the Orbit Debris Removal and the On-Orbit Servicing. The approach to an uncooperative object is challenging but it can enable the achievement of important mission objectives, such as restoring, refueling, repairing or removing a malfunctioning satellite. All these features are achieved at the cost of a significant increase in the on-board autonomy. Due to the relative distances involved, the agent composing the system needs to be able to rapidly and autonomously react to unforeseen events, such as collisions. To this end, it is critical that the chaser spacecraft is capable of planning, navigating and controlling itself in unknown or partially-known environment around a partially known uncooperative object, without ground-intervention. The research work presented here focuses on the development and testing of a full Guidance, Navigation & Control system aided by Artificial Intelligence techniques. The enhancement provided by Artificial Intelligence techniques allow the system to fly around objects whose dynamics is not fully determined, such as uncooperative debris, by incrementally learning its mathematical modeling. The AI-reconstructed dynamics is capable of detecting a non-nominal thrust or torque, which is not predictable beforehand. A number of methods are explored to recover the underlying dynamics by simply measuring relative state and processing it with an Artificial Neural Network. This dynamics is then used to plan control action and enhance the navigation and control synthesis. In particular, three methods for dynamics reconstruction are developed, together with their mathematical foundation. The three approaches integrate the Artificial Neural Network at different levels: from fully integrated, where the dynamics is completely encapsulated into an Artificial Neural Network, to partially integrated in which the network learns either the unknown dynamical accelerations or reconstructs the unknown parameters of the analytical expression. Such reconstruction scheme is used in two different planning and control algorithms: Neural-Artificial Potential Field method and Model-Based Reinforcement Learning. The former is a fast and light algorithm that easily handles collision avoidance but lacks of planning; the latter is able to generate plans and control the spacecraft based on the learnt dynamics. In order to reconstruct future plans of the inspected spacecraft, it is necessary to develop an AI-based routine coupling Long-Short Term Memory and Inverse Reinforcement Learning to predict the behavior of the external agent, being either in free-motion or controlled-motion. The algorithms, when compared to classical methods, showed superior performance and constant increase in relevant Guidance, Navigation & control metrics (navigation accuracy, maneuvers Δv, etc.). Finally, in order to increase the Technology Readiness Level of the algorithms, the work presents the Processor-In-the-Loop testing campaign executed with relevant hardware: a micro-controller unit and a single-board computer with similar computational power with respect to flight-hardware. An end-to-end autocoding procedure has been developed to transition from Model-In-the-Loop simulations to Processor-In-the-Loop validation. The tests were deemed successful by evaluating the execution times, resource utilization and achieved accuracy. The outcome of the Thesis is a complete framework to integrate different AI-based techniques to enhance existing, well-established, algorithms. The methodology described here can easily be extended to other mission scenarios, where the flexibility and adaptivity of the system is critical.
