Solicitation_BAA-RVKV-2015-0001.docx

BROAD AGENCY ANNOUNCEMENT

BAA-RVKV-2015-0001

Air Force Research Laboratory/Space Vehicles Directorate

FULL TEXT ANNOUNCEMENT

21 October 2014

FEDERAL AGENCY NAME: Air Force Research Laboratory, Space Vehicles Directorate (AFRL/RV)

BROAD AGENCY ANNOUNCEMENT TITLE: Control, Navigation, & Guidance for Autonomous Spacecraft (CoNGAS)

BROAD AGENCY ANNOUNCEMENT TYPE: This is the Initial Announcement.

PROPOSAL DUE DATE AND TIME: Proposals are due by 12:00 Noon, Mountain Time 10 December 2014 and delivered to Det 8 AFRL/RVKVV, ATTN: Jaclyn Pino, 3550 Aberdeen Ave., Kirtland AFB, NM 87117-5776. Proposals received after this due date and time shall be governed by the provisions of FAR 52.215-1(c)(3). It should be noted that this installation observes strict security procedures to enter the facility. These security procedures are NOT considered an interruption of normal Government processes, and proposals received after the above stated date and time as a result of security delays will be considered “late”. NOTE: If offerors use commercial carriers for delivery of proposals, carriers may not honor time-of-day delivery guarantees on military installations. Early proposal submission is encouraged. See Proposal/Application and Submission Information for full instructions. Please note that BAA-RVKV-2015-0001 and BAA-RVKV-2015-0001A, Special Notice to Small Businesses (not a set-aside), have the same Technical Requirements, share the same funding pool, and will be evaluated at the same time using the same evaluation criteria which will place first priority on technical capability. If proposing to BAA-RVKV-2015-0001, there is no value in submitting the same proposal under BAA-RVKV-2015-0001A, Special Notice to Small Businesses (not a set-aside).

PROGRAM Description

Statement of Objectives/Needs

The Air Force Research Laboratory (AFRL) Space Vehicle Directorate (RV) would like to investigate and advance the state-of-the-art knowledge in spacecraft guidance, navigation, and control (GNC) technology; specifically in the areas of advanced on-board guidance and navigation for rendezvous and proximity missions, advanced algorithms for space object tracking and identification, advanced spacecraft dynamics models, autonomous GNC algorithms, and advanced GNC sensors and thrusters. The Guidance, Navigation, & Control group at AFRL has been successful in advancing GNC technologies in a number of areas to demonstrate new capabilities vital to future Air Force Systems. This effort seeks to develop forward-looking technologies that will enable the Air Force to meet future Space Situational Awareness and Space Control mission requirements.

Technical Topics

On-Board Spacecraft Navigation, Attitude State, and Health Determination, Fault Detection, & Analysis

This topic area focuses on applied estimation theory and methods of data exploitation for knowledge inference in On-Orbit Spacecraft Navigation and Attitude Determination, and Spacecraft System ID, Fault Detection, and Analysis. It is intended to support work in development of Autonomous Guidance and Control Algorithms (see Section B.iii), but proposals can consider each topic separately, or only propose against one of the two topics).

The primary driving capability of interest that this topic will support is spacecraft autonomy. Proposed frameworks must be able to take as input and fuse the information from a variety of in-situ measurements [e.g. inertial measurement units, gyroscopes, star-trackers, reaction wheels, etc.) to autonomously (i.e. without ground-based input) yield a comprehensive and robust capability for accurate and precise trajectory, attitude, and/or spacecraft state parameter estimation. However, it is realized that faults/anomalies do occur on-orbit where the spacecraft-system configuration changes (i.e., change in spacecraft, sensor, and actuator parameters, states, and measurements).

The majority of documented spacecraft faults/anomalies that have been documented are associated with the Guidance, Navigation, and Control (GN&C) system in the first week of the mission [1]. Many of these faults/anomalies have been traced specifically to problems with the flight software. Very few anomalies are the cause of problems multiple times, and thus it is not pragmatic to just prepare for faults/anomalies that have occurred; rather, approaches that can be adaptable for new and/or unknown anomalies are sought. In particular, techniques that can handle more than simply “on/off” failure modes are sought (e.g., graceful/degradation, stuck-on, intermittent failure conditions, etc.)

Furthermore, after an anomaly has occurred there are typically many man-hours spent addressing this occurrence through trend analysis of the telemetry, all the while the spacecraft sits in safe-mode and the mission halted. Approaches are desired that can replace this man-intensive diagnosis period with autonomous, on-board fault diagnosis/analysis and system/parameter identification, where applicable.

Proposed approaches should incorporate self-calibration activities such as star-tracker and/or thruster calibration, as well as dynamic model estimation and refinement, estimation of new states and parameters once configuration of spacecraft system has changed. The proposed work must account for all sensor noises and biases, bounds for possible time and input-output delays.

Proposed approaches should focus on the navigation, attitude determination, and fault/health detection and fault tolerant control aspects of the program, but consider how an autonomous GN&C subsystem would interface with higher-level planning, scheduling, and decision making approaches (which are not themselves the focus of this solicitation).

Topic References:

1. Brown, N.F., “Worldwide and NASA-Only Satellite and Launch Vehicle GN&C Anomaly Trends”, NASA Engineering and Safety Center, Aerospace Report NO. ATR 2008(5175)-1, 2007.

Space Situational Awareness

This topic area focuses on Space Situational Awareness (specifically space object detection, tracking, identification, and characterization [DTIC]).

