There is an increasing need to fly Unmanned Aircraft Systems (UAS) in the National Airspace System (NAS) to perform missions of vital importance to national security and defense, emergency management, science, and to enable commercial applications. However, routine access by UAS to the NAS remains unrealized. 

The UAS community needs routine access to the global airspace for all classes of UAS. Based on this need, NASA's UAS Integration in the NAS Project identified the following goal: To provide research findings to reduce technical barriers associated with integrating UAS into the NAS utilizing integrated system level tests in a relevant environment. These barriers include: a lack of sense-and-avoid concepts and technologies that can operate within the NAS, robust communication technologies, robust human systems integration, and a relevant environment for use in testing the developed technologies.

The project's goal will be accomplished by developing system-level integration of key concepts, technologies and/or procedures, as well as demonstrating those integrated capabilities in an operationally relevant environment. 

The project conducts research to address technical barriers in the following areas:

• Sense and Avoid (SAA) [synonymous with Detect and Avoid (DAA)] Performance Standards: Provide research findings to develop and validate UAS Minimum Operational Performance Standards (MOPS) for SAA performance and interoperability.
• Command and Control (C2) Performance Standards: Provide research findings to develop and validate UAS MOPS for terrestrial C2 communication.
• Human Systems Integration (HSI): Provide research findings to develop and validate HSI ground control station (GCS) guidelines enabling implementation of the SAA and C2 performance standards.
• Integrated Test and Evaluation (IT&E): Develop a relevant test environment that is a live virtual constructive (LVC) distributed environment (DE), for use in generating research findings to develop and validate HSI guidelines, DAA, and C2 MOPS with test scenarios supporting integration of UAS into the NAS.

These activities support research within the aeronautics strategic thrust area 6. 

Rover missions typically involve visiting a set of predetermined waypoints to perform science functions, such as sample collection. Given the communication delay between Earth and the rover, and the possible occurrence of faults, an autonomous decision making system is essential to ensure that the rover maximizes the scientific operations performed without damaging itself further or stalling. This paper presents a modular software architecture for autonomous decision making for rover operations that uses diagnostic and prognostic information to influence mission planning and decision making to maximize the completion of mission objectives. The decision making system consists of separate modules that perform the functions of control, diagnosis, prognosis, and decision making.We demonstrate our implementation of this architecture on a simulated rover testbed.

Modern space propulsion and exploration system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. Inductive Monitoring System uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or for analysis of archived events. Ongoing and developing Inductive Monitoring System space operations applications include International Space Station flight control, spacecraft vehicle system health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the Inductive Monitoring System data-driven technique as related to several Integrated Systems Health Management elements. Thematically, the projects listed will be used as case studies. The maturation of Inductive Monitoring System via projects where it has been deployed or is currently being integrated to aid in fault detection will be described. The paper will also explain how Inductive Monitoring System can be used to complement a suite of other Integrated System Health Management tools, providing initial fault detection support for diagnosis and recovery.

Future NASA plans call for long-duration deep space missions with human crews. Because of light-time delay and other considerations, increased autonomy will be needed. This will necessitate integration of tools in such areas as anomaly detection, diagnosis, planning, and execution. In this paper we investigate an approach that integrates planning and execution by embedding planner-derived temporal constraints in an execution procedure. To avoid the need for propagation, we convert the temporal constraints to dispatchable form. We handle some uncertainty in the durations without it affecting the execution; larger variations may cause activities to be skipped.

Actuator systems are employed widely in aerospace, transportation and industrial processes to provide power to critical loads, such as aircraft control surfaces. They must operate reliably and accurately in order for the vehicle / process to complete successfully its designated mission. Incipient actuator failure conditions may severely endanger the operational integrity of the vehicle / process and compromise its mission. The ability to maintain a stable and credible operation, even in the presence of incipient failures, is of paramount importance to accomplish “must achieve” mission objectives. This paper introduces a novel methodology for the fault-tolerant design of critical subsystems, such as an ElectroMechanical Actuator (EMA), that takes advantage of on-line, real-time estimates of the Remaining Useful Life (RUL) or Time-to-Failure (TTF) of a failing component and reconfigures the available control authority by trading off system performance with control activity. The primary goal is to complete critical mission objectives within a time window dictated by prognostic algorithms so that the fault mode is accommodated and an acceptable level of performance maintained for the duration of the mission. The proposed fault-tolerant control design is mathematically rigorous, generic and applicable to a variety of application domains. An EMA is used to illustrate the efficacy of the proposed approach.

