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This paper describes the means for generating rover localization information for NASA/JPL's FIDO rover. This is accomplished using a sensor fusion framework which combines wheel odometry with sun sensor and intertial navigation sensors to provide an integrated state estimate for the vehicle's position and oreintation relative to some fixed reference frame.
How overall vehicle stiffness is affected by local joint stiffness is discussed. By using the principle of virtual work and the minimum strain energy theorem, a closed form expression for the sensitivity coefficient was derived. The insensitivity of the vehicle stiffness to a particular joint, when its stiffness exceeds a certain value (or threshold value), was proven mathematically. In order to investigate the sensitivity of the structure to the joint stiffness, a so-called stick model was created, and the modeling technique is briefly described. Some data on joint stiffness of tested vehicles are also presented.
An experimental investigation was conducted to obtain detailed effects on supersonic vehicle hypersonic aerodynamic and stability and control characteristics of reaction control system jet flow field interactions with the local vehicle flow field. A 0.010-scale model was used. Six-component force data and wing, elevon, and body flap surface pressure data were obtained through an angle-of-attack range of -10 to +35 degrees with 0 deg angle of sideslip. The test was conducted with yaw, pitch and roll jet simulation at a free-stream Mach number of 10.3 and reaction control system plume simulation of flight dynamic pressures of 5, 10 and 20 PSF.
A Feature and Pose Constrained Extended Kalman Filter (FPC-EKF) is developed for highly dynamic computationally constrained micro aerial vehicles. Vehicle localization is achieved using only a low performance inertial measurement unit and a single camera. The FPC-EKF framework augments the vehicle's state with both previous vehicle poses and critical environmental features, including vertical edges. This filter framework efficiently incorporates measurements from hundreds of opportunistic visual features to constrain the motion estimate, while allowing navigating and sustained tracking with respect to a few persistent features. In addition, vertical features in the environment are opportunistically used to provide global attitude references. Accurate pose estimation is demonstrated on a sequence including fast traversing, where visual features enter and exit the field-of-view quickly, as well as hover and ingress maneuvers where drift free navigation is achieved with respect to the environment.
Launch vehicle transonic buffet environments produce large fluctuating pressures on the surface of the vehicle. Transonic buffet occurs as a launch vehicle passes through the transonic regime, typically Mach 0.8 to 1.2, where phenomena such as flow separation and shock dynamics produce large fluctuating pressures. The unsteady aerodynamic forces produced by these phenomena can excite both global and local vehicle structural response. The consequences of incorporating poorly characterized buffet forces into the design process can range from inefficient structures that can reduce the vehicle capabilities to undersized structures that, in the worst case scenario, may result in structural failure (Refs. 1, 2, 3). It is thus imperative to appropriately characterize the transonic buffet environment.
Various aspects of radar cross-section (RCS) techniques are summarized, with emphasis placed on fundamental electromagnetic phenomena, such as plane and spherical wave formulations, and the definition of RCS is given in the far-field sense. The basic relationship between electronic countermeasures and a signature level is discussed in terms of the detectability range of a target vehicle. Fundamental radar-signature analysis techniques, such as the physical-optics and geometrical-optics approximations, are presented along with examples in terms of aircraft components. Methods of analysis based on the geometrical theory of diffraction are considered and various wave-propagation phenomena are related to local vehicle geometry. Typical vehicle components are also discussed, together with their contribution to total vehicle RCS and their individual signature sensitivities.
The orbiter vent system provides dedicated vent areas to permit the gases trapped inside the vehicle to escape during accent. The same vent system also repressurizes the vehicle during entry. The vent system is one of six systems that constitutes the purge, vent and drain subsystem. The orbiter active vent system has been very adaptable to the changing requirements that have occurred during the development of the Space Shuttle orbiter. Good correlation has been obtained between predicted and measured compartment pressures during the orbital flight test (OFT) program. An investigation of the flight data showed that the difference between preflight prediction and the measured values were primarily due to the difference between the baseline external pressures, which was based on subscale wind tunnel test data, and the actual vehicle local external pressures measured during the flight. The current predictions are based on flight derived vent port pressure coefficients since the wind tunnel data does not adequately define the orbiter ascent pressure environment.
