Robotics, Automation and Control
Robotics, Automation and Control
From autonomous robots and drones to advanced control systems and machine learning algorithms, NASA's robotics, automation, and control technology offers a range of options for automating and improving the performance of systems and processes. Whether you're looking to develop new robotics technologies or to improve the efficiency of your manufacturing operations, NASA's might have a solution to help you reach your goals.
Algorithms for stabilizing intelligent networks
Algorithms for stabilizing intelligent networks
Some of the current challenges faced by research in artificial intelligence and autonomous control systems include providing self control, resilience, adaptability, and stability for intelligent systems, especially over a long period of time, in changing environments. The Evolvable Neural Software System (ENSS), Formulation for Emotion Embedding in Logic Systems (FEELS), Stability Algorithm for Neural Entities (SANE), and the Logic Expansion for Autonomously Reconfigurable Neural Systems (LEARNS) are foundations for tackling some of these challenges, by providing the basic algorithms evolvable systems could use to manage its own behavior. These algorithms would allow networks to self regulate, noticing unusual behavior and the circumstances that may have caused that behavior, and then correcting to behave more predictably when similar circumstances are encountered. The process is similar to how psychology in organisms evolved iteratively, eventually finding and keeping better responses to given stimuli.
Robotic gripper for satellite capture and servicing
The Gripper is located at the end of a robotic system consisting of a robotic arm equipped with a Tool Drive or End Effector comprising the input actuator to the Gripper as well as the structural, power and data link between the Gripper and the robotic arm. In a notional concept of operations, a Servicer would approach the Client in an autonomous rendezvous and capture (AR&C) maneuver. When the Servicers sensor suite confirms that the distance, orientation, and relative translational and angular rates with respect to the Client are within an acceptable range, the Servicer enables the grasping sequence, where the robotic arm, equipped with Gripper, extend forward to the Client. When the Gripper/ Servicer sensors indicate that the Client marman ring is sufficiently within the capture range of the Gripper, a trigger signal is sent to the robot control system that commands the End Effector to drive the mechanism of the Gripper and affect closure around the marman ring. The Gripper consists of a pair of jaws which are driven by an internal transmission. The transmission receives input torque from the End Effector and converts the torque to appropriate motion of the jaws.
Wind Turbines
Residual Mode Filters
Many control problems can benefit from the adaptive control algorithm described. This algorithm is well-suited to nonlinear applications that have unknown modeling parameters and poorly known operating conditions. Disturbance accommodation is a critical component of many systems. By using feedback control with disturbance accommodation, system performance and reliability can be increased considerably. Often the form of the disturbance is known, but the amplitude is unknown. For instance, a motor operating on a structure used for accurate pointing would cause a sinusoidal disturbance of a known frequency content. The algorithm described is able to accurately cancel these disturbances, without needing knowledge of their amplitude. In markets needing controllers, the efficiency, uptime, and lifespan of equipment can be dramatically increased due to the robustness of this technologys design.
System And Method for Managing Autonomous Entities through Apoptosis
In this method an autonomic entity manages a system through the generation of one or more stay alive signals by a hierarchical evolvable synthetic neural system. The generated signal is based on the current functioning status and operating state of the system and dictates whether the system will stay alive, initiate self-destruction, or initiate sleep mode. This method provides a solution to the long standing need for a synthetic autonomous entity capable of adapting itself to changing external environments and ceasing its own operation upon the occurrence of a predetermined condition deemed harmful.
Hubble Finds a Lenticular Galaxy Standing Out in the Crowd
Credit: NASA/ESA/Hubble; acknowledgements: Judy Schmidt (Geckzilla)
FlashPose: Range and intensity image-based terrain and vehicle relative pose estimation algorithm
Flashpose is the combination of software written in C and FPGA firmware written in VHDL. It is designed to run under the Linux OS environment in an embedded system or within a custom development application on a Linux workstation. The algorithm is based on the classic Iterative Closest Point (ICP) algorithm originally proposed by Besl and McKay. Basically, the algorithm takes in a range image from a three-dimensional imager, filters and thresholds the image, and converts it to a point cloud in the Cartesian coordinate system. It then minimizes the distances between the point cloud and a model of the target at the origin of the Cartesian frame by manipulating point cloud rotation and translation. This procedure is repeated a number of times for a single image until a predefined mean square error metric is met; at this point the process repeats for a new image. The rotation and translation operations performed on the point cloud represent an estimate of relative attitude and position, otherwise known as pose. In addition to 6 degree of freedom (DOF) pose estimation, Flashpose also provides a range and bearing estimate relative to the sensor reference frame. This estimate is based on a simple algorithm that generates a configurable histogram of range information, and analyzes characteristics of the histogram to produce the range and bearing estimate. This can be generated quickly and provides valuable information for seeding the Flashpose ICP algorithm as well as external optical pose algorithms and relative attitude Kalman filters.
