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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Verification of Autonomous Systems for Space Applications

Autonomous software, especially if it is based on model, can play an important role in future space applications. For example, it can help streamline ground operations, or, assist in autonomous rendezvous and docking operations, or even, help recover from problems (e.g., planners can be used to explore the space of recovery actions for a power subsystem and implement a solution without (or with minimal) human intervention). In general, the exploration capabilities of model-based systems give them great flexibility. Unfortunately, it also makes them unpredictable to our human eyes, both in terms of their execution and their verification. The traditional verification techniques are inadequate for these systems since they are mostly based on testing, which implies a very limited exploration of their behavioral space. In our work, we explore how advanced V&V techniques, such as static analysis, model checking, and compositional verification, can be used to gain trust in model-based systems. We also describe how synthesis can be used in the context of system reconfiguration and in the context of verification.

Brat, G.↗
NASA NTRS2013

Combustion Stability of the Gas Generator Assembly from J-2X Engine E10001 and Powerpack Tests

Testing of a powerpack configuration (turbomachinery and gas generator assembly) and the first complete engine system of the liquid oxygen/liquid hydrogen propellant J-2X rocket engine have been completed at the NASA Stennis Space Center. The combustion stability characteristics of the gas generator assemblies on these two systems are of interest for reporting since considerable effort was expended to eliminate combustion instability during early development of the gas generator assembly with workhorse hardware. Comparing the final workhorse gas generator assembly development test data to the powerpack and engine system test data provides an opportunity to investigate how the nearly identical configurations of gas generator assemblies operate with two very different propellant supply systems one the autonomous pressure-fed test configuration on the workhorse development test stand, the other the pump-fed configurations on the powerpack and engine systems. The development of the gas generator assembly and the elimination of the combustion instability on the pressure-fed workhorse test stand have been reported extensively in the two previous Liquid Propulsion Subcommittee meetings 1-7. The powerpack and engine system testing have been conducted from mid-2011 through 2012. All tests of the powerpack and engine system gas generator systems to date have been stable. However, measureable dynamic behavior, similar to that observed on the pressure-fed test stand and reported in Ref. [6] and attributed to an injection-coupled response, has appeared in both powerpack and engine system tests. As discussed in Ref. [6], these injection-coupled responses are influenced by the interaction of the combustion chamber with a branch pipe in the hot gas duct that supplies gaseous helium to pre-spin the turbine during the start transient. This paper presents the powerpack and engine system gas generator test data, compares these data to the development test data, and provides additional combustion stability analyses of the configurations.

Hulka, J. R.↗
NASA NTRS2017

A Human-Autonomy Teaming Approach for a Flight-Following Task

Managing aircraft is becoming more complex with increasingly sophisticated automation responsible for more flight tasks. With this increased complexity, it is becoming more difficult for operators to understand what the automation is doing and why. Human involvement with increasingly autonomous systems must adjust to allow for a more dynamic relationship involving cooperation and teamwork. As part of an ongoing project to develop a framework for human-autonomy teaming (HAT) in aviation, a part-task study was conducted to demonstrate, evaluate and refine proposed critical aspects of HAT. These features were built into an automated recommender system on a ground station available from previous studies. Participants performed a flight-following task once with the original ground station (i.e., No HAT condition) and once with the HAT features enabled (i.e., HAT condition). Behavioral and subjective measures were collected; subjective measures are presented here. Overall, participants preferred the ground station with HAT features enabled compared to the station without the HAT features. Participants reported that the HAT displays and automation were preferred for keeping up with operationally important issues. Additionally, participants reported that the HAT displays and automation provided enough situation awareness to complete the task and reduced workload relative to the No HAT baseline.

ACFP↗
NASA NTRS2013

Compute Element and Interface Box for the Hazard Detection System

The Autonomous Landing and Hazard Avoidance Technology (ALHAT) program is building a sensor that enables a spacecraft to evaluate autonomously a potential landing area to generate a list of hazardous and safe landing sites. It will also provide navigation inputs relative to those safe sites. The Hazard Detection System Compute Element (HDS-CE) box combines a field-programmable gate array (FPGA) board for sensor integration and timing, with a multicore computer board for processing. The FPGA does system-level timing and data aggregation, and acts as a go-between, removing the real-time requirements from the processor and labeling events with a high resolution time. The processor manages the behavior of the system, controls the instruments connected to the HDS-CE, and services the "heavy lifting" computational requirements for analyzing the potential landing spots.