Proposed approaches in this area must deal with a number of challenges including: sensor detections that are not associated to known space objects; the population of space objects that each space object is unique (different) and the differences in object behavior are all due to non-conservative forces and torques which depend on object characteristics (size, shape, materials, orientation).

Effective assessment of object behavior requires characterization. Moreover, sensor tasking is not optimized for data collection based upon object behavior nor data information content. Simply attempting to maximize the number of observations will not lead to successful space situational awareness.

Object detection, tracking, and classification is performed in mutual exclusivity. Interdependencies amongst these are not exploited in a unified framework (e.g. Finite Set Statistics [FISST]). The proposed work must, with emphasis on inference and prediction:

· Investigate new data/track association/correlation methods for orbit-regime-agnostic multi-sensor/multi-object DTIC for both known and newly-detected objects

· Develop a framework to better characterize sensor level errors including biases to improve the input to the trajectory and model parameter estimation process

· Improve nonlinear estimation and the representation of uncertainty to ensure realism in describing space object ambiguity (e.g. physically realistic/representative probability density functions)

Methods that use information theory as a framework/foundation are favored. More specifically, it is desired to receive proposals which cast the trajectory and parameter estimation process as one analogous to communication theory where the space object is considered to be transmitting a message where the message is the minimal set of states/parameters (channels), corrupted by noises and biases, that fully describe and predict observed behavior and support unique object identification and classification. Determining this basis of channels is desired as well as how to maximize the information content of those channels given available multi-sensor data, taking into account the presence of clutter and the fact that the probability of detection is often times less than unity.

Proposed efforts must be able to ingest and fuse the information from a variety of ground-based sensors (e.g. space surveillance network, Air Force Space Control Network (AFSCN) Space Ground Link Stations (SGLS) range, non-traditional sensors [radar, telescopes, etc.]) to yield a comprehensive and robust capability for accurate and precise trajectory reconstruction as well as trajectory predictions.

Topic References:

1. Hussein, I. I., Früh, C., Erwin, R. S., and Jah, M. K. “An AEGIS-FISST Algorithm for Joint Detection, Classification and Tracking,” Proc. AAS Space Flight Mechanics Conference, Kuai, HI, February 2013 (submitted).

2. Hussein, I. I., Jah, M. K., and Erwin, R. S., “An AEGIS-FISST Sensor Management Approach for Joint Detection and Tracking in SSA,” Proc. AAS Space Flight Mechanics Conference, Kuai, HI

Autonomous, Fault-Tolerant Spacecraft Guidance and Control

Due to the high cost of space systems, the ability to inspect, service, and repair/refuel these systems on-orbit is highly desirable. Such missions require precise control of spacecraft motion to ensure mission objectives are met (e.g., imaging parameters, relative velocity constraints for docking, etc.), which is dependent on the guidance algorithms used to generate trajectories for these missions as well as the control algorithms to realize the generated trajectories.

The goal of this effort is to develop safe, robust, and reliable guidance and control algorithms that lend themselves to on-board implementation, with a minimum of ground-based operator intervention and that are amenable to faults/anomalies. Furthermore, these algorithms must be able to safely switch from one mode to the next with verifiable stability and robustness in the presence of uncertainty and time delays while maintaining acceptable mission performance.

The current standard of practice is to solve the guidance and control problems separately within the GNC framework of a space system. By doing so, the conflicting guidance and control objectives are not accounted for and the subsequent solution method does not involve all the necessary aspects of the problem. For instance, having the spacecraft translate to a point, may require reorientation of the spacecraft (for controllability), but the spacecraft may be trying to perform some rotational objective (e.g., point at Sun). However, by increasing the fidelity of the solution methods by accounting for more coupled dynamics within the problem model, the algorithm’s computational feasibility decreases at a very fast rate.

Algorithms should be evaluated based on their robustness and performance in the face of systemic uncertainties (e.g., modeling errors, vehicle state uncertainties) as well as the ability to perform safely in the case of faults/anomalies and/or error (e.g., fail-safe qualities), enable efficient use of spacecraft resources (fuel, power, time, etc.), and their ability to reconfigure based on shifting mission priorities.

Preference will be given to approaches based on mathematically rigorous foundations (in terms of predicting/bounding performance, robustness, optimality, and safety), that demonstrate traceability and scalability to the applications of interest, and that minimize computational requirements. Specifically, robust algorithms that can easily be tailored to specific guidance and control algorithms are desired. These algorithms must be numerically stable with a small computational footprint.

Suggested areas to explore in autonomous fault tolerant guidance and control (for any or all of the spacecraft states) include:

· Methods that treat guidance as a relative problem, where a trajectory in space (rather than an actual space object) is targeted (e.g., Refs 1-1)

· Methods that apply traditionally under-utilized techniques or techniques from non-aerospace fields (e.g., Refs 2-4)

· Methods that consider the reachability of a spacecraft, given constraints on time and fuel (e.g., Ref 5)

· Methods that consider practical constraints, e.g., radiation and heating, sun exclusion, debris avoidance, etc. (e.g., Ref 6)

· Methods that guarantee a feasible solution and that may be used onboard (e.g., Ref.7)

· Methods that can be computationally tractable and stable to provide effective changes to the guidance and control mode switching when the system configuration has changed with respect to a fault/anomaly.

· Methods that account for coupled translational and rotational motion and actuator configurations are desired.

