Los Altos, CA, United States

Optimal Synthesis, Inc.

www.optisyn.com
Los Altos, CA, United States
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Grant
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 749.69K | Year: 2016

The objective of the Phase II work is to develop a generic, advanced Flight Management System (FMS) for the evaluation of autonomous 4D-trajectory based operations (4DTBO) concepts. The work will address the following limitations of most commercially available FMS: they have limited advanced features; are specific to a single aircraft type; and cannot be readily modified by researchers. The proposed research will identify and extend advanced FMS features for the simulation evaluation of 4DTBO concepts in different phases of flight, based on the feasibility demonstration during Phase I work. Some of proposed feature include (i) advanced 4D guidance modes such as Required Time of Arrival (RTA), 4DFMS, and Interval Management (IM), (ii) high-fidelity wind modeling and wind update capability for improved predictability, (iii) trajectory negotiation, (iv) optimal 4D trajectory planning. Phase II work will develop a generic FMS interface to NASA's Multi-Aircraft Control System (MACS) to enable the evaluation of FMS modules from multiple vendors in 4DTBO simulations. The proposed FMS platform and the generic FMS interface will allow the users to deploy a wide array of autonomy enabling FMS features through a Graphical User Interface. All the algorithms and software developed under this research will be delivered to NASA at the end of the project.


Grant
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 125.00K | Year: 2016

To accelerate the acceptance of new concepts developed under NextGen, the Shadow Mode Assessment using Realistic Technologies for the National Airspace System (SMART-NAS) testbed, which enables integrated examinations of NextGen or beyond-NextGen concepts under distributed environment, becomes critical to the Air Traffic Management (ATM) community. To support human-in-the-loop (HITL) testing for NAS-wide simulation using SMART-NAS testbed, this proposal addresses the feasibility of constructing an ATM-centric speech-enabled agent as a plug-and-play service of the SMART-NAS testbed. This service addresses the gap of HITL testing that is currently limited to small regions of airspace and few airports with a small number of controllers and pseudo-pilots. Leveraged from our prior development on noise-robust speech recognition system for the Navy and virtual agents for NASA to support HITL simulations, an infrastructure of ATM-centric speech-enabled agent will be developed. A feasibility demonstration of the speech agent as a service component of the SMART-NAS testbed will be provided by the end of the Phase I research. Phase II work will utilize the infrastructure built in Phase I to expand the speech-enabled agent to a full-scale prototype that supports HITL testing for NAS-wide simulation using the SMART-NAS testbed.


Grant
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 750.00K | Year: 2016

The goal of this research is to create a suite of tools for monitoring airport gate activities with the objective of improving aircraft turnaround prediction. Airport ramp areas are the most crowded and cluttered spaces in the entire National Airspace System (NAS). Operations associated with turnaround of the aircraft from the gate represent a significant source of delay and therefore impact the predictability of NAS operations. The computer-vision-based Gate Activity Monitoring TOol Suite (GAMTOS) will specifically identify the various stages of turnaround such as refueling, baggage handling, and deicing. It will further employ a probabilistic model of the times associated with each of these events, that will be used for predicting the future sequence of events and their predicted times of completion. We seek to leverage our expertise in monitoring aircraft using the Vision BAsed Surveillance System (VBASS) currently being developed under a Phase III SBIR research from NASA Ames Research Center. At the end of Phase II, the GAMTOS software is expected to operate in two different modes. The first mode is an offline mode, which generates a database of gate activities, their timings, and their sequence. The second mode is a real-time mode which involves continuous monitoring of activities and prediction of future activities.


Grant
Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase I | Award Amount: 79.95K | Year: 2015

Military operations and logistics are inextricably linked. The importance of In-Transit Visibility (ITV) capability for reliable tracking and delivery of logistical items has well been recognized across Department of Defense (DoD) logistics programs. The current state-of-the-art solution to ITV employs a Radio-Frequency Identification (RFID) network, which comprises a large number of read-and-write stations and hence requires high cost and intensive manpower. Motivated by past experiences on air traffic management and machine learning, Optimal Synthesis Inc. (OSI) proposes an alternative approach that exploits existing variety of data sources, a good example of which is the IDE/GTN Convergence (IGC) that is a unified data service across DoD cargo movement and tracking. The approach combines machine learning techniques with the estimation and prediction methodologies for tracking the cargo movement and predicting the time-of-arrival in each mission leg. By performing on-line risk monitoring, a decision support tool that generates automated alerts for human intervention is also developed using a stochastic decision framework. The proof-of-concept demonstration is planned in Phase I, and the software prototype is planned to be developed in Phase II for functional demonstration.