Villalpando, Carlos Y.↗
NASA NTRS2021

Autonomous Off-road Navigation over Extreme Terrains with Perceptually-challenging Conditions

We propose a framework for resilient autonomous navigation in perceptuallychallenging unknown environments with mobility-stressing elements such asuneven surfaces with rocks and boulders, steep slopes, negative obstacles like cliffsand holes, and narrow passages. Environments are GPS-denied and perceptuallydegradedwith variable lighting from dark to lit and obscurants (dust, fog, smoke).Lack of prior maps and degraded communication eliminates the possibility of prioror off-board computation or operator intervention. This necessitates real-time onboardcomputation using noisy sensor data. To address these challenges, we proposea resilient architecture that exploits redundancy and heterogeneity in sensing modalities.Further resilience is achieved by triggering recovery behaviors upon failure.We propose a fast settling algorithm to generate robust multi-fidelity traversabilityestimates in real-time. The proposed approach was deployed on multiple physicalsystems including skid-steer and tracked robots, high-speed RC car and legged robotsand as a part of Team CoSTAR’s effort to theDARPASubterranean Challenge, wherethe team won 2nd and 1st place in the Tunnel and Urban Circuit, respectively.

Agha-mohammadi, Ali-akbar↗
NASA NTRS1991

Navigation strategies for multiple autonomous mobile robots moving in formation

The problem of deriving navigation strategies for a fleet of autonomous mobile robots moving in formation is considered. Here, each robot is represented by a particle with a spherical effective spatial domain and a specified cone of visibility. The global motion of each robot in the world space is described by the equations of motion of the robot's center of mass. First, methods for formation generation are discussed. Then, simple navigation strategies for robots moving in formation are derived. A sufficient condition for the stability of a desired formation pattern for a fleet of robots each equipped with the navigation strategy based on nearest neighbor tracking is developed. The dynamic behavior of robot fleets consisting of three or more robots moving in formation in a plane is studied by means of computer simulation.

Wang, P. K. C.↗
NASA NTRS2005

Control Algorithms and Simulated Environment Developed and Tested for Multiagent Robotics for Autonomous Inspection of Propulsion Systems

The NASA Glenn Research Center and academic partners are developing advanced multiagent robotic control algorithms that will enable the autonomous inspection and repair of future propulsion systems. In this application, on-wing engine inspections will be performed autonomously by large groups of cooperative miniature robots that will traverse the surfaces of engine components to search for damage. The eventual goal is to replace manual engine inspections that require expensive and time-consuming full engine teardowns and allow the early detection of problems that would otherwise result in catastrophic component failures. As a preliminary step toward the long-term realization of a practical working system, researchers are developing the technology to implement a proof-of-concept testbed demonstration. In a multiagent system, the individual agents are generally programmed with relatively simple controllers that define a limited set of behaviors. However, these behaviors are designed in such a way that, through the localized interaction among individual agents and between the agents and the environment, they result in self-organized, emergent group behavior that can solve a given complex problem, such as cooperative inspection. One advantage to the multiagent approach is that it allows for robustness and fault tolerance through redundancy in task handling. In addition, the relatively simple agent controllers demand minimal computational capability, which in turn allows for greater miniaturization of the robotic agents.

Wong, Edmond↗
NASA NTRSDate not supplied

Runtime Monitoring for Unmanned Aerospace Systems with Neural Network Components

AI components (e.g., Deep Neural Networks) are increasingly used in unmanned Aerospace systems for safety-relevant applications. Rigorous Verification and Validation methods for such components are still in their infancy and thus, monitoring of the AI's behavior during runtime is essential. In this paper, we will present a runtime-monitoring architecture, which combines the advanced statistical analysis framework SYSAI (System Analysis using Statistical AI) with temporal and probabilistic runtime monitoring carried out by R2U2 (Realizable, Responsive, and Unobtrusive Unit). Learned statistical models of complex systems with AI components are produced by the SYSAI framework and provide detailed information to enable the R2U2 runtime monitor to efficiently perform advanced safety and performance checks in nominal and off-nominal conditions. We will present initial results of our tool set and architecture on a case study, a DNN-based autonomous centerline tracking system (ACT).