These algorithms must increase the analysis capability on the ground such that more accurate on-orbit vehicle maneuvers can be evaluated before onboard implementation (Refs. 8 –10). Autonomous control refers to the satellite’s ability to execute the commands sent from the guidance system in a robust and timely manner. Concerns with autonomous control systems is their closed-loop stability, robustness to uncertainty/noise and/or inappropriate commands (e.g., exceeding saturation/rate limits or other constraints), and performance that meets mission objectives (e.g., minimal fuel/time, pointing, tracking, positioning accuracy). Relevant areas for autonomous control are nonlinear, linear, adaptive, optimal, and robust control, hybrid systems theory, and others. Relevant areas for cooperative and non-cooperative control are decentralized and centralized control in nonlinear and linear frameworks, game-theory, optimal control, neural-networks, machine learning, and others.

Any proposed solution should:

· Address the tradeoff between executing optimal solutions and those that are physically realizable and safe to the spacecraft system

· Consider scenarios in which more than one object is being controlled (i.e., cooperative control) as well as scenarios in which the object that is being approached may be non(un)cooperative (i.e., unable to control itself or purposely uncontrollable)

· Account for coupling between translational and attitude dynamics

· Are stable, and robust to time-delays and uncertainty mode switching due to faults/anomalies, in addition to the control at the different modes.

· Be tested on a wide variety of scenarios/conditions to show robustness and guaranteed stability (e.g., Monte Carlo simulations).

· Must be written in Matlab or Simulink in such a way that the Matlab or Simulink code can generate code (e.g. C. C++, etc.)

· Have the infrastructure to account for an imperfect navigation solution as an input to the algorithms

Topic References:

1. Jasper, Anthony, Lovell, and Newman, “Application of Relative Satellite Motion Dynamics to Lambert’s Problem,” AAS Paper 13-393, presented at the AAS/AIAA Space Flight Mechanics Meeting, Kauai, HI, Feb 10-14, 2013

2. Zhang, Zhou, and Mortari, “An approximate analytical method for short-range impulsive orbit rendezvous using relative Lambert solutions,” Acta Astronautica, Volume 81, Issue 1, December 2012, Pages 318–324

3. Bevilacqua, “Constrained-Time Guidance and Control for Spacecraft Planar Re-phasing via Input-Shaping and Differential Drag,” submitted to AIAA Journal of Guidance, Control, and Dynamics.

4. Thompson, Choi, Piggott, and Beaver, “Orbital Targeting Based on Hodograph Theory for Improved Rendezvous Safety,” AIAA Journal of Guidance, Control, and Dynamics, Vol. 33, No. 5, pp. 1566-1576.

5. Lopez and McInnes. "Autonomous rendezvous using artificial potential function guidance", AIAA Journal of Guidance, Control, and Dynamics, Vol. 18, No. 2 (1995), pp. 237-241

6. Holzinger and Scheeres, “Applied Reachability for Space Situational Awareness and Safety in Spacecraft Proximity Operations,” AIAA Paper 2009-6096, presented at the AIAA Guidance, Navigation, & Control Conference, Chicago, IL, Aug 10-13, 2009

7. Dutta, “Minimizing total radiation fluence during time-constrained electric orbit-raising,” presented at the 23rd International Symposium on Space Flight Dynamics, Pasadena, CA, October 29 - November 2, 2012

8. Lu, Ping, and Xinfu Liu. "Autonomous Trajectory Planning for Rendezvous and Proximity Operations by Conic Optimization." Journal of Guidance, Control, and Dynamics (2013): 1-15.

9. Hablani, Hari B., Myron Tapper, and David Dana-Bashian. "Guidance algorithms for autonomous rendezvous of spacecraft with a target vehicle in circular orbit." AIAA Guidance, Navigation, and control conference and Exhibit. 2001.

10. Scharf, Daniel P., Fred Y. Hadaegh, and Scott R. Ploen. "A survey of spacecraft formation flying guidance and control. part ii: control." American Control Conference, 2004. Proceedings of the 2004. Vol. 4. IEEE, 2004.

11. Pan, Haizhou, and Vikram Kapila. "Adaptive nonlinear control for spacecraft formation flying with coupled translational and attitude dynamics." Decision and Control, 2001. Proceedings of the 40th IEEE Conference on. Vol. 3. IEEE, 2001.

On-Board Relative Navigation Methods for Passive Sensors

Relative navigation is the process by which one satellite determines its state (position, velocity and/or attitude) relative to another object using various sensors and algorithms. Relative navigation can be achieved using either active (e.g. LIDAR) or passive (e.g. optical) sensors. Additionally, relative navigation can be performed between two cooperating satellites or by one satellite relative to a non-cooperative object (e.g. orbital debris).

For this research it is desired to look at passive navigation sensors to perform navigation relative to a non-cooperative resident space object (RSO). The difficulty with using passive sensors for relative navigation is that, while easily providing directional data, they are generally unable to directly provide the range or relative orientation between the sensor and RSO. The difficulty with navigating relative to a non-cooperative RSO is that it neither shares information with the primary satellite, nor is it equipped with navigation aids such as fiducials or retro-reflectors. The use of advanced computer vision techniques will allow for safe and accurate relative navigation using passive sensors enabling key missions such as orbital debris removal.

As the distance between the sensor and the object being observed decreases, or as the resolving power of the sensor system increases as in the case of spacecraft proximity operations, large telescopes, or distributed arrays, the observed signal representing the object becomes more complex and contains richer information. In this scenario it is possible to more robustly define the structure of the 3D object as well as estimate the observed six degree of freedom relative motion with respect to the observer. Advanced 6 DOF tracking algorithms are desired which exploit multi-resolution spatio-temporal data analysis methods. These algorithms should enable a robust onboard solution to the relative navigation problem. Relative navigation is the process by which one satellite determines its state (position, velocity and/or attitude) relative to a proximal body which may be cooperative or uncooperative (i.e. debris). These methods include but are not limited to scale space analysis both linear, isotropic, and anisostropic, structure tensor, mean shift theory, optical flow, structure from motion, and differential geometry. The desired algorithms will be capable of being efficiently implemented for on board implementation, and will enable the tracking of complex objects across scale in order to enable variations in standoff distance to be possible during proximity operations.