Cooperative Service Valve for on-orbit cooperative satellite fueling
Cooperative Service Valve for In-orbit Cooperative Satellite Fueling
The CSV replaces a standard spacecraft Fill and Drain Valve to facilitate cooperative servicing. The CSV offers various advantages over standard service valves: a robotic interface, three individually actuated seals, a self-contained anti-back drive system, and built-in thermal isolation. When mounted to a spacecraft as designed, the CSV transfers all operational and induced robotic loads to the mounting structure. An anti-back drive mechanism prevents the CSV seal mechanism from inadvertent actuation. Alignment marks, thermal isolation, and a mechanical coupling capable of reacting operational and robotic loads optimize the CSV for tele-robotic operations. Unique keying of the mating interface prevents mixing of media where more than one configuration of the CSV is used. Color-coding and labels are also used to prevent operator error. The CSV has four configurations for different working fluids, all with essentially unchanged geometry and mechanics.
In celebration of the 25th anniversary of NASA's first space servicing mission to the Hubble Space Telescope, we are sharing this gallery of images from all five of the Hubble servicing missions. Astronauts serviced Hubble for the first time in December 1993. Including that trip, there have been five astronaut servicing missions to Hubble between 1993 and 2009. How did astronauts repair and service the Hubble Space Telescope more than 300 miles above the surface of the Earth? Watch Hubble astronauts as they discuss servicing from the innovative Robotics Operations Center: STS109-713-003 (8 March 2002) --- Astronaut John M. Grunsfeld, STS-109 payload commander, anchored on the end of the Space Shuttle Columbias Remote Manipulator System (RMS) robotic arm, moves toward the giant Hubble Space Telescope (HST) temporarily hosted in the orbiters cargo bay. Astronaut Richard M. Linnehan works in tandem with Grunsfeld during this fifth and final session of extravehicular activity (EVA). Activities for the space walk centered around the Near-Infrared Camera and Multi-Object Spectrometer (NICMOS) to install a Cryogenic Cooler and its Cooling System Radiator.
Robot-Driven Blind Mate Interface
The Robot-Driven Blind Mate Interface is a specialized interface utilizing a robot-driven, blind mate mechanism that allows structural, electrical, and fluid connections to be reliably made in a single motion. The interface is composed of a removable side and a fixed side. The removable side consists of the robot grasp point, a drive bolt, one side of a blind mate fluid, electrical couplings, and one side of the interface alignment features. The fixed side consists of corresponding alignment features and the mechanisms carriage. The carriage houses the corresponding fluid and electrical couplings and over-travel protection for the couplings. The robot system used dictates the specific type of robot grasp point, any required targets, and mechanism status indicators. The mate and de-mate forces of the interface are balanced throughout the mechanism so it can be actuated with one motion, such as turning a single drive bolt. The point at which the different connectors seat is carefully controlled the spring forces distributed throughout the mechanism. For example, the electrical connectors can make contact before the fluid couplings, if desirable, to accommodate the long length of high voltage and current pins. The springs that compress to provide over-travel protection on the electrical connectors allow for preload to be developed between the removable side and fixed side of the interface to create a sound structural connection while not over-stressing the connectors. Overtravel protection can be applied to fluid couplings as needed depending on the specific coupling used. The interface is versatile and can be tailored to a wide range of fluid and electrical couplings.
Compact Science Experiment Module
The Compact Science Experiment Module (CSEM) provides a suitable experiment platform consisting of an enclosure that contains all the required components to perform science experiments that can house either living biological samples or other samples on both the ground and on the International Space Station (ISS). The invention provides required instrumentation for video capture and data storage, environmental monitoring, inclusive of sensing temperature in degree Celsius, relative humidity as a percentage, carbon dioxide in parts per million and oxygen in percentage format. Data can be stored within the module and retrieved after the experiment or can be transmitted to the ground as for example from the ISS, by connecting to the ISS telemetry system. The Compact Life Science Experiment Module has been fully developed at NASA Ames Research Center and tested on the ISS. In general, fruit fly studies can provide information about the effects of spaceflight at the biochemical, cellular and organismal levels. Using fruit fly spaceflight hardware, researchers are able to investigate the role of spaceflight on development, growth, reproduction, aging, neurobehavioral responses, immunity, heart function, etc. The fruit fly genome matches the human disease genome by almost 77%, and flies have, therefore, been a useful tool for scientists to understand the genetics, and molecular biology of more complex biological systems like humans. The Compact Science Experiment Module is extremely adaptable to other model specimens and samples as well, and has also flown plant experiments on the ISS. The software can be tailored to accommodate different experiment scenarios by adjusting video imaging times, LED light cycles, data storage and telemetry etc.
Motorized Production Machine
Precision Low Speed Motor Controller
The Precision Low Speed Motor Controller was designed as part of an OpTIIX telescope for the International Space Station. This technology is based on a precise current control loop and a high fidelity velocity measurement algorithm. The precise current loop uses a mathematical model of the electrical dynamics of the motor, custom electronics, and a PI controller to maintain a rapid response and smooth current control. The velocity measurement algorithm is embedded in the velocity loop that is wrapped around the current loop to provide a smooth low velocity control. Current motors are only capable of operating at approximately 15 rpm with a risk of excessive jitters. This technology reduces the responsive rpms by several orders of magnitude to approximately 0.025 rpms. This technology's capability has been integral to the success of several NASA projects, such as the OpTIIX telescope, the NASA Robonaut 2 robot , and the Modular Robotic Vehicle (MRV).
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