autoNGC: Expand the Possibilities with Onboard Autonomy
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It is noted that the Shuttle navigation system employs redundant tactical air navigation (tacan) and microwave scanning beam landing system (MSBLS) equipment for use in navigation during descent from altitudes of about 150,000 feet through rollout. Attention is given here to the multiple tacan and MSBLS units (three each) that were placed onboard to provide the necessary protection in the event of possible failures. The goals, features, approach, and performance of onboard software required to manage multiple tacan MSBLS units and to provide the corresponding data for navigation processing are described.
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.
The Space Shuttle uses a complex set of software to guide, navigate, and control it through all phases of flight. Adding to the complexity is the fact that the software is reconfigured for each flight, i.e., thousands of constants in the software are changed to reflect the unique properties of a given mission. In the last level of tests, the software is flown through end to end nominal and abort scenarios taking the shuttle from liftoff to landing. The analysis of the results of the testing is experience and labor intensive. A set of pass/fail criteria were defined for each test case and in parallel with the knowledge acquisition, tools were developed which allowed the automation of the knowledge being gathered on paper. A prototype of the Analysis Criteria Expert System (ACES) was put into production in the verification of the reconfigured onboard flight software.
In the area of planetary landing, hazard detection and avoidance is the act of driving a vehicle to a safe landing area using onboard resources. A hazard detection sensor is used to scan the terrain and these measurements are evaluated to determine where the safe landing sites are located. The selected site is generally not the same as the nominal target, so the vehicle must divert to the new site. This activity involves the interaction between several components, including a suite of onboard GNC algorithms that work together to efficiently choose and divert to a new site. This paper presents the Hazard Boresight Relative Navigation concept, which is a method that provides a common interface between the hazard scan, safe-site selection algorithm, size of the target-relative landing ellipse, divert offset distance and guidance targeting algorithm. After the safe-site is selected from the hazard scan, the original inertial target is replaced with a vehicle-relative target, which is initialized by a measurement from the hazard scan. The new target-relative position state is estimated over time in the navigation filter, and is fed to the guidance algorithm to perform the divert maneuver. In addition to detailing the Hazard Boresight Relative Navigation concept, this paper also presents some general landing terms that can be used in the greater discussion, as well as analysis on how to estimate and predict the vehicle footprint dispersion ellipse during flight, which is used in the safe-site selection algorithm.
In the area of planetary landing, hazard detection and avoidance is the act of driving a vehicle to a safe landing area using onboard resources. A hazard detection sensor is used to scan the terrain and these measurements are evaluated to determine where the safe landing sites are located. The selected site is generally not the same as the nominal target, so the vehicle must divert to the new site. This activity involves the interaction between several components, including a suite of onboard GNC algorithms that work together to efficiently choose and divert to a new site. This paper presents the Hazard Boresight Relative Navigation concept, which is a method that provides a common interface between the hazard scan, safe-site selection algorithm, size of the target-relative landing ellipse, divert offset distance and guidance targeting algorithm. After the safe-site is selected from the hazard scan, the original inertial target is replaced with a vehicle-relative target, which is initialized by a measurement from the hazard scan. The new target-relative position state is estimated over time in the navigation filter, and is fed to the guidance algorithm to perform the divert maneuver. In addition to detailing the Hazard Boresight Relative Navigation concept, this paper also presents some general landing terms that can be used in the greater discussion, as well as analysis on how to estimate and predict the vehicle footprint dispersion ellipse during flight, which is used in the safe-site selection algorithm.