Of particular interest are methods which exploit data from passive sensors using zero a priori knowledge of the proximal body, are inherently robust to changing lighting, can fuse data from multiple sensors (various wavelength, e.g. IR, variable sensing geometries, e.g. stereovision), or monocular approaches based on Structure from Motion (SfM). Methods based on multi-scale feature detection and tracking algorithms should demonstrate efficient tracking through range variations as well as robustness to outlier features caused by lighting artifacts, specular reflections, or glints. Methods based on optical flow should rigorously demonstrate the 3D information learned about the object over time in order to enable predicted observations derived from the estimated 6DOF state and the current understanding of the observed 3D object. All approaches should be data driven and determine track-able features from the native texture measured on the object.

Finally, the structure of features or appearance of signals as a function of relative pose should enable a methodology for the characterization of space objects. Accurate characterization of space objects including debris enables better prediction of object behavior which enhances object tracking. The use of advanced computer vision techniques onboard will enable safe and accurate relative navigation using passive sensors which will enable key missions such as orbital debris removal. The desired relative navigation software should be capable of providing reliable and accurate estimates of the relative position and attitude (pose) of the RSO relative to the primary satellite. The software should be capable of being thoroughly tested using a variety of synthetic space imagery.

References:

1. S. Augenstein, S. Rock, “Simultaneous Estimation of Target Pose and 3-D Shape using the FastSLAM Algorithm,” in Proc. AIAA Guidance, Navigation, and Control Conference (GNC), Chicago, IL, USA 2009

2. K. Anderson, A. Howell, “Natural Feature Tracking for Rendezvous and Proximity Operations,” Advances in the Astronautical Sciences, Volume 141, AAS 11-075, 2011

3. Goddard Natural Feature Image Recongnition (GNFIR), unpublished sources

4. B. Jahne, Spatio-Temporal Image Processing – Theory and Scientific Applications, Lecture Notes in Computer Science, 751, 1993

5. A.P. Witkin, Scale space filtering, in Proceedings, International Joint Conference on Artificial Intelligence, 1983, pp. 1019-1023.

6. L.M.J. Florack, M.M. ter Haar Romeny, J.J. Koenderink, and M.A. Viergever, Cartesian differential invariants in scale space, Journal of Mathematical Imaging and Vision, 3(4), 1993, 327-348.

7. J. Weber and J. Malik, Robust Computation of Optical-Flow in a Multiscale Differential Framework, International Journal of Computer Vision, 14(1), 1995, 67-81.

8. Lowe, D. G., Distinctive Image Feature from Scale-Invariant Keypoint, International Journal of Computer Vision, Vol. 60, No. 2, 2004, pp. 91-110.

9. Ho, H.T. and Gibbins, D., Multi-Scale Feature Extraction for 3D Models Using Local Surface Curvature, DICTA, 2008, pp16-23.

10. W.J. Niessen, J.S. Duncan, M. Nielsen, L.M.J. Florack, B.M. ter Haar Romeny, and, V.A. Viergever, A Multiscale Approach to Image Sequenct Analysis, Computer Vision and Image Understanding, Vol. 65, No. 2, pp. 259-268, 1997.

11. C. Tomasi, and T. Kanade, Detection and Tracking of Point Features, Technical Report CMU-CS-91-132, April 1991

12. T. Lindeberg, Linear Spatio-Temporal Scale-Space, Technical Report ISRN KTH/NA/P -01/22-SE, November 2001

13. D. Comaniciu, and P. Meer, Mean Shift: A Robust Approach toward Feature Space Analsysis

14. R.T. Collins, Mean-shift Blob Tracking through Scale-Space

15. M.H. Hangbo, J-S Kim, and T. Kanade, Inertial-Aided KLT Feature Tracking for a Moving Camera, In Proceedings, International Conference on Intelligent Robots and Systems, October 11-15, 2009, St. Louis, Ms.

16. F. Juire, and M. Dhome, Real Time Robust Template Matching, In Proceedings, British Machine Vision Conference, 2002, pp. 123-132

17. H. Houssecker, and B. Jahne, A Tensor Approach for Local Structure Analysis in Multi-Dimensional Images, Theoretical/Modeling Development of Spacecraft Dynamics for Close-Proximity Missions

The Air Force has a pressing need to better understand and utilize the dynamics of relative satellite motion (i.e., the motion of one satellite with respect to one or more other satellites) for close-proximity missions. These include both active (formation, cluster, fractionated) and passive (rendezvous, proximity operations, docking, grappling) mission types. The former missions typically involve multiple satellites with maneuvering capability and communication links (both with the ground and one another) performing some cooperative task, e.g. remote sensing. Whereas the latter missions typically involve a lesser degree of maneuverability, connectivity, and cooperation among the satellites.

For over a century, scientists have derived various sets of differential equations characterizing relative motion; for many of these models, closed-form solutions have been solved and documented. The basic aspects of each relative motion model are based on assumptions made about the scenario being modeled; in particular:

· What natural forces on each object are to be accounted for?

· Are the objects in close proximity, i.e., on closely neighboring orbits?

· Is the chief on a circular or near-circular orbit?