Grant
Agency: Department of Defense | Branch: Missile Defense Agency | Program: SBIR | Phase: Phase I | Award Amount: 100.00K | Year: 2015

The sensor models to be employed in the proposed research will improve tracking performance. An implementation of the technology on High-Performance Computers is proposed in order to address the computational complexity. Phase I research will demonstrate benchmark simulations on a GPU and MIC-equipped computer. (Approved for Public Release 15-MDA-8482 (17 November 15))


Grant
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 125.00K | Year: 2015

Optimal Synthesis Inc. proposes to develop a formal verification and validation approach to small-scale Unmanned Aerial Vehicle (UAV) autopilots. The UAV autopilots are modeled as hybrid systems and further abstracted into a finite state machine to which a computational model checking tool is applied to verify the safety property of the autopilot. The abstraction is performed by rechability computation. While traditional reachability computation has been limited to low-dimensional systems, the abstraction approach developed by Purduer University approximates the hybrid system and exhibit significant improvement in computational efficiency. This forms the basis for onboard model-checking for safety. The proof of concept is planned to be demonstrated in the Phase I using simulation studies, and ensuring hardware-in-the-loop simulation and flight demonstration are planned in the Phase II research.


Grant
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 125.00K | Year: 2015

The objective of this research is to create a suite of tools for monitoring airport gate activities with the objective of improving aircraft turnaround. Airport ramp areas are the most crowded and cluttered spaces in the entire National Airspace System (NAS). Activities related to turnaround of the aircraft from the gate represent a significant source of delay and therefore impact the predictability of NAS operations. Optimal Synthesis Inc., seeks to leverage its expertise in monitoring aircraft in the ramp areas using video surveillance data and advanced computer vision algorithms towards building an advanced gate activity monitoring that will in turn enable a gate turnaround prediction tool. The tool suite will specifically identify the various stages of turnaround such as refueling, luggage unloading/loading, catering, and deicing. It will further create a probabilistic model of the times associated with each of these events, that will be used for predicting the future sequence of events and their predicted times of completion. Phase I research will demonstrate the core ideas of gate activity recognition using state-of-the-art computer vision and machine learning algorithms. Phase II research will elevate the technology readiness level of this tool suite to work with real-time video surveillance streams.


Grant
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 125.00K | Year: 2015

The concepts of Virtual Towers and Autonomous Airport Operations emerged as cost-effective options in early conceptualization of the Next-Generation Air Transportation System (NextGen) for relieving traffic demand at major airports by providing control tower services at nearby uncontrolled airports. These concepts have the benefit of saving the tower construction cost and the cost for otherwise staffing the towers with a full cadre of controllers. More recently, the threat of sequestration forced the FAA to announce closure of 149 airport towers; though the closure plan was rescinded, these concepts again appear as viable alternatives for providing control-tower services at reduced costs. Virtual Towers and Autonomous Airport Operations are in fact related concepts. On one hand, Virtual Towers depend on automation to allow a small crew of controllers to manage traffic at multiple airports, and the increase in automation moves the concept towards Autonomous Airport Operations as automation becomes more capable. On the other hand, Autonomous Airport Operations should have controllers available as a fall-back option in a Virtual Tower environment to ensure safety when abnormal conditions emerge. The proposed research seeks to develop practical concepts for Autonomous Airport Operations, and apply state-of-the-art automation technologies to enable such operations. The automation technologies include a computer-based ATC agent that can monitor and plan traffic movement, issue clearances, and communicate with the pilots over the radio using advanced speech processing technologies. In addition, low-cost surveillance systems based on machine vision will be explored to provide the necessary traffic information around the airport and on the airport surface.


Grant
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 750.00K | Year: 2014

Based upon the feasibility demonstrated in the Phase I research, Optimal Synthesis Inc.(OSI) proposes to develop a software tool that can be used validate aircraft flight deck user interfaces over the entire flight envelope. The approach is based on a mathematical formalism derived from hybrid systems theory. The correctness of information content in user interfaces is analyzed by a special observability test that takes into account of the limitations in human cognition and psychology. A possible mismatch between an operational mode perceived by the human operator and the one active in the aircraft is detected using an algorithm that compares the inferred intent of the human operator to that of the machine. Metrics-based performance evaluation will be carried out to demonstrate the benefits of the prototype software developed under the Phase II research. The feasibility of employing the software on the flight deck as a real-time pilot aid will also be analyzed in the Phase II research.


Grant
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 125.00K | Year: 2015

The objective of this research is to create a generic advanced Flight Management System (FMS) platform that could be used for evaluation of autonomous trajectory-based operation concepts. The research addresses the following deficiencies: most FMSs have limited advanced features; are specific to a single aircraft type; expensive and protected by FMS manufacturers. The proposed FMS platform will enable users to deploy a wide array of autonomy enabling FMS features by the click of a button. Some of the proposed features include: (i) air-ground & inter-aircraft trajectory negotiation, (ii) 4D Trajectory-Based Operations (4DTBO), (iii) high-fidelity wind modeling for improved predictability, (iii) trajectory planning options based on environmental and efficiency considerations, and (iv) advanced guidance modes such as Required Time of Arrival (RTA) and 4DFMS. A key feature of the proposed research is the integration of this platform and its features with NASA's Multi-AirCraft Simulation (MACS) platform. Phase I research will identify the complete array of features for possible inclusion in this platform. Moreover, Phase I will demonstrate select features through the interface to MACS. Phase II research will elevate the technology readiness level suitable for deployment in Human-In-The-Loop simulation pilot stations.

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