On February 14, 2000, an orbit insertion burn will place NASA's Near Earth Asteroid Rendezvous (NEAR) spacecraft (S/C) into orbit around asteroid 433 Eros. NEAR will initially orbit Eros with distances ranging from 500 to 100 km in order to characterize the shape, gravity and spin of Eros. Once the physical parameters of Eros are determined reasonably well, the plan is to establish an orbit of the NEAR S/C with increasingly lower altitudes as the one year orbital mission progresses while further characterizing the gravity and shape of Eros. Towards the end of the NEAR mission, after the shape, gravity and spin of Eros have been well characterized, the scientific interest of obtaining very close observations (< 5 km) can be realized. The navigation during this phase relies on a combination of NASA's Deep Space Network (DSN) radio metric tracking, laser ranging (LIDAR) data from the S/C to the surface of Eros, and onboard optical imaging of landmarks on Eros. This paper will provide preliminary plans for mission design and navigation during the last two months of the orbit phase, where several close passes to the surface will be incorporated to enhance the science return. The culmination of these close passes will result in the eventual landing of the S/C on the surface of Eros. Several considerations for these plans are given by Antreasian, et at. [1998]. The objective for the end of the mission will be to land the S/C autonomously using the surface relative information obtained from the onboard LIDAR instrument. The goal will be to soft land the S/C in such a way as to keep it operational. With the use of an onboard LIDAR landing algorithm as discussed by Antreasian et at. [1998], it is believed that the S/C impact velocity can be kept well under 7 m/s which is a requirement for allowing the S/C to remain operational.
A prototype low-flow marine current turbine for deployment from a small unmanned mobile floating platform has been developed for autonomously seeking and harnessing tidal/coastal currents. The support platform is an unmanned surface vehicle (USV), in the form of a catamaran with two electric outboard motors and with capabilities for autonomous navigation. The USV utilized is a WAM-V 16 vehicle that has been developed separately with support from the Office of Naval Research (ONR) [1]. The marine current turbine is based on a freestream waterwheel (FSWW), also known as an undershot waterwheel (FSWW), mounted on the stern of the USV. The concept of operation involves the USV autonomously navigating to a designated marine current resource. Upon arrival, the USV anchors itself, aligns with the current, and deploys the FSWW turbine using a custom cable-lift mechanism. The turbine harnesses the local current, and an onboard power-take-off (PTO) device converts the mechanical energy into electricity, which is stored in an onboard battery bank. When energy harvesting is completed, the turbine and the anchor are retrieved and the USV navigates to a selected location. These unmanned at-sea platforms can provide power to other unmanned maritime systems. Specifically, in this project, the power generated onboard can be used to charge aerial drones via a custom flight deck that has been developed for the USV. The recharging capabilities offered by a fleet of such strategically placed recharging stations can significantly benefit aerial drones operating in the maritime domain by eliminating the need to travel back and forth to land or ship based charging stations. The project has resulted in the development of subcomponents, including the FSWW turbine, a novel PTO, an automated anchoring system for the USV, an automated turbine deployment system, and a flight deck with capabilities onboard the USV for landing, direct-contact charging and takeoff of aerial drones. The design and development of these subsystems have culminated in the overall prototype marine hydrokinetic platform (MHK Platform, Fig. 1). Comprehensive lab and field testing have been conducted to validate the functionality and performance of the platform and its components. The project demonstrates the potential for autonomous, unmanned systems to harness renewable energy from marine currents, and provide sustainable power solutions for maritime applications such as coastal surveillance and environmental monitoring; shoreline mapping; search and rescue; oceanographic research; inspection and maintenance of offshore energy installations like wind turbines and oil rigs; oil spill response; maritime disaster response; and aerial surveys, as well as facilitation of data transfer drones and shore stations.
Space Shuttle navigation is defined, in a narrow sense, as the task of maintaining adequate knowledge of vehicle position and velocity. The state of the Orbiter in terms of this goal is described by a vector of at least six elements, three for position and three for velocity, at a given time. These are referred to as the 'state vector' or simply as the state. Shuttle navigation will relay on a blend of ground-based and onboard systems. The onboard systems will be capable of state propagation at all times and will perform state determination during the latter part of the entry from orbit. The ground-based system will be capable of accurate state propagation for free-flight phases. Radio communication will be necessary in order for the ground and onboard capabilities to work as a coordinated system. The use of radio equipment for state determination is discussed for the ascent, orbit, rendezvous, descent, and abort phases of Shuttle missions.