Yuning He↗
NASA NTRS2006

Biology Inspired Approach for Communal Behavior in Sensor Networks

Research in wireless sensor network technology has exploded in the last decade. Promises of complex and ubiquitous control of the physical environment by these networks open avenues for new kinds of science and business. Due to the small size and low cost of sensor devices, visionaries promise systems enabled by deployment of massive numbers of sensors working in concert. Although the reduction in size has been phenomenal it results in severe limitations on the computing, communicating, and power capabilities of these devices. Under these constraints, research efforts have concentrated on developing techniques for performing relatively simple tasks with minimal energy expense assuming some form of centralized control. Unfortunately, centralized control does not scale to massive size networks and execution of simple tasks in sparsely populated networks will not lead to the sophisticated applications predicted. These must be enabled by new techniques dependent on local and autonomous cooperation between sensors to effect global functions. As a step in that direction, in this work we detail a technique whereby a large population of sensors can attain a global goal using only local information and by making only local decisions without any form of centralized control.

Jones, Kennie H.↗
NASA NTRS2009

Cooperative Three-Robot System for Traversing Steep Slopes

Teamed Robots for Exploration and Science in Steep Areas (TRESSA) is a system of three autonomous mobile robots that cooperate with each other to enable scientific exploration of steep terrain (slope angles up to 90 ). Originally intended for use in exploring steep slopes on Mars that are not accessible to lone wheeled robots (Mars Exploration Rovers), TRESSA and systems like TRESSA could also be used on Earth for performing rescues on steep slopes and for exploring steep slopes that are too remote or too dangerous to be explored by humans. TRESSA is modeled on safe human climbing of steep slopes, two key features of which are teamwork and safety tethers. Two of the autonomous robots, denoted Anchorbots, remain at the top of a slope; the third robot, denoted the Cliffbot, traverses the slope. The Cliffbot drives over the cliff edge supported by tethers, which are payed out from the Anchorbots (see figure). The Anchorbots autonomously control the tension in the tethers to counter the gravitational force on the Cliffbot. The tethers are payed out and reeled in as needed, keeping the body of the Cliffbot oriented approximately parallel to the local terrain surface and preventing wheel slip by controlling the speed of descent or ascent, thereby enabling the Cliffbot to drive freely up, down, or across the slope. Due to the interactive nature of the three-robot system, the robots must be very tightly coupled. To provide for this tight coupling, the TRESSA software architecture is built on a combination of (1) the multi-robot layered behavior-coordination architecture reported in "An Architecture for Controlling Multiple Robots" (NPO-30345), NASA Tech Briefs, Vol. 28, No. 10 (October 2004), page 65, and (2) the real-time control architecture reported in "Robot Electronics Architecture" (NPO-41784), NASA Tech Briefs, Vol. 32, No. 1 (January 2008), page 28. The combination architecture makes it possible to keep the three robots synchronized and coordinated, to use data from all three robots for decision- making at each step, and to control the physical connections among the robots. In addition, TRESSA (as in prior systems that have utilized this architecture) , incorporates a capability for deterministic response to unanticipated situations from yet another architecture reported in Control Architecture for Robotic Agent Command and Sensing (NPO-43635), NASA Tech Briefs, Vol. 32, No. 10 (October 2008), page 40. Tether tension control is a major consideration in the design and operation of TRESSA. Tension is measured by force sensors connected to each tether at the Cliffbot. The direction of the tension (both azimuth and elevation) is also measured. The tension controller combines a controller to counter gravitational force and an optional velocity controller that anticipates the motion of the Cliffbot. The gravity controller estimates the slope angle from the inclination of the tethers. This angle and the weight of the Cliffbot determine the total tension needed to counteract the weight of the Cliffbot. The total needed tension is broken into components for each Anchorbot. The difference between this needed tension and the tension measured at the Cliffbot constitutes an error signal that is provided to the gravity controller. The velocity controller computes the tether speed needed to produce the desired motion of the Cliffbot. Another major consideration in the design and operation of TRESSA is detection of faults. Each robot in the TRESSA system monitors its own performance and the performance of its teammates in order to detect any system faults and prevent unsafe conditions. At startup, communication links are tested and if any robot is not communicating, the system refuses to execute any motion commands. Prior to motion, the Anchorbots attempt to set tensions in the tethers at optimal levels for counteracting the weight of the Cliffbot; if either Anchorbot fails to reach its optimal tension level within a specified time, it sends message to the other robots and the commanded motion is not executed. If any mechanical error (e.g., stalling of a motor) is detected, the affected robot sends a message triggering stoppage of the current motion. Lastly, messages are passed among the robots at each time step (10 Hz) to share sensor information during operations. If messages from any robot cease for more than an allowable time interval, the other robots detect the communication loss and initiate stoppage.

