Michael Nayak

Attitude Determination and Control System Design for a 6U Cube Sat for Proximity Operations and Rendezvous

The work presented in this paper focuses on the design of an attitude determination and control subsystem (ADCS) for a proximity operation and imaging satellite mission. The ARAPAIMA (Application for Resident Space Object Proximity Analysis and IMAging) mission is carried out by a 6 U CubeSat class satellite equipped with a warm gas propulsion system. The propulsion system comprises an orbital maneuvering thruster which produces 100 mN and a set of 16 reaction control system (RCS) thrusters, of 25 mN each, installed in pairs that generate torques about each of the satellite body axes. The thrust of the RCS thrusters can be modulated over the entire range in steps of 1% due to the rapid solenoid valve actuation. The requirement of the control system is to provide pointing control accuracy of 1 arcmin at 3 σ in the desired imaging direction. The ADCS employs two control laws. One control law, for large angle maneuvers, implements eigenaxis maneuvering and the other, for accurate pointing, is implemented with PID controllers about each body axis. Simulations performed for tracking the resident space object flying in a circular orbit of 500 km altitude from a relative orbit of 250 m are used to test the performance of the ADCS. A comparison between a “traditional” ADCS system with reaction wheels and RCS thrusters for their off-loading, and a system with only RCS thrusters has been performed. The accuracy of the pointing is comparable, the pointing performance as well as the propellant usage for both options are further discussed in the paper. The integration of both controllers within the ADCS and the switching are also discussed.

Design of relative motion and attitude profiles for three-dimensional resident space object imaging with a laser rangefinder

This paper focuses on the aerospace application of a single beam laser rangefinder (LRF) for 3D imaging, shape detection, and reconstruction in the context of a space-based space situational awareness (SSA) mission scenario. The primary limitation to 3D imaging from LRF point clouds is the one-dimensional nature of the single beam measurements. A method that combines relative orbital motion and scanning attitude motion to generate point clouds has been developed and the design and characterization of multiple relative motion and attitude maneuver profiles are presented. The target resident space object (RSO) has the shape of a generic telecommunications satellite. The shape and attitude of the RSO are unknown to the chaser satellite however, it is assumed that the RSO is un-cooperative and has fixed inertial pointing. All sensors in the metrology chain are assumed ideal. A previous study by the authors used pure Keplerian motion to perform a similar 3D imaging mission at an asteroid. A new baseline for proximity operations maneuvers for LRF scanning, based on a waypoint adaptation of the Hill-Clohessy-Wiltshire (HCW) equations is examined. Propellant expenditure for each waypoint profile is discussed and combinations of relative motion and attitude maneuvers that minimize the propellant used to achieve a minimum required point cloud density are studied. Both LRF strike-point coverage and point cloud density are maximized; the capability for 3D shape registration and reconstruction from point clouds generated with a single beam LRF without catalog comparison is proven. Next, a method of using edge detection algorithms to process a point cloud into a 3D modeled image containing reconstructed shapes is presented. Weighted accuracy of edge reconstruction with respect to the true model is used to calculate a qualitative “metric” that evaluates effectiveness of coverage. Both edge recognition algorithms and the metric are independent of point cloud density, therefore they are utilized to compare the quality of point clouds generated by various attitude and waypoint command profiles. The RSO model incorporates diverse irregular protruding shapes, such as open sensor covers, instrument pods and solar arrays, to test the limits of the algorithms. This analysis is used to mathematically prove that point clouds generated by a single-beam LRF can achieve sufficient edge recognition accuracy for SSA applications, with meaningful shape information extractable even from sparse point clouds. For all command profiles, reconstruction of RSO shapes from the point clouds generated with the proposed method are compared to the truth model and conclusions are drawn regarding their fidelity.

Atmospheric Retrieval for Direct Imaging Spectroscopy of Gas Giants in Reflected Light. II. Orbital Phase and Planetary Radius

Future space-based telescopes, such as the Wide-Field Infrared Survey Telescope (WFIRST), will observe the reflected-light spectra of directly imaged extrasolar planets. Interpretation of such data presents a number of novel challenges, including accounting for unknown planet radius and uncertain stellar illumination phase angle. Here we report on our continued development of Markov Chain Monte Carlo retrieval methods for addressing these issues in the interpretation of such data. Specifically we explore how the unknown planet radius and potentially poorly known observer-planet-star phase angle impacts retrievals of parameters of interest such as atmospheric methane abundance, cloud properties and surface gravity. As expected, the uncertainty in retrieved values is a strong function of signal-to-noise ratio (SNR) of the observed spectra, particularly for low metallicity atmospheres, which lack deep absorption signatures. Meaningful results may only be possible above certain SNR thresholds; for cases across a metallicity range of 1-50 times solar, we find that only an SNR of 20 systematically reproduces close to the correct methane abundance at all phase angles. However, even in cases where the phase angle is poorly known we find that the planet radius can be constrained to within a factor of two. We find that uncertainty in planet radius decreases at phase angles past quadrature, as the highly forward scattering nature of the atmosphere at these geometries limits the possible volume of phase space that relevant parameters can occupy. Finally, we present an estimation of possible improvement that can result from combining retrievals against observations at multiple phase angles.