Mass loading effects on localized vibratory environments of rocket vehicles
As part of DARPA's MARS2020 program, the Jet Propulsion Laboratory developed a vision-based system for localization in urban environments that requires neither GPS nor active sensors. System hardware consists of a pair of small FireWire cameras and a standard Pentium-based computer. The inputs to the software system consist of: 1) a crude grid-based map describing the positions of buildings, 2) an initial estimate of robot location and 3) the video streams produced by each camera. At each step during the traverse the system: captures new image data, finds image features hypothesized to lie on the outside of a building, computes the range to those features, determines an estimate of the robot's motion since the previous step and combines that data with the map to update a probabilistic representation of the robot's location. This probabilistic representation allows the system to simultaneously represent multiple possible locations, For our testing, we have derived the a priori map manually using non-orthorectified overhead imagery, although this process could be automated. The software system consists of two primary components. The first is the vision system which uses binocular stereo ranging together with a set of heuristics to identify features likely to be part of building exteriors and to compute an estimate of the robot's motion since the previous step. The resulting visual features and the associated range measurements are software component, a particle-filter based localization system. This system uses the map and the then fed to the second primary most recent results from the vision system to update the estimate of the robot's location. This report summarizes the design of both the hardware and software and will include the results of applying the system to the global localization of a robot over an approximately half-kilometer traverse across JPL'S Pasadena campus.
In the Fall of 1993, NASA Ames deployed a modified Phantom S2 Remotely-Operated underwater Vehicle (ROV) into an ice-covered sea environment near McMurdo Science Station, Antarctica. This deployment was part of the antarctic Space Analog Program, a joint program between NASA and the National Science Foundation to demonstrate technologies relevant for space exploration in realistic field setting in the Antarctic. The goal of the mission was to operationally test the use of telepresence and virtual reality technology in the operator interface to a remote vehicle, while performing a benthic ecology study. The vehicle was operated both locally, from above a dive hole in the ice through which it was launched, and remotely over a satellite communications link from a control room at NASA's Ames Research Center. Local control of the vehicle was accomplished using the standard Phantom control box containing joysticks and switches, with the operator viewing stereo video camera images on a stereo display monitor. Remote control of the vehicle over the satellite link was accomplished using the Virtual Environment Vehicle Interface (VEVI) control software developed at NASA Ames. The remote operator interface included either a stereo display monitor similar to that used locally or a stereo head-mounted head-tracked display. The compressed video signal from the vehicle was transmitted to NASA Ames over a 768 Kbps satellite channel. Another channel was used to provide a bi-directional Internet link to the vehicle control computer through which the command and telemetry signals traveled, along with a bi-directional telephone service. In addition to the live stereo video from the satellite link, the operator could view a computer-generated graphic representation of the underwater terrain, modeled from the vehicle's sensors. The virtual environment contained an animate graphic model of the vehicle which reflected the state of the actual vehicle, along with ancillary information such as the vehicle track, science markers, and locations of video snapshots. The actual vehicle was driven either from within the virtual environment or through a telepresence interface. All vehicle functions could be controlled remotely over the satellite link.
Development of unmanned vehicles for local navigation in terrains unstructured by humans is reviewed. Modes of navigation include teleoperation or remote control, computer assisted remote driving (CARD), and semiautonomous navigation (SAN). A first implementation of a CARD system was successfully tested using the Robotic Technology Test Vehicle developed by Jet Propulsion Laboratory. Stereo pictures were transmitted to a remotely located human operator, who performed the sensing, perception, and planning functions of navigation. A computer provided range and angle measurements and the path plan was transmitted to the vehicle which autonomously executed the path. This implementation is to be enhanced by providing passive stereo vision and a reflex control system for autonomously stopping the vehicle if blocked by an obstacle. SAN achievements include implementation of a navigation testbed on a six wheel, three-body articulated rover vehicle, development of SAN algorithms and code, integration of SAN software onto the vehicle, and a successful feasibility demonstration that represents a step forward towards the technology required for long-range exploration of the lunar or Martian surface. The vehicle includes a passive stereo vision system with real-time area-based stereo image correlation, a terrain matcher, a path planner, and a path execution planner.
