Static Attitude Determination Using Convolutional Neural Networks

The need to estimate the orientation between frames of reference is crucial in spacecraft navigation. Robust algorithms for this type of problem have been built by following algebraic approaches, but data-driven solutions are becoming more appealing due to their stochastic nature. Hence, an approach based on convolutional neural networks in order to deal with measurement uncertainty in static attitude determination problems is proposed in this paper. PointNet models were trained with different datasets containing different numbers of observation vectors that were used to build attitude profile matrices, which were the inputs of the system. The uncertainty of measurements in the test scenarios was taken into consideration when choosing the best model. The proposed model, which used convolutional neural networks, proved to be less sensitive to higher noise than traditional algorithms, such as singular value decomposition (SVD), the q-method, the quaternion estimator (QUEST), and the second estimator of the optimal quaternion (ESOQ2).

Geomagnetic orbit determination: EKF or UKF?

Purpose Autonomous orbit determination using geomagnetic measurements is an important backup technique for safe spacecraft navigation with a mere magnetometer. The geomagnetic model is used for the state estimation of orbit elements, but this model is highly nonlinear. Therefore, many efforts have been paid to developing nonlinear filters based on extended Kalman filter (EKF) and unscented Kalman filter (UKF). This paper aims to analyze whether to use UKF or EKF in solving the geomagnetic orbit determination problem and try to give a general conclusion. Design/methodology/approach This paper revisits the problem and from both the theoretical and engineering results, the authors show that the EKF and UKF show identical estimation performances in the presence of nonlinearity in the geomagnetic model. Findings While EKF consumes less computational time, the UKF is computationally inefficient but owns better accuracy for most nonlinear models. It is also noted that some other navigation techniques are also very similar with the geomagnetic orbit determination. Practical implications The intrinsic reason of such equivalence is because of the orthogonality of the spherical harmonics which has not been discovered in previous studies. Thus, the applicability of the presented findings are not limited only to the major problem in this paper but can be extended to all those schemes with spherical harmonic models. Originality/value The results of this paper provide a fact that there is no need to choose UKF as a preferred candidate in orbit determination. As UKF achieves almost the same accuracy as that of EKF, its loss in computational efficiency will be a significant obstacle in real-time implementation.

Autonomy for Space Robots: Past, Present, and Future

Purpose of Review The purpose of this review is to highlight space autonomy advances across mission phases, capture the anticipated need for autonomy and associated rationale, assess state of the practice, and share thoughts for future advancements that could lead to a new frontier in space exploration. Recent Findings Over the past two decades, several autonomous functions and system-level capabilities have been demonstrated and used in spacecraft operations. In spite of that, spacecraft today remain largely reliant on ground in the loop to assess situations and plan next actions, using pre-scripted command sequences. Advances have been made across mission phases including spacecraft navigation; proximity operations; entry, descent, and landing; surface mobility and manipulation; and data handling. But past successful practices may not be sustainable for future exploration. The ability of ground operators to predict the outcome of their plans seriously diminishes when platforms physically interact with planetary bodies, as has been experienced in two decades of Mars surface operations. This results from uncertainties that arise due to limited knowledge, complex physical interaction with the environment, and limitations of associated models. Summary Robotics and autonomy are synergistic, wherein robotics provides flexibility, autonomy exercises it to more effectively and robustly explore unknown worlds. Such capabilities can be substantially advanced by leveraging the rapid growth in SmallSats, the relative accessibility of near-Earth objects, and the recent increase in launch opportunities.

Unscented Kalman Filter and Control in TSE(3) with Applications to Spacecraft Dynamics

This paper presents a novel rigid-body spacecraft navigation and control architecture within the framework of special Euclidean group SE(3) and its tangent bundle TSE(3) while considering stochastic processes in the system. The proposed framework combines the orbit-attitude motions of the spacecraft into a single, compact set. The stochastic state filter is designed based on the unscented Kalman filter which uses a special retraction function to encode the sigma points onto the manifold. The navigation system is then integrated to an almost globally asymptotically stabilizing Morse-Lyapunov-based control system with backstepping. Numerical simulations are conducted to demonstrate the effectiveness of the proposed navigation filter for the full state estimation. In addition, the navigation and control system is tested in the nonlinear gravity field of a small celestial body with an irregular shape. In particular, the performance of the closed-loop system is studied in a tracking problem of spacecraft motion near the asteroid Bennu based on the OSIRIS-REx's mission data.

Computer Modelling of Autonomous Satellite Navigation Characteristics on Geostationary Orbit

Satellite systems are a fast-developing and broad field of study. The use of global navigation satellite systems for relatively autonomous spacecraft navigation holds a lot of interest for researchers. It is extremely expensive to research space applications as live experiments. Therefore, computer modelling comes in handy when there is a need to analyze important factors in space environment. The chapter describes the radionavigation field model that uses the off-nadir satellites. This model allows estimation of the availability and accuracy characteristics of autonomous satellite navigation in space up to the geostationary orbit in order to provide the necessary research data.