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Tour of the Johnson Space Center Robotic Technologies: ARGOS (Active Response Gravity Offload System), Rover (Featuring the Small Pressurized Rover), mSTAR/STAR, Six-Degree-of-Freedom Dynamic Test System (SDTS docking test system). Video has a 19:19 run time, color, audio and must be downloaded to view.
The application of robots to complex on-orbit tasks will require a high degree of adaptability. This paper describes a project which is developing very adaptive robotic systems for welding of the Space Shuttle main engine. A number of the developments which will arise from this program will serve as useful starting points for more advanced systems to be used on-orbit.
In order to meet stringent performance goals for power and reuseability, the Space Shuttle Main Engine was designed with many complex, difficult welded joints that provide maximum strength and minimum weight. To this end, the SSME requires 370 meters of welded joints. Automation of some welds has improved welding productivity significantly over manual welding. Application has previously been limited by accessibility constraints, requirements for complex process control, low production volumes, high part variability, and stringent quality requirements. Development of robots for welding in this application requires that a unique set of constraints be addressed. This paper shows how robotic welding can enhance production of aerospace components by addressing their specific requirements. A development program at the Marshall Space Flight Center combining industrial robots with state-of-the-art sensor systems and computer simulation is providing technology for the automation of welds in Space Shuttle Main Engine production.
The present volume discusses progress toward intelligent robot systems in aerospace applications, NASA Space Program automation and robotics efforts, the supervisory control of telerobotics in space, machine intelligence and crew/vehicle interfaces, expert-system terms and building tools, and knowledge-acquisition for autonomous systems. Also discussed are methods for validation of knowledge-based systems, a design methodology for knowledge-based management systems, knowledge-based simulation for aerospace systems, knowledge-based diagnosis, planning and scheduling methods in AI, the treatment of uncertainty in AI, vision-sensing techniques in aerospace applications, image-understanding techniques, tactile sensing for robots, distributed sensor integration, and the control of articulated and deformable space structures.
Designed and implemented in the 1980's, the Advanced Multi-Mission Operations System (AMMOS) was a breakthrough for deep-space NASA missions, enabling significant reductions in the cost and risk of implementing ground systems. By designing a framework for use across multiple missions and adaptability to specific mission needs, AMMOS developers created a set of applications that have operated dozens of deep-space robotic missions over the past 30 years. We seek to leverage advances in technology and practice of architecting and systems engineering, using model-based approaches to update the AMMOS. We therefore revisit fundamental aspects of the AMMOS, resulting in a major update to the Mission Operations System (MOS): MOS 2.0. This update will ensure that the MOS can support an increasing range of mission types, (such as orbiters, landers, rovers, penetrators and balloons), and that the operations systems for deep-space robotic missions can reap the benefits of an iterative multi-mission framework.12 This paper reports on the first phase of this major update. Here we describe the methods and formal semantics used to address MOS 2.0 architecture and some early results. Early benefits of this approach include improved stakeholder input and buy-in, the ability to articulate and focus effort on key, system-wide principles, and efficiency gains obtained by use of well-architected design patterns and the use of models to improve the quality of documentation and decrease the effort required to produce and maintain it. We find that such methods facilitate reasoning, simulation, analysis on the system design in terms of design impacts, generation of products (e.g., project-review and software-delivery products), and use of formal process descriptions to enable goal-based operations. This initial phase yields a forward-looking and principled MOS 2.0 architectural vision, which considers both the mission-specific context and long-term system sustainability.
On April 12, 2002, NASA Administrator Sean O Keefe opened a new window to the future of space exploration with these words in his Pioneering the Future address. Thus began the conceptual framework for structuring the new Education Enterprise. The Agency s mission is to understand and protect our home planet; to explore the universe in search for life; and to inspire the next generation of explorers as only NASA can. In adopting this mission, education became a core element and is now a vital part of every major NASA research and development mission. NASA s call to inspire the next generation of explorers is now resounding throughout the NASA community and schools of all levels all around the country. The goal is to capture student interest, nurture their natural curiosities, and intrigue their minds with new and exciting scientific research; as well as to provide educators with the creative tools they need to improve America s scientific literacy. The future of NASA begins with America s youngest scholars. According to Administrator O Keefe s address, if NASA does not motivate the youngest generation now, there is little prospect this generation will choose to pursue scientific disciplines later. Since embracing Administrator O Keefe s educational mandate over a year ago, NASA has been fully devoted to broadening its roadmap to motivation. The efforts have generated a whole new showcase of thoughtprovoking and fun learning opportunities, through printed material, Web sites and Webcasts, robotics, rocketry, aerospace design contests, and various other resources as only NASA can.