Real-time attitude commanding to detect coverage gaps and generate high resolution point clouds for RSO shape characterization with a laser rangefinder

This paper expands on previous studies by the authors into 3D imaging with a single-beam laser rangefinder (LRF) by implementing real-time attitude maneuvers of a chaser satellite flying in relative orbit around a resident space object (RSO). Point clouds generated with an LRF are much sparser than those generated with an imaging LIDAR, making it difficult to autonomously distinguish between gaps in coverage and truly empty space. Furthermore, if both the attitude and the shape of the target RSO are unknown, it is particularly difficult to register a collection of LRF strike points together and detect gaps in strike point coverage in realtime. This paper presents the incorporation of a narrow field of-view (NFOV) camera that detects the strike point on the RSO and supplements LRF distance measurements with image data. This data is used to generate attitude command profiles that efficiently fill LRF coverage gaps and generate high density point clouds, thus maximizing coverage of an unknown RSO. Results obtained so far point the way to a real-time implementation of the algorithm. A method to detect and close gaps in LRF strike point coverage is presented first. Coverage gap detection is achieved using Voronoi diagrams, where Voronoi cells are centered at the LRF strike points. A three-part algorithm is used that 1) creates a 3D panoramic map from “stitched” NFOV camera images; 2) correlates the areas of sparse LRF coverage to the map; and 3) generates attitude commands to close the coverage gaps. The map provides a consistent and reliable method to register positions of strike points relative to each other and to the NFOV image of the RSO without a priori knowledge of the RSO attitude. Using this algorithm, gaps and sparse areas in LRF coverage are covered with strike points, allowing for the generation of a higher-resolution point cloud than that obtained with preprogrammed attitude profiles. Attitude maneuvers can now be designed on-line in real-time such that they satisfy the constraints of the chaser spacecraft attitude determination and control system. Finally, the effectiveness of the camera-aided generation of attitude profiles is analyzed by using a weighted edge reconstruction metric, and comparing results to those generated with pre-programmed attitude maneuvers. The effect of on-line maneuver generation on the overall decrease of time and propellant expenditure to generate an adequate point cloud is also discussed. The analysis bears particular relevance to low-budget, nano-satellite demonstration missions for space-based space situational awareness (SSA).

Nanosatellite maneuver planning for point cloud generation with a rangefinder

This paper discusses the application of a single beam laser rangefinder (LRF) to point cloud generation, shape detection, and shape reconstruction for a space-based space situational awareness (SSA) mission. The LRF is part of the payload of a chaser satellite tasked to image a resident space object (RSO). The one-dimensional (1D) nature of LRF returns significantly increases the complexity of the imaging task. To maximize coverage, a method to autonomously detect and fill gaps in sparse point cloud coverage using a narrow field of view (NFOV) camera in conjunction with the LRF is presented. First, relative orbital motion and scanning attitude motion are combined to generate a baseline 3D point cloud of the RSO. The effectiveness of pregenerated command profiles is analyzed by using a weighted edge reconstruction metric that scores how well a point cloud characterizes RSO shape. The design and characterization of multiple relative motion and attitude maneuver profiles, as well as the time and propellant cost of each profile, are presented with the assumption that the entire metrology chain is error free. Next, a three-part algorithm is used that creates a 3D panoramic map from stitched NFOV camera images, correlates the areas of sparse LRF coverage to the map, and generates attitude commands to close the coverage. This provides a consistent and reliable method to register positions of strike points relative to each other and to the NFOV image of the RSO with a priori knowledge of the RSO attitude. Gaps and sparse areas in LRF coverage are covered with strike points; the result is a point cloud of significantly higher resolution than that obtained with preprogrammed attitude profiles. The analysis bears particular relevance to power-constrained nanosatellite missions for space-based SSA for whom a multibeam LRF payload is not feasible. Maneuvers can now be designed on-line in real time; results presented validate the utility of a single-beam LRF as a tool for 3D imaging of RSO shapes.

Small-Scale Payload Operations Simulator for Proximity Operations

The process and studies that make-up the design, construction, and testing of a desktop size spacecraft simulator are detailed. The spacecraft simulator is of the essence to validate and verify the requirements and science algorithms of the Embry-Riddle Aeronautical University (ERAU) cubesat, Application for Resident Space Object (RSO) Autonomous Proximity Analysis and Imaging (ARAPAIMA). The simulator is designed with simple commercial-off-the-shelf (COTS) hardware integrated in a way that allows for accuracy and feedback control in a small form factor.