SEARCH · Search NASA

Results for “proximity operations and docking”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records

All sources NASA NTRS DOE OSTI

NASA NTRS2011

A Summary of the Rendezvous, Proximity Operations, Docking, and Undocking (RPODU) Lessons Learned from the Defense Advanced Research Project Agency (DARPA) Orbital Express (OE) Demonstration System Mission

The Guidance, Navigation, and Control (GN&C) Technical Discipline Team (TDT) sponsored Dr. J. Russell Carpenter, a Navigation and Rendezvous Subject Matter Expert (SME) from NASA's Goddard Space Flight Center (GSFC), to provide support to the Defense Advanced Research Project Agency (DARPA) Orbital Express (OE) rendezvous and docking flight test that was conducted in 2007. When that DARPA OE mission was completed, Mr. Neil Dennehy, NASA Technical Fellow for GN&C, requested Dr. Carpenter document his findings (lessons learned) and recommendations for future rendezvous missions resulting from his OE support experience. This report captures lessons specifically from anomalies that occurred during one of OE's unmated operations.

Dennehy, Cornelius J.↗

NASA NTRS2011

Orion Handling Qualities During ISS Proximity Operations and Docking

NASA's Orion spacecraft is designed to autonomously rendezvous and dock with many vehicles including the International Space Station. However, the crew is able to assume manual control of the vehicle s attitude and flight path. In these instances, Orion must meet handling qualities requirements established by NASA. Two handling qualities assessments were conducted at the Johnson Space Center to evaluate preliminary designs of the vehicle using a six degree of freedom, high-fidelity guidance, navigation, and control simulation. The first assessed Orion s handling qualities during the last 20 ft before docking, and included both steady and oscillatory motions of the docking target. The second focused on manual acquisition of the docking axis during the proximity operations phase and subsequent station-keeping. Cooper-Harper handling qualities ratings, workload ratings and comments were provided by 10 evaluation pilots for the docking study and 5 evaluation pilots for the proximity operations study. For the docking task, both cases received 90% Level 1 (satisfactory) handling qualities ratings, exceeding NASA s requirement. All ratings for the ProxOps task were Level 1. These evaluations indicate that Orion is on course to meet NASA's handling quality requirements for ProxOps and docking.

Stephens, John-Paul↗

NASA NTRS2007

Orion Rendezvous, Proximity Operations, and Docking Design and Analysis

The Orion vehicle will be required to perform rendezvous, proximity operations, and docking with the International Space Station (ISS) and the Earth Departure Stage (EDS)/Lunar Landing Vehicle (LLV) stack in Low Earth Orbit (LEO) as well as with the Lunar Landing Vehicle in Low Lunar Orbit (LLO). The RPOD system, which consists of sensors, actuators, and software is being designed to be flexible and robust enough to perform RPOD with different vehicles in different environments. This paper will describe the design and the analysis which has been performed to date to allow the vehicle to perform its mission. Since the RPOD design touches on many areas such as sensors selection and placement, trajectory design, navigation performance, and effector performance, it is inherently a systems design problem. This paper will address each of these issues in order to demonstrate how the Orion RPOD has been designed to accommodate and meet all the requirements levied on the system.

D'Souza, Christopher↗

NASA NTRS2009

Proximity Operations and Docking Sensor Development

The Next Generation Advanced Video Guidance Sensor (NGAVGS) has been under development for the last three years as a long-range proximity operations and docking sensor for use in an Automated Rendezvous and Docking (AR&D) system. The first autonomous rendezvous and docking in the history of the U.S. Space Program was successfully accomplished by Orbital Express, using the Advanced Video Guidance Sensor (AVGS) as the primary docking sensor. That flight proved that the United States now has a mature and flight proven sensor technology for supporting Crew Exploration Vehicles (CEV) and Commercial Orbital Transport Systems (COTS) Automated Rendezvous and Docking (AR&D). NASA video sensors have worked well in the past: the AVGS used on the Demonstration of Autonomous Rendezvous Technology (DART) mission operated successfully in spot mode out to 2 km, and the first generation rendezvous and docking sensor, the Video Guidance Sensor (VGS), was developed and successfully flown on Space Shuttle flights in 1997 and 1998. 12 Parts obsolescence issues prevent the construction of more AVGS units, and the next generation sensor was updated to allow it to support the CEV and COTS programs. The flight proven AR&D sensor has been redesigned to update parts and add additional capabilities for CEV and COTS with the development of the Next Generation AVGS at the Marshall Space Flight Center. The obsolete imager and processor are being replaced with new radiation tolerant parts. In addition, new capabilities include greater sensor range, auto ranging capability, and real-time video output. This paper presents some sensor hardware trades, use of highly integrated laser components, and addresses the needs of future vehicles that may rendezvous and dock with the International Space Station (ISS) and other Constellation vehicles. It also discusses approaches for upgrading AVGS to address parts obsolescence, and concepts for minimizing the sensor footprint, weight, and power requirements. In addition, the testing of the brassboard and proto-type NGAVGS units will be discussed along with the use of the NGAVGS as a proximity operations and docking sensor.