Ultrasound examinations represent one of the major diagnostic modalities of future healthcare. They are currently used to support medical space research but require a high skilled operator for both probe positioning on the patient's skin and image interpretation. TERESA is a tele-echography project that proposes a solution to bring astronauts and remotely located patients on ground quality ultrasound examinations despite the lack of a specialist at the location of the wanted medical act.
Through Small Business Innovation Research (SBIR) contracts with Ames Research Center, Intelligent Automation Inc., based in Rockville, Maryland, advanced specialized software the company had begun developing with U.S. Department of Defense funding. The agent-based infrastructure now allows NASA's Airspace Concept Evaluation System to explore ways of improving the utilization of the National Airspace System (NAS), providing flexible modeling of every part of the NAS down to individual planes, airports, control centers, and even weather. The software has been licensed to a number of aerospace and robotics customers, and has even been used to model the behavior of crowds.
Automated tow placement has become a widely used fabrication technique, especially for large aerospace structures. Robotic heads lay down strips (tows) of preimpregnated fiber along programmed paths. The intention is to lay adjacent tows abutted to one another, but sometimes a gap is left between a tow and the previously-placed tow. If a tow gap exists, it fills with resin during cure, forming a fiber-free volume. In immersion ultrasonic pulse-echo measurements of a cured laminate, the gap can be observed to produce a noticeable echo, without significantly attenuating the back-wall reflection of the laminate. To understand this behavior, we considered a one dimensional model of the composite laminate, with a thin layer having the ultrasonic sound speed and density of neat resin, sandwiched between two layers of material having the sound speed and density of fiber-reinforced composite and surrounded on both sides by water. Neglecting attenuation, we considered the transmission and reflection coefficients of each interface, as well as that of the thin resin layer. Using the initial water/composite reflection as a reference, we computed the relative magnitude of the back surface/water reflection in the presence and in the absence of a resin-only layer, as well as the relative magnitude of the reflection arising from a thin resin layer in composite. While the one-dimensional model did not fully match the measurements, it did qualitatively explain the observed behavior.
Harmonic drives are a highly efficient and compact gearing technology that are widely used in robotics and aerospace applications (e.g. Mars Science Lab) due to their excellent torque-to-mass ratio, low backlash, and high positional accuracy. One major problem using any gearing on other planetary bodies is the need for heaters to keep the gearbox from freezing. Currently the Mars Science Lab uses 40% of its daily power output to operate heaters to keep the grease in the gearboxes from freezing. Also wear can limit the lifetime and performance of these systems. One possible solution to this problem could be to use a relatively new material known as Bulk Metallic Glasses (BMGs, also known as Amorphous Metals). BMGs are non-crystalline metallic alloys that exhibit superior mechanical properties. Compared to modern aerospace steels BMGs have a higher strength, higher elastic limit, and lower density. BMGs used in harmonic drives (especially the flex spline) could lead to longer system lifetimes, lower mass, and increased performance. Jet Propulsion Laboratory (JPL) is researching a new method of manufacturing the flex spline by injection molding and is hoping to have reduced costs as well as the benefits listed above. In conjunction with Kennedy Space Center (KSC), JPL is providing gearboxes that KSC will test greaseless in an extreme environment. KSC has completed the assembly and checkout of a test stand that is capable of testing a gearbox in a vacuum of less than 2 milli torr, 100 Kelvin temperature, 250 Nm of load, input and output torque measurements and 120 RPM input speed.
DARTS is a rigid/flexible multibody dynamics toolkit for themodeling and simulation of aerospace and robotic vehicles forengineering applications. In this paper we describe an on-line,browser-based environment using Jupyter notebooks to supporttraining needs for the DARTS software. The suite of curated tutorial notebooks is organized into different topic areas, and intomultiple themes within each topic area. The notebooks within atheme use a progression of examples for users to expand theirunderstanding of the software. The topic areas include one onthe DARTS multibody dynamics software and another one on thetheory underlying the multibody dynamics formulation. We alsodescribe a number of Jupyter extensions that were used - andsome developed in house - to enhance the notebook interface foruse with the dynamics simulation software. One significant extension we implemented allows the embedding of live 3D visualizations within simulation notebooks.
Planetary exploration rovers are expensive, weight constrained, and cannot be serviced once deployed. Here, we explore one way to increase their capabilities while avoiding the cost, mass, and complexity leading to these issues. We propose to re-use the large wheel actuators for trenching and other digging operations, which will enable a range of missions such as sampling deeper layers of soil. We present a new, closed-form model of the soil displaced by an angled, spinning wheel to analyze the trenching potential of a driving strategy and inform the control of the wheel. The model is demonstrated with single wheel experiments under different driving conditions. The model suggests: that a deep trench does not require large tractive efforts; that the shape of the trench can be controlled; and that a rear wheel has a lower risk of entrapment when trenching than a front wheel. Ultimately this model could be used in a nonprehensile manipulation planning or learning algorithm to enable autonomous trenching.
