Angles-Only Autonomous Rendezvous Navigation to a Space Resident Object

Transcription

1 aa.stanford.edu stanford.edu Angles-Only Autonomous Rendezvous Navigation to a Space Resident Object Josh Sullivan PhD. Candidate, Space Rendezvous Laboratory PI: Dr. Simone D Amico Slide 1

2 Contents Research motivation Problem description Previous work on the topic Research goals Technical details Observability assessment Navigation filter design Conclusions and way forward Tango S/ C Vega First image taken during ARGON April 23, 2012 (DLR) Slide Slide 2

3 Motivation: Distributed Space Systems Navigation approach which only makes use of camera-based measurements. Is an enabling technology for many distributed space systems applications. Autonomous Rendezvous Novel Science Missions General Space Situational Awareness Slide Slide 3

4 Motivation: Metrology Systems Vision- Based LIDAR Laser- Ranging GNSS Radio- Frequency Slide Slide 4

5 Problem Description Reconstruct relative translational motion state from a set of camera-based measurements. No range information available. Measurements are two angles from single sensor. Devise guidance and navigation architecture for rendezvous that considers: Choice of state representation Observability constraints Navigation conditions Orbit geometry Radial, R Chief Cross-track, N Deputy/Target Along-track, T Slide Slide 5

6 Existing Rendezvous Techniques Radial, o r Deputy Δv Δv Δv Δv Chief V-Bar Along-track, o t Deputy Radial, o r R-Bar Δv Δv Δv Along-track, o t Chief Slide Slide 6

7 Existing Rendezvous Techniques Uses size and orientation of relative eccentricity and inclination vectors. E/I-Vector Separation Design of passively safe relative trajectories based on minimum R-N separation. SAFE UNSAFE Slide Slide 7

8 Existing Work on Angles-Only Navigation Theory Development Lacking a unified connection between state, observability constraints, measurement model formulation, and navigation conditions General result: Angles-only navigation problem is inherently unobservable as currently formulated. Proposed remedies: Orbit/attitude maneuvers to change line-of-sight trend Binocular vision Offset camera from S/C CG Angles-Only Navigation Performance Resulting Trajectory Trajectory Planning (Guidance) Slide Slide 8

9 Existing Work on Angles-Only Navigation Representative Missions ARGON (PRISMA) Ground-in-theloop: image processing, navigation, and maneuver planning AVANTI (FireBIRD) Autonomous guidance, navigation, and maneuver planning mstar (CEAA) Fully autonomous vision-based rendezvous with ejected nano-sat Slide Slide 9 9

10 Short-Term Research Goals Dynamics State Orbit Geometry Navigation Architecture Measurement Modeling Navigation Conditions Observability Constraints Slide Slide 10 10

11 Long-Term Research Goals High-fidelity validation in SLAB multisensor testbed mstar (CEAA) mdot (SLAB) Slide Slide 11 11

12 Dynamics State: Relative Orbital Elements Relative Orbital Elements (ROE) Rel. Semi-Major Axis Rel. Mean Arg. of Latitude Rel. Eccentricity Vector Rel. Inclination Vector Straightforward inclusion of perturbations (geopotential, differential drag, control) Insight into relative orbit geometry Unobservable state has been shown to decouple Slide Slide 12 12

13 Observability Assessment Goal: determine how well state can be reconstructed from measurements. Ingredients: dynamics and measurement model State Transition Matrix (STM) Measurement Sensitivity Matrix (MSM) Criteria: Observability Matrix (OM) and Observability Gramian (OG) Rank of OM: first indication of observability. Condition Number of OG: sensitivity of reconstructed state Observability Matrix Observability Gramian Slide Slide 13 13

14 Observability: Variable Simulation Parameters Simulation varies navigation conditions and orbit geometry for each state representation and choice of measurement model formulation. Returns conditioning characteristics at each iteration. Parameter Navigation Procedure Duration Measurement Density Relative Orbit Drift Rate Radial Oscillation Amplitude Cross-Track Oscillation Amplitude Mean Along-Track Separation Eccentricity/Inclination Angle Nominal Value 1 day 30 measurements/orbit 0.50 km/orbit 4 km 4 km 10 km 0 radians Slide Slide 14 14

15 Nominal Relative Orbit Trajectory Slide Slide 15 15

16 Observability: Example Simulation Output Slide Slide 16 16

17 Navigation Filter Design EKF with ROE state and nonlinear measurement model Gaussian white noise with σ = 40 added to azimuth and elevation measurements State initialization error on the order of 0.5 km Blue: Est. Error Red: 1-σ Green: 3-σ Slide Slide 17 17

18 Navigation Filter Design EKF with rectilinear state Gaussian white noise with σ = 40 added to azimuth and elevation measurements No state initialization error Slide Slide 18 18

19 Navigation Filter Improvement Estimation error is largely influenced by initialization and not measurement error. Improvement: deterministic batch initial orbit determination algorithm that initializes the EKF with a more favorable accuracy. Batch Estimation Sequential Estimation Slide Slide 19 19

20 Navigation Filter Improvement Preliminary algorithm returns initialization error on the order of 30m. Idea: use batch algorithm to sequentially re-initialize for improved observability. Run sequential estimation while batch is building. Blue: Est. Error Red: 1-σ Green: 3-σ Slide Slide 20 20

21 Ways Forward Introduce sequential re-initialization of EKF with orbit determination algorithm- similar to using maneuvers to improve observability. Improve ROE state transition matrix for more applications. Consideration of maneuvers to further improve observability. Validate in SLAB high-fidelity spacecraft simulation software. Embedding algorithms and testing with hardware-in-the-loop. Slide Slide21 21

22 Thank you for your time! Questions? Slide Slide 22 22

23 Backup Slide Slide 23 23

24 State Comparison: Rectilinear and Curvilinear Rectilinear State Hill-Clohessy-Wiltshire (HCW) model for relative rectilinear position and velocity Approximates curvature of relative orbit with straight line segments. Curvilinear State Hill-Clohessy-Wiltshire (HCW) model for relative curvilinear position and velocity Approximates curvature of relative orbit with arc segments. Slide Slide 24 24

25 State Comparison: Integration Constants Slide Slide 25 25

26 Observability: Measurement Model Line-of-sight unit vector from reference spacecraft camera frame to target. Described by two angles: azimuth and elevation. Measurements can be modeled from state knowledge Actual VBS line-of-sight trend during PRISMA mission Image Credit: DLR Azimuth is angle from boresight to LOS projection on x-z plane Elevation is angle from x-z plane to LOS vector Slide Slide 26 26

27 Measurement Models: Rectilinear State Measurement Sensitivity Matrix (MSM): sensitivity of measurement with respect to the state of interest. Sensitivity of measurements w.r.t relative position in camera frame Absolute attitude of camera Mapping from vehicle RTN to absolute attitude Proposed attitude of the camera is a permutation of the RTN frame Measurements are only position-dependent Slide Slide 27 27

28 Measurement Models: ROE State Built off of Rectilinear MSM by adding one additional mapping to the new state representation. Mapping comes from solution of HCW equations, represented in terms of the Relative Orbital Elements Mapping between rectilinear position and ROE state Rectilinear Portion Mapping measurement sensitivity in rectilinear representation to sensitivity in ROE Slide Slide 28 28

29 Observability: Example Simulation Output Slide Slide 29 29

30 Initial Orbit Determination Built from batch of LOS measurements. Leverages the planar assumption: the observed orbit LOS vectors must lie in a plane because the target orbit forms a plane. Determined position vectors are not independent. Slide Slide 30 30