This element involves the development of software that enables easier commanding of a wide range of NASA relevant robots through the Robot Application Programming Interface Delegate (RAPID) robot messaging system and infusing the developed software into flight projects.  In June and July of 2013, RAPID was tested on ISS as the robot messaging software for the Technology Demonstration Mission (TDM) Human Exploration Telerobotics (HET) Surface Telerobotics experiment.  RAPID has also been made available to — and integrated with — the Robot Operating System (ROS), a popular software framework for developing state-of-the-art robots for ground and space. While ROS powers a number of new robots and components such as Robonaut 2’s climbing legs and R5, the addition of RAPID allows these robots to interoperate in collaborative human-robot teams, safely and effectively over time-delayed communications links. The objective this year is to take this space-tested software and extend it to providing video streaming from remote robots and delivering this new capability to the Exploration Ground Data Systems (xGDS) area within HRS.  xGDS will then deliver its software to Science Mission Directorate (SMD) funded field tests to improve the technology readiness moving leading (potentially) to being used for the Lunar Prospector Mission ground data systems.  Success will involve delivering RAPID to xGDS and then xGDS supporting SMD field test.

The team is also developing algorithms for sensors capable of reconstructing remote worlds and efficiently shipping that remote environment back to earth using the RAPID robot messaging system.  This type of system could eventually lead to scientists on earth gain new insights as they are able to step into the remote world.  This sensor also has the ability to engage the public, bringing remote worlds back to earth.  During FY13, this task used science operations personnel from current SMD projects to objectively measure improvement in remote science target selection and decision-making based. The team continues to work with SMD projects to ensure that the technologies being developed are directly responsive to SMD project personnel needs. The objective of this work in FY14 is to expand the range of science operations tasks addressed by the technology, and to perform laboratory demonstrations for JPL/SMD stakeholders of the immersive visualization of data from a sensor using an SMD representative environment.

During 2014, the “Controlling Robots Over Time Delay” project element will develop two technologies:

• Develop RAPID robot messaging for unified cross-center operations platform for TDM, xGDS, and CCSDS
• Sensor Systems for the Construction of Immersive Virtual Environments

The ability to utilize prognostic system health information in operational decision making, especially when fused with information about future operational, environmental, and mission requirements, is becoming desirable for both manned and unmanned aerospace vehicles. A vehicle capable of evaluating its own health state and making (or assisting the crew in making) decisions with respect to its system health evolution over time will be able to go further and accomplish more mission objectives than a vehicle fully dependent on human control. This paper describes the development of a hardware testbed for integration and testing of prognostics-enabled decision making technologies. Although the testbed is based on a planetary rover platform (K11), the algorithms being developed on it are expected to be applicable to a variety of aerospace vehicle types, from unmanned aerial vehicles and deep space probes to manned aircraft and spacecraft. A variety of injectable fault modes is being investigated for electrical, mechanical, and power subsystems of the testbed. A software simulator of the K11 has been developed, for both nominal and off-nominal operating modes, which allows prototyping and validation of algorithms prior to their deployment on hardware. The simulator can also aid in the decision-making process. The testbed is designed to have interfaces that allow reasoning software to be integrated and tested quickly, making it possible to evaluate and compare algorithms of various types and from different sources. Currently, algorithms developed (or being developed) at NASA Ames - a diagnostic system, a prognostic system, a decision-making module, a planner, and an executive - are being used to complete the software architecture and validate design of the testbed.

Unmanned aircraft systems (UAS) can be used for scientific, emergency management, and defense missions, among others. The existing federal air regulations, procedures, and technologies do not allow routine UAS access to the National Airspace System (NAS), with the UAS being flown primarily within restricted airspaces. The current Certificate of Waiver of Authorization (COA) process requirement for UAS operations in the NAS are extremely resource intensive, lengthy, and often lacks the flexibility to meet the full mission needs. As the number of UAS operations increases, new methodologies will be needed to enable their safe and routine access to both restricted and unrestricted airspace in the NAS. This paper focuses on gaining a better understanding of growth of NAS usage in near-term NAS UAS demand in that airspace, and an assessment of the impact of unrestricted UAS deployment in the NAS that may facilitate the development of enabling methodologies. Using software simulations for demand growth generation and NAS operations the impact of UAS integration into the NextGen NAS is simulated to analyze its impact on the delay, congestion, loss of separation conflicts, fuel burn, and noise level. Our analyses show that while there is a slight increase in these factors due to additional UAS flight, this increase is minimal compared to the levels caused by the increase of commercial traffic alone.

As fault diagnosis and prognosis systems in aerospace applications become more capable, the ability to utilize information supplied by them becomes increasingly important. While certain types of vehicle health data can be effectively processed and acted upon by crew or support personnel, others, due to their complexity or time constraints, require either automated or semi-automated reasoning. Prognostics-enabled Decision Making (PDM) is an emerging research area that aims to integrate prognostic health information and knowledge about the future operating conditions into the process of selecting subsequent actions for the system. The newly developed PDM algorithms require suitable software and hardware platforms for testing under realistic fault scenarios. The paper describes the development of such a platform, based on the K11 planetary rover prototype. A variety of injectable fault modes are being investigated for electrical, mechanical, and power subsystems of the testbed, along with methods for data collection and processing. In addition to the hardware platform, a software simulator with matching capabilities has been developed. The simulator allows for prototyping and initial validation of the algorithms prior to their deployment on the K11. The simulator is also available to the PDM algorithms to assist with the reasoning process. A reference set of diagnostic, prognostic, and decision making algorithms is also described, followed by an overview of the current test scenarios and the results of their execution on the simulator.