Howard, Richard T.↗

NASA NTRS1992

Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations: Translational controller results

The reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Maximum Mission (SMM) satellite simulation. In utilizing these fuzzy learning techniques, we also use the Approximate Reasoning based Intelligent Control (ARIC) architecture, and so we use two terms interchangeable to imply the same. This activity is carried out in the Software Technology Laboratory utilizing the Orbital Operations Simulator (OOS). This report is the deliverable D3 in our project activity and provides the test results of the fuzzy learning translational controller. This report is organized in six sections. Based on our experience and analysis with the attitude controller, we have modified the basic configuration of the reinforcement learning algorithm in ARIC as described in section 2. The shuttle translational controller and its implementation in fuzzy learning architecture is described in section 3. Two test cases that we have performed are described in section 4. Our results and conclusions are discussed in section 5, and section 6 provides future plans and summary for the project.

Jani, Yashvant↗

NASA NTRS1992

Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations: Attitude control results

As part of the RICIS activity, the reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Max satellite simulation. This activity is carried out in the software technology laboratory utilizing the Orbital Operations Simulator (OOS). This report is deliverable D2 Altitude Control Results and provides the status of the project after four months of activities and outlines the future plans. In section 2 we describe the Fuzzy-Learner system for the attitude control functions. In section 3, we provide the description of test cases and results in a chronological order. In section 4, we have summarized our results and conclusions. Our future plans and recommendations are provided in section 5.

Jani, Yashvant↗

NASA NTRS1993

Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations: Special approach/docking testcase results

As part of the RICIS project, the reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Maximum Mission (SMM) satellite simulation. In utilizing these fuzzy learning techniques, we use the Approximate Reasoning based Intelligent Control (ARIC) architecture, and so we use these two terms interchangeably to imply the same. This activity is carried out in the Software Technology Laboratory utilizing the Orbital Operations Simulator (OOS) and programming/testing support from other contractor personnel. This report is the final deliverable D4 in our milestones and project activity. It provides the test results for the special testcase of approach/docking scenario for the shuttle and SMM satellite. Based on our experience and analysis with the attitude and translational controllers, we have modified the basic configuration of the reinforcement learning algorithm in ARIC. The shuttle translational controller and its implementation in ARIC is described in our deliverable D3. In order to simulate the final approach and docking operations, we have set-up this special testcase as described in section 2. The ARIC performance results for these operations are discussed in section 3 and conclusions are provided in section 4 along with the summary for the project.

Jani, Yashvant↗

NASA NTRS1992

Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations

As part of the Research Institute for Computing and Information Systems (RICIS) activity, the reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Max satellite simulation. This activity is carried out in the software technology laboratory utilizing the Orbital Operations Simulator (OOS). This interim report provides the status of the project and outlines the future plans.

Jani, Yashvant↗

NASA NTRSDate not supplied

Orion GN&C Sequencing for Off-Nominal Rendezvous, Proximity Operations, and Docking

This paper discusses the Concept of Operations of the nine contingency strategies available for off-nominal Rendezvous, Proximity Operations, and Docking of Orion with the Gateway in a Near-Rectilinear Halo Orbit around the Moon. Explanations are provided for each contingency strategy, how they are initiated, when they can be initiated, and how they are designed to protect the crew. Then an overview is provided of the sequencing of the Guidance, Navigation, and Control flight software used to achieve each contingency strategy, in the form of Phases, Segments, Activities, and Modes. These off-nominal scenarios re-quire the definition of new Segments and Activities.