The National Aeronautics and Space Administration s (NASA) Lunar Reconnaissance Orbiter (LRO) launched on June 18, 2009 from the Cape Canaveral Air Force Station aboard an Atlas V launch vehicle and into a direct insertion trajectory to the oon. LRO, which was designed, built, and operated by the NASA Goddard Space Flight Center in Greenbelt, MD, is gathering crucial data on the lunar environment that will help astronauts prepare for long-duration lunar expeditions. The mission has a nominal life of 1 year as its seven instruments find safe landing sites, locate potential resources, characterize the radiation environment, and test new technology. To date, LRO has been operating well within the bounds of its requirements and has been collecting excellent science data images taken from the LRO Camera Narrow Angle Camera of the Apollo landing sites appeared on cable news networks. A significant amount of information on LRO s science instruments is provided at the LRO mission webpage. LRO s Guidance, Navigation and Control (GN&C) subsystem is made up of an onboard attitude control system (ACS) and a hardware suite of sensors and actuators. The LRO onboard ACS is a collection of algorithms based on high level and derived requirements, and reflect the science and operational events throughout the mission lifetime. The primary control mode is the Observing mode, which maintains the lunar pointing orientation and any offset pointing from this baseline. It is within this mode that all science instrument calibrations, slews and science data is collected. Because of a high accuracy requirement for knowledge and pointing, the Observing mode makes use of star tracker (ST) measurement data to determine an instantaneous attitude pointing. But even the star trackers alone do not meet the tight requirements, so a six-state Kalman Filter is employed to improve the noisy measurement data. The Observing mode obtains its rate information from an inertial reference unit (IRU) and in the event of an IRU failure, the rate data is be derived from the star tracker, but with degraded pointing performance. The Delta-V control mode responsibility is to maintain attitude pointing during the cruise trajectory, insertion burns and lunar orbit maintenance by adjustments made to the spacecraft s velocity magnitude and vector direction. The ACS also provides for a thruster based system momentum management algorithm (known as Delta-H) to maintain the system and wheel momentum to within acceptable levels. In the event an anomaly causes the LRO spacecraft to lose the ability to maintain its current attitude pointing, a Sun Safe mode is included in the ACS for the purpose of providing a known power and thermally safe coarse inertial sun attitude for an indefinite period of time, within the manageable limits of the reaction wheels. The Sun Safe mode is also the initial spacecraft control mode off of the launch vehicle and provides for a means to null tip-off rates immediately after separation. The nominal configuration is to use the IRU for rate information in the controller. In the event of a gyro failure a gyroless control mode was developed that computes rate information from the CSS data.
End-to-end integration tests are critical risk reduction efforts for any complex vehicle. Phasing tests are an end-to-end integrated test that validates system directional phasing (polarity) from sensor measurement through software algorithms to end effector response. Phasing tests are typically performed on a fully integrated and assembled flight vehicle where sensors are stimulated by moving the vehicle and the effectors are observed for proper polarity. Orion Multi-Purpose Crew Vehicle (MPCV) Pad Abort 1 (PA-1) Phasing Test was conducted from inertial measurement to Launch Abort System (LAS). Orion Exploration Flight Test 1 (EFT-1) has two end-to-end phasing tests planned. The first test from inertial measurement to Crew Module (CM) reaction control system thrusters uses navigation and flight control system software algorithms to process commands. The second test from inertial measurement to CM S-Band Phased Array Antenna (PAA) uses navigation and communication system software algorithms to process commands. Future Orion flights include Ascent Abort Flight Test 2 (AA-2) and Exploration Mission 1 (EM-1). These flights will include additional or updated sensors, software algorithms and effectors. This paper will explore the implementation of end-to-end phasing tests on a flight vehicle which has many constraints, trade-offs and compromises. Orion PA-1 Phasing Test was conducted at White Sands Missile Range (WSMR) from March 4-6, 2010. This test decreased the risk of mission failure by demonstrating proper flight control system polarity. Demonstration was achieved by stimulating the primary navigation sensor, processing sensor data to commands