Perception plays a key role in autonomous and semi-autonomous planetary exploration vehicles. For instance, landers can use computer vision techniques for identifying safe landing locations, aerial vehicles use cameras as navigation sensors, and planetary rovers use them for localization and hazard detection. Engineering simulations of such systems requires the accurate modeling of perception and vision sensors for simulating autonomy scenarios. In addition, the modeling of sensors for landers, aerial and ground vehicles requires the ability to handle large and high-resolution terrains, the accurate modeling of illumination, hi-fidelity rendering via ray/path tracing and the inclusion of sensor characteristics. Vision sensor models strive to simulate sensor reality by using physics principles to model the interaction of light and objects. Furthermore, high frame rate performance is highly desirable for in-the-loop simulations involving vehicle dynamics and control software. In this paper we describe a new sensor modeling capability called Inter-planetary Rendering for Imaging and Sensors (IRIS) that meets these requirements for the real-time and high-fidelity simulation of vision sensors for planetary aerospace and robotics applications.
FRET (https://github.com/NASA-SW-VnV/fret [github.com]) is a framework for the elicitation, formalization and analysis of requirements. FRET allows its user to enter requirements in a structured natural language called FRETish. Requirements written in FRETish are assigned unambiguous semantics. FRET supports its users in understanding this semantics and repairing requirements if applicable, by utilizing a variety of forms for each requirement: natural language description, formal mathematical logics, diagrams, and interactive simulation. FRET exports requirements into forms that can be used by a variety of analysis tools, including state-of-the-art model checkers and runtime monitoring tools. The talk will cover some of the theory behind the framework, present case studies from the aerospace and robotics domains, as well as current work on extending FRET for specifying requirements for software that learns.
The Basic Operational Robotics Instructional System (BORIS) is a six-degree-of-freedom rotational robotic manipulator system simulation used for training of fundamental robotics concepts, with in-line shoulder, offset elbow, and offset wrist. BORIS is used to provide generic robotics training to aerospace professionals including flight crews, flight controllers, and robotics instructors. It uses forward kinematic and inverse kinematic algorithms to simulate joint and end-effector motion, combined with a multibody dynamics model, moving-object contact model, and X-Windows based graphical user interfaces, coordinated in the Trick Simulation modeling environment. The motivation for development of BORIS was the need for a generic system for basic robotics training. Before BORIS, introductory robotics training was done with either the SRMS (Shuttle Remote Manipulator System) or SSRMS (Space Station Remote Manipulator System) simulations. The unique construction of each of these systems required some specialized training that distracted students from the ideas and goals of the basic robotics instruction.
Two major challenges exist for the development of dynamic control systems: first, the control system must be resourceful enough to provide problem-solving capabilities in unforeseen circumstances; second, it must be rapid enough to respond to dynamic environments. Most conventional control systems do not have the ability to 'step back' and problem-solve, especially in environments with incomplete and uncertain models and data. Orthogonally, commercially available AI systems usually do not respond at the rates required to support 'real-time' control. Hence, control systems are not available that respond to complex dynamic environments within appropriate time constraints. This paper describes work on an AI-based control system designed to address these challenges. A prototype system has been used, in a simulated environment, to control the robotic welding of aerospace components. This AI-based control system has demonstrated the ability to flexibly control a complex process within required time constraints by incorporating higher level reasoning and the ability to deal with uncertainty. Further work is in progress to expand the system's problem-solving capabilities.
In recent years, capabilties of Deep Neural Networks (DNN) and Artificial Intelligence (AI) systems have grown tremendously. They are now applied in many areas ranging from game playing, social media, science, to robotics, automotive, and aerospace applications.Based upon requirements for safety of DNN and AI in high assurance automotive and aerospace applications, I will discuss the necessity to ensure that AI technqiues for the analysis of Earth observation data and reasoning are working correctly and reliably.In this talk I will present modern techniques for the verification and validation (V&V) of DNN and other AI components as well as approaches for interpretable AI. I will discuss how these techniques can help to ensure quality of the AI results, improve confidence in their application, and facilitate human-AI interaction and collaboration.
Twenty-two papers are documented regarding aeronautical and spacecraft hardware. Technological areas include actuators, latches, cryogenic mechanisms, vacuum tribology, bearings, robotics, ground support equipment for aerospace applications, and other mechanisms.