Stroupe, Ashley↗
NASA NTRS1991

Intelligent computer-aided training and tutoring

Specific autonomous training systems based on artificial intelligence technology for use by NASA astronauts, flight controllers, and ground-based support personnel that demonstrate an alternative to current training systems are described. In addition to these specific systems, the evolution of a general architecture for autonomous intelligent training systems that integrates many of the features of traditional training programs with artificial intelligence techniques is presented. These Intelligent Computer-Aided Training (ICAT) systems would provide, for the trainee, much of the same experience that could be gained from the best on-the-job training. By integrating domain expertise with a knowledge of appropriate training methods, an ICAT session should duplicate, as closely as possible, the trainee undergoing on-the-job training in the task environment, benefitting from the full attention of a task expert who is also an expert trainer. Thus, the philosophy of the ICAT system is to emulate the behavior of an experienced individual devoting his full time and attention to the training of a novice - proposing challenging training scenarios, monitoring and evaluating the actions of the trainee, providing meaningful comments in response to trainee errors, responding to trainee requests for information, giving hints (if appropriate), and remembering the strengths and weaknesses displayed by the trainee so that appropriate future exercises can be designed.

Loftin, R. Bowen↗
NASA NTRS1989

Using multiple sensors for printed circuit board insertion

As more and more activities are performed in space, there will be a greater demand placed on the information handling capacity of people who are to direct and accomplish these tasks. A promising alternative to full-time human involvement is the use of semi-autonomous, intelligent robot systems. To automate tasks such as assembly, disassembly, repair and maintenance, the issues presented by environmental uncertainties need to be addressed. These uncertainties are introduced by variations in the computed position of the robot at different locations in its work envelope, variations in part positioning, and tolerances of part dimensions. As a result, the robot system may not be able to accomplish the desired task without the help of sensor feedback. Measurements on the environment allow real time corrections to be made to the process. A design and implementation of an intelligent robot system which inserts printed circuit boards into a card cage are presented. Intelligent behavior is accomplished by coupling the task execution sequence with information derived from three different sensors: an overhead three-dimensional vision system, a fingertip infrared sensor, and a six degree of freedom wrist-mounted force/torque sensor.

Sood, Deepak↗
NASA NTRS1994

Integrating deliberative planning in a robot architecture

The role of planning and reactive control in an architecture for autonomous agents is discussed. The postulated architecture seperates the general robot intelligence problem into three interacting pieces: (1) robot reactive skills, i.e., grasping, object tracking, etc.; (2) a sequencing capability to differentially ativate the reactive skills; and (3) a delibrative planning capability to reason in depth about goals, preconditions, resources, and timing constraints. Within the sequencing module, caching techniques are used for handling routine activities. The planning system then builds on these cached solutions to routine tasks to build larger grain sized primitives. This eliminates large numbers of essentially linear planning problems. The architecture will be used in the future to incorporate in robots cognitive capabilites normally associated with intelligent behavior.

Elsaesser, Chris↗
NASA NTRS2011

Process Algebra Approach for Action Recognition in the Maritime Domain

The maritime environment poses a number of challenges for autonomous operation of surface boats. Among these challenges are the highly dynamic nature of the environment, the onboard sensing and reasoning requirements for obeying the navigational rules of the road, and the need for robust day/night hazard detection and avoidance. Development of full mission level autonomy entails addressing these challenges, coupled with inference of the tactical and strategic intent of possibly adversarial vehicles in the surrounding environment. This paper introduces PACIFIC (Process Algebra Capture of Intent From Information Content), an onboard system based on formal process algebras that is capable of extracting actions/activities from sensory inputs and reasoning within a mission context to ensure proper responses. PACIFIC is part of the Behavior Engine in CARACaS (Cognitive Architecture for Robotic Agent Command and Sensing), a system that is currently running on a number of U.S. Navy unmanned surface and underwater vehicles. Results from a series of experimental studies that demonstrate the effectiveness of the system are also presented.

process algebras↗
NASA NTRS2017

Measuring the Effectiveness of Human Autonomy Teaming

Human-Automation Teaming (HAT), is now recognized as a promising solution to the problems of humans managing increasingly complex work systems. A human-automation team can be defined as the interdependent coupling between one or more human operators and one or more autonomous systems requiring collaboration and coordination to accomplish system and task goals (e.g., Langan-Fox et al., 2009). In this conception, automated agents are considered team members that can operate at various levels of automation, be focused on one or more human-information-processing stages, and the interactions with human operators may be adaptable, adjustable or mixed initiative. We investigated some metrics for assessing HAT effectiveness in a demonstration of a HAT tool used by ground station operators in a Reduced Crew Operations project that was conducted at NASA Ames Human Automation Teaming Laboratory. In this paper, we focus on operator metrics of HAT effectiveness, specifically workload and operator behaviors.