and viewing propulsion response. PA-1 primary navigation sensor was a Space Integrated Inertial Navigation System (INS) and Global Positioning System (GPS) (SIGI) which has onboard processing, INS (3 accelerometers and 3 rate gyros) and no GPS receiver. SIGI data was processed by GN&C software into thrust magnitude and direction commands. The processing changes through three phases of powered flight: pitchover, downrange and reorientation. The primary inputs to GN&C are attitude position, attitude rates, angle of attack (AOA) and angle of sideslip (AOS). Pitch and yaw attitude and attitude rate responses were verified by using a flight spare SIGI mounted to a 2-axis rate table. AOA and AOS responses were verified by using a data recorded from SIGI movements on a robotic arm located at NASA Johnson Space Center. The data was consolidated and used in an open-loop data input to the SIGI. Propulsion was the Launch Abort System (LAS) Attitude Control Motor (ACM) which consisted of a solid motor with 8 nozzles. Each nozzle has active thrust control by varying throat area with a pintle. LAS ACM pintles are observable through optically transparent nozzle covers. SIGI movements on robot arm, SIGI rate table movements and LAS ACM pintle responses were video recorded as test artifacts for analysis and evaluation. The PA-1 Phasing Test design was determined based on test performance requirements, operational restrictions and EGSE capabilities. This development progressed during different stages. For convenience these development stages are initial, working group, tiger team, Engineering Review Team (ERT) and final.
One of the critical factors affecting flight safety of unmanned aerial vehicles (UAVs) is the amount of vibration they are exposed during a flight. For UAVs under remote operation, vehicle stabilization and navigation is typically achieved by estimating its attitude and position using onboard miniature sensors such as accelerometers, gyroscopes, and GPS via an onboard autopilot. Since precise control of the UAV relies heavily on the attitude sensing, the vibration levels need to be as low as possible in order to minimize the signal noise. Incorrect sensor data can lead to uncertain state estimation causing the multirotor to drift from its desired position. Moreover, high vibrations can induce faults in the safety-critical components of the UAV such as its on-board sensors, motors and propellers. Hence, it is important to monitor the vibration levels during a UAV flight. This paper specifically looks into effect of wind on the vibrations recorded by the autopilot system in an octocopter. Using data from experimental flights under varying wind conditions, we aim to classify between a nominal and anomalous vibration level and define a safety metric known as the Vibrational Anomaly Indicator (VAI) for small UAV systems. Further, we will study effect of high vibrations on the inertial measurement unit (IMU) of an octocopter under laboratory set-up and compute the VAI from IMU measurements. Results would demonstrate the utility of VAI as an health indicator for unmanned flights either in presence of winds or from degraded on-board IMU sensor.
Unmanned Aerial Vehicles (UAV) are playing an increasing role in aviation. Various methods exist for the computation of UAV attitude based on low cost microelectromechanical systems (MEMS) and Global Positioning System (GPS) receivers. There has been a recent increase in UAV autonomy as sensors are becoming more compact and onboard processing power has increased significantly. Correct UAV attitude estimation will play a critical role in navigation and separation assurance as UAVs share airspace with civil air traffic. This paper describes attitude estimation derived by post-processing data from a small low cost Inertial Navigation System (INS) recorded during the flight of a subscale commercial off the shelf (COTS) UAV. Two discrete time attitude estimation schemes are presented here in detail. The first is an adaptation of the Kalman Filter to accommodate nonlinear systems, the Extended Kalman Filter (EKF). The EKF returns quaternion estimates of the UAV attitude based on MEMS gyro, magnetometer, accelerometer, and pitot tube inputs. The second scheme is the complementary filter which is a simpler algorithm that splits the sensor frequency spectrum based on noise characteristics. The necessity to correct both filters for gravity measurement errors during turning maneuvers is demonstrated. It is shown that the proposed algorithms may be used to estimate UAV attitude. The effects of vibration on sensor measurements are discussed. Heuristic tuning comments pertaining to sensor filtering and gain selection to achieve acceptable performance during flight are given. Comparisons of attitude estimation performance are made between the EKF and the complementary filter.