Jordan S. Abell↗

NASA NTRS2020

GN&C Sequencing for Orion Rendezvous, Proximity Operations, and Docking

As part of the Artemis program to return humans to the lunar surface, the National Aeronautics and Space Administration is planning to use the Orion Multi- Purpose Crew Vehicle to transport crew to a small orbital platform called Gate- way in cislunar space. To facilitate this activity, Orion is required to perform Rendezvous, Proximity Operations, and Docking (RPOD) with both the Gate- way and the launch vehicle upper stage. The Orion spacecraft uses sequencing in the form of Phases, Segments, Activities, and Modes (PSAM) to configure Guidance, Navigation, & Control (GN&C) software during each portion of the mission. Significant updates to Orion PSAM definitions are required for RPOD. This paper describes the process of defining these new sequencing elements, implementing them in prototype flight software, and testing them in an integrated simulation environment. First, requirements are specified to determine the nominal and off-nominal sequencing behavior necessary to complete the mission. These requirements also specify which software functions should be fully autonomous and which functions require manual interactions from crew or ground operators. Next, the RPOD concept of operations is defined with detailed events listed in a mission timeline. Third, a state machine diagram is developed to show all PSAM states, including all possible transitions between them. After this, the PSAM states and transitions are entered into a sequencing software emulator and parameter values and modes are defined for GN&C software elements. Finally, the PSAM architecture is tested within an integrated simulation environment by connecting it with prototypes of relevant GN&C flight software elements and with detailed vehicle models. After the sequencing design has been finalized and tested, it is implemented in flight software.

Schulte, Peter Z.↗

NASA NTRS1991

Approximate reasoning-based learning and control for proximity operations and docking in space

A recently proposed hybrid-neutral-network and fuzzy-logic-control architecture is applied to a fuzzy logic controller developed for attitude control of the Space Shuttle. A model using reinforcement learning and learning from past experience for fine-tuning its knowledge base is proposed. Two main components of this approximate reasoning-based intelligent control (ARIC) model - an action-state evaluation network and action selection network are described as well as the Space Shuttle attitude controller. An ARIC model for the controller is presented, and it is noted that the input layer in each network includes three nodes representing the angle error, angle error rate, and bias node. Preliminary results indicate that the controller can hold the pitch rate within its desired deadband and starts to use the jets at about 500 sec in the run.

Berenji, Hamid R.↗

NASA NTRS1991

The role of smart systems in rendezvous, close proximity operations and docking maneuvers

Various missions scenarios (Space Station logistics, LEO and GEO services, and SEI operation) will involve flexibility in mission management. This means operations will be one or a combination of the following: autonomous, supervised autonomous, and machine aided manual control. Smart Systems will likely play a significant role in making these missions successful from a safety/reliability perspective and less costly from an operations perspective. This does not imply that Smart Systems need to be super sophisticated. On the contrary, Smart Systems have been described as automated intelligence that if a person had done it wrong, it would be considered stupid. The first part of this paper will describe the types of Smart System techniques involved in AR and CC, their specifications, duties, and interactions. Next will be a discussion of the work performed under the auspice of the ALS Program to further Expert Systems applications imbedded in the control process, NASA/JSC CRAD, and other related IRAD projects. This will include issues pertaining to the following: integration, speed, knowledge encapsulation, and cooperative systems. Finally, a brief description will be offered to outline the major obstacles for the acceptance of Smart Systems in critical applications.

Szatkowski, Gerard P.↗

NASA NTRS1988

An evaluation of interactive displays for trajectory planning and proximity operations

Rendezvous, docking, and other Space Station proximity operations will be routine in nature in years to come. However, the specific parameters describing each maneuver, such as initial range and position, will vary from mission to mission so a means for depicting and interacting with graphic representations of proposed mission plans is necessary. Orbital operations are inherently non-intuitive due to non-linearities in the equations of motion of orbiting vehicles. Consequently, relative motion between two spacecraft cannot always be easily visualized. For these reasons, real time interactive visual aids and planning tools will be helpful, if not necessary, for future missions both in pre-flight training and on-orbit. Two such displays, Navie and eivaN, are currently available for examination and human factors testing. Since the docking tasks were fundamentally different with each device and because Navie imposed more constraints on the users than eivaN did, the orbital mechanics effects had a more pronounced effect on the Navie results.

Brody, Adam R.↗

NASA NTRS1993

Interactive displays for trajectory planning and proximity operations

Rendezvous, docking, and other Space Station proximity operations (PROX OPS) will be conducted routinely in space. Real-time interactive visual aids and planning tools will be helpful, if not necessary, for future missions both in preflight training and on orbit. Two such displays, eivaN and Navie, are currently available for examination and human factors testing. A study was conducted in which data were collected from eight test subjects. Solution times for both devices decreased rapidly with experience. Neither fuel usage nor the number of waypoints (burns) decreased with experience. With Navie, medians of solution time and fuel consumption totaled over all subjects peaked at one of two starting points above the V-bar with monotonically decreasing values in both directions. This pattern did not appear with eivaN values. Since the docking tasks were fundamentally different with each device, and because Navie imposed more constraints on the users than eivaN did, the orbital mechanics effects had a more pronounced effect on the Navie results than on the eivaN data.