Each relative motion model in the literature can be categorized by how it addresses each of these three basic questions. It is also important to note how each of the above aspects affects the form of the governing differential equations:

· Most natural forces included in a relative motion model have a nonlinear effect, i.e. they are nonlinear functions of the relative position and/or velocity.

· When assuming the objects are in close proximity, a common practice is to linearize the motion about the reference (“chief”) orbit.

· When assuming the chief is on a circular orbit, the relative motion dynamics are time-invariant; conversely, for an elliptical chief orbit, the dynamics are time-varying.

Existing relative motion models include Hill’s-Clohessy-Wiltshire (Refs. 1 – 1), Tschauner-Hempel (Ref. 2), Yamanaka-Ankersen (Ref. 3), Sedwick-Schweighart (Ref. 4), Gim-Alfriend (Ref. 5), Sabol-McLaughlin (Ref. 6), and Newman et al. (Ref. 7). There is no widely accepted closed-form analytical solution for relative motion that accounts for forces other than two-body and J2 gravity, nor one that contains higher than 2nd-order terms in the solution.

Additionally, some of the above models have been re-cast in geometric coordinates (as opposed to using Cartesian coordinates), specifically Ref. 8 which reformulates the Hill’s-Clohessy-Wiltshire model and Ref. 9 which reformulates the Tschauner-Hempel model. These characterizations lend themselves well to visualization of the motion and particular mission applications.

It is this significant gap in the research that motivates the work proposed here.

The goal of this effort is to expand the state-of-the-art in relative motion modeling beyond that described above. Areas of relevant research include:

· Derivation of closed-form analytical solutions that account for forces in addition to two-body and J2 gravity, particularly solutions that contain terms of 2nd-order or higher

· Derivation (and utilization) of transformations that allow analysis and/or manipulation of complex relative motion models in a simpler domain (Refs. 10 – 11)

· Demonstration of how the derived models can be used for guidance, navigation, and control of spacecraft

References:

1. Clohessy, W. H., and Wiltshire, R. S., “Terminal Guidance System for Satellite Rendezvous”, Journal of the Aerospace Sciences, Vol. 27, No. 9, 1960, pp. 653-658.

2. Hill, G. W., “Researches in the Lunar Theory,” American Journal of Mathematics, Volume 1, 1878, pp. 5-26.

3. Tschauner, J., and Hempel, P., “Rendezvous with a Target in an Elliptical Orbit,” Astronautica Acta, Vol. 11, Mar.-Apr. 1965, pp. 104-109.

4. Yamanaka, K., and Ankersen, F., “New State Transition Matrix for Relative Motion on an Arbitrary Elliptical Orbit”, Journal of Guidance, Control and Dynamics, Vol. 25, No. 1, 2002, pp. 60-66.

5. Schweighart, S. A., and Sedwick, R. J., “High Fidelity Linearized J2 Model for Satellite Formation Flight”, Journal of Guidance, Control and Dynamics, Vol. 25, No. 6, 2002, pp. 1073-1080.

6. Gim, D.W., and Alfriend, K.T., ``State Transition Matrix of Relative Motion for the Perturbed Noncircular Reference Orbit", AIAA Journal of Guidance, Control and Dynamics, Vol. 26, No. 6, 2003, pp. 956-971.

7. Sabol, C., and Mclaughlin, C., “Meet the Cluster Orbits with Perturbations of Keplerian Elements (COWPOKE) Equations,” Paper AAS 03-138, presented at the AAS/AIAA Space Flight Mechanics Meeting, Ponce, PR, Feb 9-13, 2003.

8. Stringer, Newman, Lovell, and Omran, “Analysis of a New Nonlinear Solution of Relative Orbital Motion,” presented at the 23rd International Symposium on Space Flight Dynamics, Pasadena, CA, October 29 - November 2, 2012

9. Lovell, T.A. and Tragesser, S.G., “Guidance for Relative Motion of Low Earth Orbit Spacecraft Based on Relative Orbit Elements,” paper AIAA-2004-4988, presented at the AIAA/AAS Astrodynamics Specialist Conference and Exhibit, August 2004.

10. Sherrill, Sinclair, and Lovell, “Review of the Solutions to the Tschauner-Hempel Equations for Satellite Relative Motion,” AAS Paper 12-149, presented at the AAS/AIAA Space Flight Mechanics Meeting, Charleston, SC, Jan 29 – Feb 2, 2012

11. Sherrill, Sinclair, Sinha, and Lovell, “Lyapunov-Floquet Transformation of Satellite Relative Motion in Elliptic Orbits,” AAS Paper 13-466, presented at the AAS/AIAA Space Flight Mechanics Meeting, Kauai, HI, Feb 10-14, 2013

12. Sinclair, Sherrill, and Lovell, “Calibration of Linearized Solutions for Satellite Relative Motion,” AAS Paper 13-467, presented at the AAS/AIAA Space Flight Mechanics Meeting, Kauai, HI, Feb 10-14, 2013

High Accuracy, Reduced SWaP IMU

Inertial measurement units (IMUs) for spacecraft are available from several domestic vendors such as Honeywell, L3, Northrop, Kearfott, and ATA. Among the IMUs produced by these vendors, the unit with the smallest SWaP is the LN200 produced by Northrop and the highest performing units are the MIMU, which is produced by Honeywell. High accuracy IMUs make it possible to capture even the smallest motion of the spacecraft, and as a result are valuable for propulsion experiments, during which it is desired to accurately assess the resulting effect of thruster firings on the spacecraft.