Failure effects analysis of guidance, navigation, and control, data management, and communications subsystems
NASA Dryden Flight Research Center and NASA Johnson Space Center are jointly conducting a phased program to determine the feasibility of the autonomous recovery of a spacecraft using a ram-air parafoil system for the final stages of entry from space to a precision landing. The feasibility is being studied using a flight model of a spacecraft in the generic shape of a flattened biconic that weighs approximately 120 lb and is flown under a commercially available ram-air parafoil. Key components of the vehicle include the global positioning system (GPS) guidance for navigation, a flight control computer, an electronic compass, a yaw rate gyro, and an onboard data recorder. A flight test program is being used to develop and refine the vehicle. The primary flight goal is to demonstrate autonomous flight from an altitude of 3,000 m (10,000 ft) with a lateral offset of 1.6 km (1.0 mi) to a precision soft landing. This paper summarizes the progress to date. Much of the navigation system has been tested, including a heading tracker that was developed using parameter estimation techniques and a complementary filter. The autoland portion of the autopilot is still in development. The feasibility of conducting the flare maneuver without servoactuators was investigated as a means of significantly reducing the servoactuator rate and load requirements.
The Global Position System (GPS) has become a standard method for low cost onboard satellite orbit determination. The use of GPS as an attitude and rate sensor has also been developed in the recent past. Additionally, focus has been given to attitude and orbit estimation using the magnetometer, a low cost, reliable sensor. Combining measurements from both GPS and a magnetometer can provide a robust navigation system which takes advantage of the estimation qualities of both measurements. Ultimately a low cost, accurate navigation system can result, potentially eliminating the need for more costly sensors, including gyroscopes. This work presents the development and preliminary testing of a unified navigation algorithm which produces estimates of attitude, angular rate, position, and velocity for a low earth orbit (LEO) spacecraft. The system relies on GPS phase, range, and range rate data as well as magnetometer data. The algorithm used is an extended Kalman filter (EKF) developed to provide LEO attitude, orbit, and rate estimates using magnetometer and sun sensor data. Incorporating sun sensor data into the EKF improved the attitude and rate estimates. For many LEO spacecraft the sun data is available during only a portion of the orbit. However, GPS data is available continuously throughout the orbit. GPS can produce accurate orbit estimates and combining GPS and magnetometer data improves the attitude and rate estimates. The magnetometer based EKF can converge from large initial errors in position, velocity, and 3 attitude . Combining the magnetometer and GPS data into a single EKF will provide a more robust and accurate system. The EKF is based on an existing EKF. The GPS measurement models for phase, range, and range rate are incorporated into the existing structure of the filter. The original EKF produced the orbit estimates in terms of Keplerian elements. Due to the nature of the GPS measurements and ease of computation, the orbit estimates are converted to the Cartesian position and velocity. The measurement model for the magnetometer is adjusted for this change in the state and the measurements. Preliminary test results based on simulated GPS and magnetometer data are included.
A coherent Doppler lidar, referred to as Navigation Doppler Lidar (NDL), has been developed at NASA Langley Research Center for providing simultaneous but independent precision velocity and altitude measurements to a spacecraft’s navigation system during the final stages of landing, starting at approximately 5 km altitude. Onboard the Intuitive Machines NOVA-C lunar lander (also known as Odysseus), NDL provided fully reliable measurements within its intended operational range without any false alarms and demonstrated the sensitivity of coherent lidar by measuring velocity and range to the ground from up to 10 km altitude. The data collected during this mission illustrates the capability of NDL for future lunar and interplanetary missions, enhancing navigation accuracy and safety.
The Global Positioning System (GPS) has become a standard method for low cost onboard satellite orbit determination. The use of a GPS receiver as an attitude and rate sensor has also been developed in the recent past. Additionally, focus has been given to attitude and orbit estimation using the magnetometer, a low cost, reliable sensor. Combining measurements from both GPS and a magnetometer can provide a robust navigation system that takes advantage of the estimation qualities of both measurements. Ultimately a low cost, accurate navigation system can result, potentially eliminating the need for more costly sensors, including gyroscopes.