This software addresses the issue of underwater localization of unmanned vehicles and the inherent drift in their onboard sensors. The software gives a 2 to 3 factor of improvement over the state-of-the-art underwater localization algorithms. The software determines the localization (position, heading) of an AUV (autonomous underwater vehicle) in environments where there is no GPS signal. It accomplishes this using only the commanded position, onboard gyros/accelerometers, and the bathymetry of the bottom provided by an onboard sonar system. The software does not rely on an onboard bathymetry dataset, but instead incrementally determines the position of the AUV while mapping the bottom. In order to enable long-distance underwater navigation by AUVs, a localization method called ULTRA uses registration of the bathymetry data products produced by the onboard forward-looking sonar system for hazard avoidance during a transit to derive the motion and pose of the AUV in order to correct the DR (dead reckoning) estimates. The registration algorithm uses iterative point matching (IPM) combined with surface interpolation of the Iterative Closest Point (ICP) algorithm. This method was used previously at JPL for onboard unmanned ground vehicle localization, and has been optimized for efficient computational and memory use.
Prediction of rocket vehicle environment - statistical analyses of structural configuration in vibration
Prediction of acoustic vibration response of rocket vehicle structures
Computer program provides time history of sun aspect angles as seen from spacecraft and generates binary tape containing time history of position, velocity, and body attitude angles of vehicle in local horizontal, local vertical system. Program is written in FORTRAN 5 and SLEUTH 2 for Univac 1108 computer.
Rotorcraft operating in hostile environment fly at low altitudes to minimize exposure to the defensive weapons arrayed against them. The development of intelligent guidance commands at low altitudes requires the integration of conventional guidance with the information provided by the sensor on relative position between the vehicle and local terrain or obstacles. Deceleration to hover (DTH) is one of the common maneuvers executed by a rotorcraft. The authors describe the integration of DTH guidance logic with an image-based scheme to estimate the hover point. The performance of such a system is affected by parameters of the guidance and by navigation and image processing algorithms. Results are presented on the effect of the parameters on system performance.
Many of the most challenging reliability problems of our present decade involve complex distributed systems such as interconnected telephone switching computers, air traffic control centers, aircraft and space vehicles, and local area and wide area computer networks. In addition to the challenge of complexity, modern fault-tolerant computer systems require very high levels of reliability, e.g., avionic computers with MTTF goals of one billion hours. Most analysts find that it is too difficult to model such complex systems without computer aided design programs. In response to this need, NASA has developed a suite of computer aided reliability modeling programs beginning with CARE 3 and including a group of new programs such as: HARP, HARP-PC, Reliability Analysts Workbench (Combination of model solvers SURE, STEM, PAWS, and common front-end model ASSIST), and the Fault Tree Compiler. The HARP program is studied and how well the user can model systems using this program is investigated. One of the important objectives will be to study how user friendly this program is, e.g., how easy it is to model the system, provide the input information, and interpret the results. The experiences of the author and his graduate students who used HARP in two graduate courses are described. Some brief comparisons were made with the ARIES program which the students also used. Theoretical studies of the modeling techniques used in HARP are also included. Of course no answer can be any more accurate than the fidelity of the model, thus an Appendix is included which discusses modeling accuracy. A broad viewpoint is taken and all problems which occurred in the use of HARP are discussed. Such problems include: computer system problems, installation manual problems, user manual problems, program inconsistencies, program limitations, confusing notation, long run times, accuracy problems, etc.
Advanced Air Mobility (AAM) aims to develop an air transportation system for novel air vehicles between local, regional, intraregional, and urban places. Safety and efficiency of increasingly complex AAM operations are expected to require extensive use of automation, ranging from controlling the revolutionary new aircraft to managing flights dynamically in the high tempo airspace and aerodrome operations. Both onboard and ground automation will play central roles in assisting AAM operators with managing the flight paths of their fleet. This document presents a concept for dynamic path planning (DPP) automation applicable to AAM and other flight operations. The role of the DPP automation system is fivefold: (1) it creates a flight path with desired qualities of being feasible, deconflicted, harmonized, flexible, and optimal; (2) it monitors the progress of flight in a dynamic operating environment; (3) it supports the user in evaluating continued acceptability of the flight path in changing conditions; (4) it revises the flight path as needed to maintain the desired flight path qualities; and (5) it coordinates the flight path with airspace users and service providers. Key users of the DPP automation system include flight planners, pilots, and airspace service providers The concept allows for the system to be installed onboard the aircraft as well as on the ground. The system responds automatically to the dynamic operating environment to ensure that a safe and operationally acceptable flight path is available throughout the flight.