The overall object is to develop an IMU that has at least the MIMU performance characteristics in a LN200 form factor. The preferred development approach is a modular one where accelerometer and gyro packages are developed separately so that there is also an option of using them independently of the IMU. However, integrated solutions meeting the desired requirements will also be considered. If the modular approach is taken, the baseline performance desired for accelerometers is the QA3000 (Honeywell) and for the gyro is the GG1320 (Honeywell), which is used in the MIMU. Additional desired specifications for the IMU and its components include at least 3-5 years of life and a high sampling rate (10+ Hz).

Desired deliverables:

· TRL 6 validated performance design

· 3-5 years of life

· Sampling Rate: > 10 Hz

· Form Factor: < 90 mm cube

· Mass: < 2 kg

· Accelerometer

· Input Range: ± 60 g or greater

· Bias: < 4 mg (1 )

· Scale Factor Accuracy: < 80 ppm (1 )

· Noise: < 5 g rms, 0 – 50 Hz

· Gyro

· Range: ± 375 °/sec or greater

· Bias: < 0.005 °/hr (1 )

· Random Walk: < 0.005 °/√hr (1 )

· Scale Factor Accuracy: < 1 ppm (1 )

· Environmental

· Radiation: 100 Krad total dose or greater

· SEU Tolerant

· Latch-up Immune

· Temperature: -30°C to 65°C

· Vibration: 19.7 g rms or higher

References:

1. Northrop Grumman. (2013). LN-200S Datasheet. Retrieved June 18, 2013 from http://www.northropgrumman.com/Capabilities/LN200FOG/Documents/ln200s.pdf

2. Honeywell International Inc. (2006). Miniature Inertial Measurement Unit Datasheet. Retrieved June 18, 2013 from http://www51.honeywell.com/aero/common/documents/myaerospacecatalog-documents/Miniature_Inertial_MeasurementUnit.pdf

3. Honeywell International Inc. (2006). Q-Flex QA-3000 Accelerometer Datasheet. Retrieved June 18, 2013 from http://www51.honeywell.com/aero/common/documents/myaerospacecatalog-documents/MissilesMunitions-documents/QA3000_DataSheet.pdf

4. Honeywell International Inc. (n.d.). GG1320 Digital Ring Laser Gyro Datasheet. Retrieved June 18, 2013 from http://www51.honeywell.com/aero/common/documents/myaerospacecatalog-documents/Missiles-Munitions/GG1320AN_Digital_Laser_Gyro.pdf

High Resolution Hydrazine Thruster Technologies

Commercially available thrusters in the 1 N and 22 N classes have long flight legacies and relatively well defined operating constraints. However, while the operating constraints have been well established (propellant inlet temperatures, thruster component pre-heat temperatures, and time to pre-heat temperatures), the variance in response and decomposition/combustion efficiency has not been well characterized over either the life of the thruster, or as a function of its duty cycle.

An objective is the development of a spacecraft liquid chemical thruster in the 1 – 22 N thrust range which improves thruster operating performance. Since the standard performance for 1-22 N hydrazine thrusters are defined by minimum impulse bit (MIB), for state of the art these values, for flight proven thrusters, are 0.01 N-sec with a 1.6 milli-sec on-time (Aerojet MR-103G for 1N thrust class) and 0.5 N-sec with an on-time of 1.6 milli-sec(MR-106E for 22N thrust class). With on-time defined as the time required for the valve to completely open. The Aerojet MR-103M is in the 1N thrust class and delivers a MIB of 0.006N-sec but has not been flown although it has been flight qualified. It is desired to develop a thruster design is capable maintaining aforementioned MIB at constant feed pressure/propellant flow rate over the mission life. If constant feed pressure/propellant flow rates are not available, it is desired to have a physics-based equation functioning on inputs of thruster geometry and feed pressure so that the MIB can be predicted accurately. Further enhancement of required MIB performance is the variance reduction of the delivered impulse as thruster operation continues over many pulse cycles. Currently the delivered impulse for the first MIB if often very different from the MIB delivered impulse after several firings, this leads to large fluctuations and inconsistent impulse delivery even when the same MIB is command. A further consideration is the reduction in the “blow-down” time of the thruster which corresponds to the time from off command to end of thrust delivery; this will ensure that the desired impulse is appropriately delivered.

In either case, the contribution of chemical and thermal energy driving the decomposition/combustion reactions and the amount additional thermal energy that will be required to maintain the desired response performance are desired to be included in the operational algorithm for the thruster. It is anticipated that the thermal energy factor will increase over time not only due to reduction of propellant mass flow rate, but also due to attrition of catalyst-the chemical driver. Various system approaches will be considered, however, state of the art, advanced monopropellant formulation and bipropellant thrusters are of interest.

Desired deliverables:

· TRL 6 validated performance design for user supplied mission duty cycles with:

· For 22N class, <0.5 N-sec MIB with 0.015 milli-second on-time

· For 1N class, <0.01N-sec MIB with 0.015 milli-second on-time

· Appropriately quantification of “blow-down” time from “off” command to delivery of 0 thrust

· Reduction in variance of delivered impulse for different pulses given same MIB command

· Identification of overall energy input needed to achieve transition of liquid propellants to high temperature gas

· Clearly defined thermal and chemical reactivity (whether hypergolic, catalytic, or thermal initiation) contributions

· MIB performance map (physics based performance degradation algorithm) that accounts for thermal and chemical effects

The catalytic reactor beds, monopropellant thrusters, are responsible for adding the thermal and chemical impetus to the liquid hydrazine to decompose into high temperature gas. These catalytic reactor beds are particularly sensitive, and they degrade over mission life with propellant throughput, which impacts the responsiveness of the thruster. In order to counteract this issue, an additional objective for this topics is the development of low power, space flight rated diagnostic sensors, which provide accurate assessment of the state of health of the thruster, are desired. State of the art, advanced monopropellant formulation and bipropellant thrusters are of interest.