Brody, Adam R.↗

NASA NTRS1988

Spacecraft flight simulation: A human factors investigation into the man-machine interface between an astronaut and a spacecraft performing docking maneuvers and other proximity operations

The anticipated increase in rendezvous and docking activities in the various space programs in the Space Station era necessitates a renewed interest in manual docking procedures. Ten test subjects participated in computer simulated docking missions in which the influence of initial velocity was examined. All missions started from a resting position of 304.8 meters (1000 feet) along the space station's +V-bar axis. Test subjects controlled their vehicle with a translational hand controller and digital auto pilot which are both virtually identical to their space shuttle counterparts. While the 0.1 percent rule (range rate is equal to 0.1 percent of the range) used by space shuttle pilots is comfortably safe, it is revealed to be extremely inefficient in terms of time and not justifiable in terms of marginal safety. Time is worth money, not only because of training and launch costs, but because the sooner a pilot and spacecraft return from a mission, the sooner they can begin the next one. Inexperienced test subjects reduced the costs of simulated docking by close to a factor of 2 and achieved safe dockings in less than 4 percent of the time the baseline approach would entail. This reduction in time can be used to save lives in the event of an accident on orbit, and can tremendously reduce docking costs if fuel is produced from waste water on orbit.

Brody, Adam R.↗

NASA NTRS2007

Automated Rendezvous and Docking Sensor Testing at the Flight Robotics Laboratory

Mitchell, J.↗

NASA NTRS2007

Automated Rendezvous and Docking Sensor Testing at the Flight Robotics Laboratory

Howard, Richard T.↗

NASA NTRS2007

Multi-Sensor Testing for Automated Rendezvous and Docking Sensor Testing at the Flight Robotics Laboratory

The Exploration Systems Architecture defines missions that require rendezvous, proximity operations, and docking (RPOD) of two spacecraft both in Low Earth Orbit (LEO) and in Low Lunar Orbit (LLO). Uncrewed spacecraft must perform automated and/or autonomous rendezvous, proximity operations and docking operations (commonly known as AR&D). The crewed missions may also perform rendezvous and docking operations and may require different levels of automation and/or autonomy, and must provide the crew with relative navigation information for manual piloting. The capabilities of the RPOD sensors are critical to the success of the Exploration Program. NASA has the responsibility to determine whether the Crew Exploration Vehicle (CEV) contractor proposed relative navigation sensor suite will meet the requirements. The relatively low technology readiness level of AR&D relative navigation sensors has been carried as one of the CEV Project's top risks. The AR&D Sensor Technology Project seeks to reduce the risk by the testing and analysis of selected relative navigation sensor technologies through hardware-in-the-loop testing and simulation. These activities will provide the CEV Project information to assess the relative navigation sensors maturity as well as demonstrate test methods and capabilities. The first year of this project focused on a series of"pathfinder" testing tasks to develop the test plans, test facility requirements, trajectories, math model architecture, simulation platform, and processes that will be used to evaluate the Contractor-proposed sensors. Four candidate sensors were used in the first phase of the testing. The second phase of testing used four sensors simultaneously: two Marshall Space Flight Center (MSFC) Advanced Video Guidance Sensors (AVGS), a laser-based video sensor that uses retroreflectors attached to the target vehicle, and two commercial laser range finders. The multi-sensor testing was conducted at MSFC's Flight Robotics Laboratory (FRL) using the FRL's 6-DOF gantry system, called the Dynamic Overhead Target System (DOTS). The target vehicle for "docking" in the laboratory was a mockup that was representative of the proposed CEV docking system, with added retroreflectors for the AVGS. The multi-sensor test configuration used 35 open-loop test trajectories covering three major objectives: (1) sensor characterization trajectories designed to test a wide range of performance parameters; (2) CEV-specific trajectories designed to test performance during CEV-like approach and departure profiles; and (3) sensor characterization tests designed for evaluating sensor performance under more extreme conditions as might be induced during a spacecraft failure or during contingency situations. This paper describes the test development, test facility, test preparations, test execution, and test results of the multi-sensor series of trajectories.

Brewster, L.↗