Integration of Onboard Autonomous Guidance and Navigation Algorithms for Interplanetary Cruise
Creators: name: Andreis, Eleonora
name: Morelli, Andrea Carlo
name: Panicucci, Paolo
name: Morselli, Alessandro
name: Topputo, Francesco
Publisher: name: Zenodo
Year: 2023
Subjects: CubeSats
Autonomous Navigation
Convex Optimization
Autonomous GNC
Deep Space

description: The present work, framed within the ERC-funded project EXTREMA (Engineering Extremely Rare Events in Astrodynamics for Deep-Space Mission in Autonomy), proposes an integrated autonomous guidance and navigation algorithm for interplanetary cruises that can be adopted onboard CubeSats, building the closed-loop GNC framework. For the navigation unit, autonomous onboard optical navigation algorithms are selected as cheap and fully ground-independent solutions, which enable deep-space CubeSats to determine their state by observing the unresolved celestial bodies motion on images taken by optical sensors. Since the celestial bodies are unresolved, the only information to estimate the probe state is the LoS directions. Moreover, since the CubeSat application is envisaged, the optical sensor has limited detection capabilities and only planets can be observed. An optical non-dimensional extended Kalman filter (EKF) based on the celestial triangulation approach is exploited to estimate the probe state. The planet information to correct the probe state estimation is extracted from the image generated with the DART LAB sky-field simulator by determining the probe attitude and identifying planets among the remaining bright dots. Additionally, since the light-time and light-aberration effects become relevant in deep space, their influence is included in the sky simulator, and their corrections are implemented in the filter. For the guidance unit, a convex optimization-based guidance algorithm using a Hermite--Simpson discretization is adopted as it allows to repeatedly recompute feasible trajectories onboard with limited computational resources, taking as input the navigation state estimation. <br> The guidance algorithm consists of three steps: first, a problem is solved which computes the optimal trajectory from the current estimated probe state to the target body. This considers a fixed time of flight optimization. Yet, if the probe state has diverged significantly with respect to the planned trajectory, no feasible solutions can be found. Thus, the guidance algorithm will autonomously solve a free-final-time problem instead. In the second step, the solution found in the first step is refined for the next duty cycle. In this phase, the duty cycle constraints are imposed. Finally, the third step transforms the guidance law, i.e., thrust magnitude and direction, found by the convex optimization algorithm in single arbitrary-order polynomials for each thrust arc. In the employed GNC strategy, the probe performs an asynchronous celestial triangulation of the two planets due to the limited FoV of the miniaturized camera. Thus, it tracks the first planet of the pair for 6 hours with an acquisition frequency of 2.5 mHz, then performs a slew maneuver to point to the second planet and acquires the information of this latter for other 6 hours. At the end of the tracking procedure, when the accuracy of the state estimation is the highest, the optimal trajectory is recomputed considering as initial state the one estimated by the navigation filter. The dynamics is then propagated using the updated guidance law set by the guidance algorithm for 7 days. Figures 3 and 4 show the duty cycle and trajectory leg analyzed, respectively.<br> <br> Figure 5 shows the position and velocity error profiles and covariance bounds in the J2000 ecliptic reference frame over the studied trajectory leg. For clarity, only one sample of the 50 Monte Carlo runs is represented. At the end of the trajectory leg, the filter estimates the spacecraft position and velocity with a 3σ accuracy of about 2300 km and 0.45 m/s, respectively. Figure 6 shows the thrust arcs commanded by the guidance algorithm over the analyzed trajectory leg and recomputed at the end of each coasting arc. Moreover, an additional analysis is performed to assess the robustness of the guidance algorithm when very large perturbations are considered at the beginning of the interplanetary transfer. The algorithm is able to recompute optimal trajectories even with perturbations on the spacecraft position in the order of 106 km (see Fig. 7). The performed preliminary numerical simulations show that the integrated guidance and navigation algorithms can pilot the spacecraft to the target planet.<br> <br> Future works will investigate the possibility of performing close-loop GNC simulations without undergoing predefined duty cycles, but by enforcing conditions to automatically move from the coasting arc to the commanding one and vice versa. Eventually, the integrated algorithms will be tested through hardware-in-the-loop simulations to validate their applicability to the Cubesat scenario.