The real time knowledge of a thruster’s impulse delivery capability and the thrust vector variance are the state of health information that have the most impact in supporting increased precision spacecraft maneuvering. With the availability of sensors that are capable of assessing the thruster’s state of health, it is anticipated that thruster commands can be modified accordingly to enable continued precision maneuvering of the spacecraft over the mission life. Other anticipated benefit of this technology is a decreased use of propellant, which would lead to increased mission life. Plume species spectrographic approaches, which are not life limited, are envisioned as having the most promise of being adapted for the desire purpose, but other approaches will be considered. The determination of variances of the delivered thrust vector is also desired as a part of the state of health assessment, and in order to obtain the desired data, the vision system would probably have to track indicative plume species, and the species and temperature distribution throughout the plume. Desired power levels would be those of other thruster componentry such as temperature control media (catalytic reactor heaters or valves).

Desired deliverable:

· TRL 6 validated performance design of a diagnostic tool that allows an assessment of the state of health of a liquid chemical propulsion thruster. The tool should be able to provide knowledge of:

· Impulse delivery capability

· Variance of the thrust vector

Significant improvements in attitude control capabilities and reductions in propellant consumption can be realized by improving the accuracy of the predicted axial impulse bit and/or steady-state axial thrust of hydrazine monopropellant thrusters. The objective of providing diagnostic sensors for these thrusters will help but still requires an initial value for thruster parameters at the beginning of mission life. Therefore, it is desirable to accurately predict the impulse bit and/or thrust for an entire range of pulse durations, ranging from 15 ms to steady-state thrust, at all points during the lifetime of the thruster (as the catalyst bed degrades). It is also important to be able to predict, or alternatively to experimentally verify as negligible, any off-axis thrust components and torques produced by the propulsion system due to either asymmetries in the thruster plume or to misalignments of the thruster components. The precision of the impulse/thrust predictions for any specified conditions must also be well defined. In order to meet these requirements, the third objective of this topics is a thrust stand with full 6 degree of freedom (6-DOF) that can measure the full range of impulses, from short (15ms) to steady-state thrust, will be required.

Full 6-DOF thrust stands have been designed and used to characterize solid rocket motors with thrust levels (~ 10 kN) that are typically significantly higher than the desired capability.1,2 Off-axis impulse measurements for small pulsed plasma thrusters have been conducted, but usually by mounting the thruster in different orientations. The ability to measure impulsive and steady-state force and torque in all three dimensions for small, 1N class thrusters, would be a unique capability. The added complexity of making gas and electrical connections to the thruster/thrust stand while still maintaining the required measurement performance must also be satisfied.

The 6 DOF thrust stand must meet the following requirements:

· Measure axial steady-state thrust of up to 2N with an accuracy of 2% at maximum thrust.

· Measure axial impulse bit ranging from 15 mNs to 2Ns (1N for 15ms – 2s) with an accuracy of 2% or better.

· It is desirable, but not required, to be able to resolve the thrust profile during impulse firings with a time resolution of 1 ms or better and a thrust resolution of 10 mN or better.

· Measure any off-axis thrust components with a resolution of 1mN or better for steady-state thrust.

· Measure any off-axis impulse components with a resolution of 1mNs or better for impulsive firings ranging from 15 mNs to 2Ns (1N for 15ms – 2s).

· Measure any steady-state torques (3-axis) produced by the steady-state firing of the thruster with a resolution of 0.01Nm or better.

· Measure any angular impulse (3-axis) produced by the impulsive firing of the thruster for 15ms to 2s with a resolution of .01mNms or better.

References:

Luper, A., “Data Reduction for a Small Solid Rocket Motor Test by Fast Fourier Transform,” AIAA-93-1866, 29th Joint Propulsion Conference, Monterey, CA, June 28-30, (1993).

Brimhall, Z.,M., Divitotawela, N., “Design and Validation of a Six Degree of Freedom Rocket Motor Test Stand,” AIAA 2008-5051, 44th Joint Propulsion Conference, Hartford, CT, July 21-13, (2008).

Arrington, L.,A., Haag, T.,W., “Multi-Axis Thrust Measurements of the EO-1 Pulsed Plasma Thruster,” AIAA 99-2290, 35th Joint Propulsion Conference, Los Angeles, CA, June 20-24, (1999).

Other Requirements

Export Control: Information involved in this research effort may be subject to Export Control (International Traffic in Arm Regulation (ITAR) 22 CFR 120-131, or Export Administration Regulations (EAR) 15 CFR 710-774). If effort is subject to export control then a Certified DD Form 2345, Militarily Critical Technical Data Agreement, will be required to be submitted with proposal.

Export-Controlled Items: As prescribed by DFARS 204.7304, DFARS 252.204-7008, Export-Controlled Item (APR 2010) is contained in this BAA announcement (as shown below). This clause shall be contained in ALL BAA announcements and resulting contracts.

Definition. “Export-controlled items,” as used in this clause, means items subject to the Export Administration Regulations (EAR) (15 CFR Parts 730-774) or the International Traffic in Arms Regulations (ITAR) (22 CFR Parts 120-130). The term includes:

“Defense items,” defined in the Arms Export Control Act, 22 U.S.C. 2778(j)(4)(A), as defense articles, defense services, and related technical data, and further defined in the ITAR, 22 CFR Part 120.