measurements↗
NASA NTRSDate not supplied

Formal Specification and Parametric Verification of the ICAROUS Distributed Merging Protocol for Autonomous Aircraft Systems

ICAROUS is a software architecture that provides highly assured core software modules for building safety-centric autonomous unmanned aircraft applications. One of its core components is the ICAROUS distributed merging (IDM) protocol, which allows for decentralized merging of autonomous aircrafts through a designated intersection. This report presents initial results on formal specification and parametric verification of the IDM protocol. We present the development of a formal, discrete-time specification of the ICAROUS distributed merging protocol in TLA+. The developed TLA+ specification includes an abstracted model of the physical aircraft dynamics, the consensus machinery for leader election and coordination, and the computation of merging schedules. In addition, we present details on a command line tool we developed verimerge, that utilizes the TLC model checker for doing bounded, parametric verification and allows for plotting of these results in 2D parameter spaces. The tool also provides functionality for visualization of concrete protocol behaviors, to aid debugging and understanding. We present preliminary, bounded time verification results for a finite number of aircraft. Limitations of the current techniques and possible future extensions of this work are also discussed.

ICAROUS↗
NASA NTRS1997

Autogenic-Feedback Training Exercise (AFTE) Method and System

The Autogenic-Feedback Training Exercise (AFTE) method of the present invention is a combined application of physiologic and perceptual training techniques. such as autogenic therapy and biofeedback. This combined therapy approach produces a methodology that is appreciably more effective than either of the individual techniques used separately. The AFTE method enables sufficient magnitude of control necessary to significantly reduce the behavioral and physiologic reactions to severe environmental stressors. It produces learned effects that are persistent over time and are resistant to extinction and it can be administered in a short period of time. The AFTE method may be used efficiently in several applications, among which are the following: to improve pilot and crew performance during emergency flying conditions; to train people to prevent the occurrence of nausea and vomiting associated with motion and sea sickness, or morning sickness in early pregnancy; as a training method for preventing or counteracting air-sickness symptoms in high-performance military aircraft; for use as a method for cardiovascular training, as well as for multiple other autonomic responses, which may contribute to the alleviation of Space Motion Sickness (SMS) in astronauts and cosmonauts; training people suffering from migraine or tension headaches to control peripheral blood flow and reduce forehead and/or trapezius muscle tension; training elderly people suffering from fecal incontinence to control their sphincter muscles; training cancer patients to reduce the nauseagenic effects of chemotherapy; and training patients with Chronic Intestinal Pseudo-obstruction (CIP).

Cowings, Patricia S.↗
NASA NTRS2002

Automated fault-management in a simulated spaceflight micro-world

BACKGROUND: As human spaceflight missions extend in duration and distance from Earth, a self-sufficient crew will bear far greater onboard responsibility and authority for mission success. This will increase the need for automated fault management (FM). Human factors issues in the use of such systems include maintenance of cognitive skill, situational awareness (SA), trust in automation, and workload. This study examine the human performance consequences of operator use of intelligent FM support in interaction with an autonomous, space-related, atmospheric control system. METHODS: An expert system representing a model-base reasoning agent supported operators at a low level of automation (LOA) by a computerized fault finding guide, at a medium LOA by an automated diagnosis and recovery advisory, and at a high LOA by automate diagnosis and recovery implementation, subject to operator approval or veto. Ten percent of the experimental trials involved complete failure of FM support. RESULTS: Benefits of automation were reflected in more accurate diagnoses, shorter fault identification time, and reduced subjective operator workload. Unexpectedly, fault identification times deteriorated more at the medium than at the high LOA during automation failure. Analyses of information sampling behavior showed that offloading operators from recovery implementation during reliable automation enabled operators at high LOA to engage in fault assessment activities CONCLUSIONS: The potential threat to SA imposed by high-level automation, in which decision advisories are automatically generated, need not inevitably be counteracted by choosing a lower LOA. Instead, freeing operator cognitive resources by automatic implementation of recover plans at a higher LOA can promote better fault comprehension, so long as the automation interface is designed to support efficient information sampling.

NASA Discipline Space Human Factors↗