Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 749.99K | Year: 2015
In Phase I of this project, SSCI carried out initial development of the Generalized Guidance, Navigation & Control Architecture for Reusable Development (GUARD). The resulting framework is applicable across different Autonomous Rendezvous and Docking (AR&D) domains, and enables further development and testing of reusable GN&C software for such applications. GUARD is based on the key functional requirements for GN&C software for AR&D, with special emphasis on the commonality across different domains of operation and unique implementation requirements for GN&C algorithms in such domains. Phase I accomplishments include: (i) Augmented the flight-test proven on-line trajectory optimization and control algorithm with a Fault Detection, Identification and Accommodation (FDIA) capability, (ii) Extended SSCI's Vision Based Navigation (VBN) algorithms, recently demonstrated for shipboard landing flight experiments, to achieve centimeter-level positioning accuracy for the AR&D implementations, and demonstrated its robustness to docking pattern variations; (iii) Carried out a detailed study of common GN&C functions for AR&D, and developed a conceptual solution for a user interface enabling agile reconfiguration of domain-specific information; and (iv) Carried out initial analysis of System-level Performance Metrics for AR&D missions to facilitate V&V of the overall integrated GN&C system. Phase II will demonstrate an enhanced prototype of the GUARD with integrated GNC/FDIA/VBN software that will make it reusable in three disparate AR&D system domains. Demonstrations will be in simulation and hardware tests as follows: orbital AR&D (in simulation), planetary rover docking with a habitat (evaluations at Olin College on R-Gator platform), and a quadrotor close-proximity operation mission (evaluations at UT Austin on quadrotor platform). Phase III will focus on commercialization of the GUARD software and its implementation to future NASA Space Exploration missions.
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 749.79K | Year: 2015
The Variable Camber Continuous Trailing Edge Flap (VCCTEF) concept offers potential improvements in the aerodynamic efficiency of aircraft through real time wing shaping. NASA and Boeing have been studying the suitability of this concept to address the drag reduction problem in aircraft with reduced-stiffness wings. However, reduced stiffness may lead to wing flutter. In addition, displacements of VCCTEF control surfaces are limited and subject to highly nonlinear and time-varying constraints. Hence control design needs to solve a constrained multi-objective optimization problem. To address these challenges, in Phase I SSCI carried out initial development and testing of the Drag Identification and Reduction Technology (DIRECT). The DIRECT software estimates wing structural modes on-line and uses that information in a robust predictive controller design. Based on using Evolutionary Optimization and off-line analysis, DIRECT estimates wind disturbances on-line and selects optimal controller parameters from a table lookup to achieve on-line drag minimization. Building upon the successful Phase I development, in Phase II we propose to extend the DIRECT approach and evaluate its performance through high-fidelity simulations and wind-tunnel testing. Specific Phase II tasks include: (i) Test Phase I flutter suppression algorithms and PSC algorithms in a GTM simulation with flexible modes and VCCTEF control surfaces; (ii) Extend and enhance the drag minimization approach by developing innovative Performance Seeking Control (PSC) algorithms; and (iii) Compare the features of PSC and other available performance seeking control algorithms through wind-tunnel testing at the University of Washington Aerodynamics Laboratory (UWAL). Professor Eli Livne of University of Washington and Mr. James Urnes, Sr. will provide technical support under the project. Phase III will focus on commercialization of SYMPTOM software to manned aircraft and UAS.