“Items,” defined in the EAR as “commodities”, “software”, and “technology,” terms that are also defined in the EAR, 15 CFR 772.1

The Contractor shall comply with all applicable laws and regulations regarding export-controlled items, including, but not limited to, the requirement for contractors to register with the Department of State in accordance with the ITAR. The Contractor shall consult with the Department of State regarding any questions relating to compliance with the ITAR and shall consult with the Department of Commerce regarding any questions relating to compliance with the EAR.

The Contractor’s responsibility to comply with all applicable laws and regulations regarding export-controlled items exists independent of, and is not established or limited by, the information provided by this clause.

Nothing in the terms of this contract adds, changes, supersedes, or waives any of the requirements of applicable Federal laws, Executive orders, and regulations, including but not limited to –

The Export Administration Act of 1979, as amended (50 U.S.C. App.2401, et seq.);

The Arms Export Control Act (22 U.S.C. 2751, et seq.);

The International Emergency Economic Powers Act (50 U.S.C. 1701, et seq.);

The Export Administrative Regulations (15 CFR Parts 730-774);

The International Traffic in Arms Regulations (22 CFR Parts 120-130); and Executive Order 13222, as extended;

The Contractor shall include the substance of this clause, including this paragraph (e), in all subcontracts. (End of Clause)

Other Information

Government Furnished Property (GFP) availability: GFP is not anticipated to be made available under any resulting contract.

Data Rights Desired: The Government anticipates receiving, as a minimum, “Government Purpose Rights” to technical data developed under contracts awarded based on proposals received in response to this announcement.

The Air Force Research Laboratory is engaged in the discovery, development, and integration of warfighting technologies for our air, space, and cyberspace forces. As such, rights in technical data developed or delivered under this contract are of significant concern to the Government. The Government will therefore carefully consider any restrictions on the use of technical data which could result in transition difficulty or less than full and open competition for subsequent development of this technology.

In exchange for paying for development of the data, the Government expects technical data developed entirely at Government expense to be delivered with Unlimited Rights.

Technical data developed with mixed funding are expected to be delivered with at least Government Purpose Rights. Offers that propose delivery of technical data subject to Government Purpose Rights should fully explain what technical data developed with costs charged to indirect pools and/or costs not allocated to a Government contract will be incorporated, how the incorporation will benefit the program, and address whether those portions or processes are segregable. The Government expects that delivery of technical data subject to Government Purpose Rights will fully meet program needs.

Offers that propose delivery of technical data subject to Limited Rights, Restricted Rights, or Specifically Negotiated License Rights will be considered. Proposals should fully explain what technical data developed with costs charged to indirect pools and/or costs not allocated to a government contract will be incorporated and how the incorporation will benefit the program.

Offerors are reminded that the Identification and Assertion of Restrictions on the Government’s Use, Release, or Disclosure of Technical Data or Computer Software (the assertions list), required under DFARS 252.227-7013 and DFARS 252.227-7014, is included in Section K (Attachment 1) and due at time of proposals. Assertions must be completed with specificity with regard to each item, component, or process listed. Nonconforming assertions lists will be rejected.

Note that DFARS 252.227-7014(d) describes requirements for incorporation of third party computer software (commercial and noncommercial). Any commercial software to be incorporated into a deliverable must be clearly identified in the proposal. Because many commercial software licenses are not transferrable or may not be acceptable to the Government, commercial software licenses proposed for delivery to the Government must be approved by the Contracting Officer prior to award.

As used in this subparagraph, the terms Unlimited Rights, Government Purpose Rights, Specifically Negotiated License Rights, and Limited Rights in technical data are as defined in DFARS 252.227-7013. The terms Unlimited Rights, Government Purpose Rights, Specifically Negotiated License Rights, and Restricted Rights in noncommercial computer software and noncommercial software documentation are as defined in DFARS 252.227-7014. The term Commercial Computer Software is as defined in DFARS 252.227-7014.

Note that DFARS 227.7103-12(b) describes unjustified markings:

An unjustified marking is an authorized marking that does not depict accurately restrictions applicable to the Government’s use, modification, reproduction, release, performance, display, or disclosure of the marked technical data.

Contracting officers have the right to review and challenge the validity of unjustified markings. However, at any time during performance of a contract and notwithstanding existence of a challenge, the contracting officer and the person who has asserted a restrictive marking may agree that the restrictive marking is not justified. Upon such agreement, the contracting officer may, at his or her election, either –

Strike or correct the unjustified marking at that person’s expense; or

Return the technical data to the person asserting the restriction for correction at that person’s expense. If the data are returned and that person fails to correct or strike the unjustified marking and return the corrected data to the contracting officer within 60 days following receipt of the data, the unjustified marking shall be corrected or stricken at that person’s expense.

Award Information

Anticipated Funding

Cost of the overall CoNGAS program across all awards is estimated not to exceed $9.7M. This funding level is an estimate only and is not a contractual obligation for funding. All funding is subject to change due to Government discretion and availability. All potential offerors should be aware that due to unanticipated budget fluctuations, funding may change with little or no notice. BAA-RVKV-2015-0001 and BAA-RVKV-2015-0001A, Special Notice to Small Businesses (not a set-aside) share a common funding pool.

Anticipated Number of Awards

The Air Force anticipates awarding a minimum of three (3) contracts. However, the Air Force reserves the right to award all, some, or none of the efforts under BAA-RVKV-2015-0001 and BAA-RVKV-2015-0001A, Special Notice to Small Businesses (not a set-aside). The total number of awardees will be chosen from the proposals received in response to BAA-RVKV-2015-0001 and/or BAA-RVKV-2015-0001A, Special Notice to Small Businesses (not a set-aside).

Anticipated Contract Type

It is…