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 749.89K | Year: 2015
SSCI has developed a suite of SAA tools and an analysis capability referred to as ASPECT (Automated System-level Performance Evaluation and Characterization Tool). ASPECT encapsulates our airspace encounter generator, sensor/tracker fusion algorithms, and prediction, threat assessment, and avoidance modules. It also provides both component-level and system-level analysis that is required for evaluating how well SAA sensors and software meet fundamental safety requirements for UAS in the NAS. ASPECT consists of MESSENGER (Multi-aircraft Encounter Scenario Generator), ASSIST (AsynchronouS Sensor fusIon SysTem), FORECAST (Fast On-line Prediction of Aircraft State Trajectories), and REACT (Rapid Encounter Avoidance & Conflict Resolution) modules. Initial versions of FORECAST and REACT were designed under related projects. Phase I developed the ASSIST (Asynchronous Sensor Fusion System) capability, which fuses combinations of SAA sensors such as GRB, ABR, camera, and Mode C transponder for localizing non-communicating threats. ASPECT was then used to analyze ASSIST's estimation accuracy, with the objective of achieving the precision of ADS-B and rejecting spurious/clutter tracks. Phase II will: (i) Expand and validate the underlying sensor models and demonstrate capability using flight test data generated at Olin College (Needham, MA), (ii) Extend our REACT system, and (iii) Carry out SAA system-level analyses using ASPECT to illustrate the relationship between sensor suite (hardware) selection, component SAA software modules, and achievable safety performance of the integrated system. The result of Phase II efforts will be a complete flow-down error and risk analysis framework, which constitutes a major step toward the integration of UAS into the National Airspace System. Phase II plans have been reviewed by NASA's UAS Traffic Management Program and AeroVironment (letters of support attached), who we anticipate to be one of our early transition partners.
Agency: Department of Defense | Branch: Defense Advanced Research Projects Agency | Program: SBIR | Phase: Phase II | Award Amount: 986.09K | Year: 2013
SSCI proposes a retooling of the Intelligent Course of Action Learning System (iCOALS) that leverages the important results from the Phase I effort and remedies the deficiencies of a rule-based approach when applied to complex Pursuit Evasion Games (PEGs). This modification is critical given the utility of the proposed approach to DARPA's Anti-Submarine Warfare (ASW) Continuous Trail Unmanned Vessel (ACTUV) program. ACTUV's"Track-and-Trail"problem is specific, real world instance of a PEG where ACTUV attempts to maintain close in sensor contact over a period of weeks with a manned diesel-electric submarine. This problem can be formulated as a PEG where the unmanned pursuit vehicle (ACTUV: propulsive superiority) attempts to maintain proximity with the evader (submarine: intellectual superiority). SSCI's specific approach to this problem provides an online Adversarial Autonomy (AA) engine that employs a Forward Reachability Model (FRM) formulation of a PEG game. This is important because it is impossible to formulate a rule-set that can handle all challenges from an intelligent adversary especially one with access to external effectors (surface traffic, active/passive decoys, weapons systems, boarding parties, etc.). Game-theoretic approaches, like FRM, model key performance parameters of the pursuer (ACTUV) as well as modeling or measuring those of the evader. Once modeled, efficient computational search algorithms (alpha-beta) are employed to ensure optimal outcomes for the current engagement configuration in real-time. It should be noted that engagement-level optimality is precluded in a rule-based approach with a finite rule set. Upgrading iCOALS with online adversarial models will eliminate the possibility track-and-trail will be easily broken by an intelligent adversary who quickly discovers and repeatedly exploits rule-set loopholes.
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase I | Award Amount: 147.87K | Year: 2014
With the proliferation of cheap sensors, reduction in storage costs and the ubiquity of communication networks, Cyber-Physical Systems are collecting and storing data at an unprecedented rate. Analysis of such large databases is necessary to find relevant information and improve the efficiency of the Cyber Physical System. The goal of an analysis tool, simply put is to find the most interesting information in the data and present it to the user in most intuitive and clear manner possible by effectively mapping the information to visual cues. In response to this need, SSCI is proposing the development of ViA-ML, a visualization assistant with an interest-driven machine learning back-end to allow users to interactively extract information from large datasets collected by cyber physical systems. Our proposed approach is based on providing analysts with visualizations that maximize view comprehension, using pyschophysics based criteria, of the raw data attributes and attributes derived from automated analysis. Maximizing viewer comprehension then allows us to quickly gauge user interest, iterate through competing hypotheses by our novel machine learning algorithms and further enhance the visualizations by incorporating user knowledge and requirements.