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Kenneth M Getzandanner

Publications and source records attributed to Kenneth M Getzandanner.

8 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Contact with Bennu! Flight Performance Versus Prediction of OSIRIS-REx TAG Sample Collection

The Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) mission collected a sample from the surface of the near-Earth asteroid (101955) Bennu in late 2020. Bennu challenged the team with a surface that was much rockier than expected, resulting in modifications to the prelaunch design of the Touch And Go (TAG) sequence. Following enhancements in onboard trajectory correction, ground-based navigation, and maneuver execution error modeling, the spacecraft was delivered to the chosen TAG site within 1 m of the target, and a sample was successfully collected on the first attempt. This paper provides a comprehensive description of all flight dynamics aspects of TAG trajectory planning and execution. It also describes hazard map generation and how that combined with error analysis results to predict the probability of safe contact before TAG and the onboard wave-off determination during TAG.

Kevin E Berry↗
NASA NTRS2021

Architecture and Operations of the OSIRIS-REx Independent Navigation Team

The Origins, Spectral Interpretation, Resource Identification, Security-Regolith Explorer (OSIRIS-REx) GoddardSpace Flight Center (GSFC) Independent Navigation Team (INT) performs center-finding and landmark-basedOptical Navigation (OpNav), Orbit Determination (OD), maneuver verification, and additional analyses in support ofnavigation operations motivated by a stringent set of science requirements. The INT has adopted a streamlined andagile approach to navigation operations support via a virtual operations environment, known as "OREX-NAV",which leverages existing capabilities of the Space Science Mission Operations (SSMO) virtual Multi-MissionOperations Center (vMMOC). The virtual environment architecture of OREX-NAV enables the INT to perform dailyoperational tasks and seamlessly interface with external mission networks, regardless of physical location. Throughthe automation and process adopted, the INT is able to keep pace with the rapid cadence of required deliverables.

OSIRIS-REx↗
NASA NTRSDate not supplied

Osiris-Rex Post-Tag Observation Trajectory Design and Navigation Performance

NASA’s OSIRIS-REx spacecraft successfully collected a sample of asteroid regolith from the surface of near-Earth asteroid Bennu in October of 2020. Subsequent imaging of the sampler head showed material leaking from the collection mechanism, thus stowage of the sample precluded execution of any planned maneuvers in the following days. Optical navigation imaging also ceased in the days following sample collection. The desire to image the sample site to investigate the results of the spacecraft-to-surface interaction led to the Navigation team designing a trajectory to return to Bennu after several months in order to image the surface one final time. After several iterations a trajectory design was created that satisfied the numerous constraints that were levied in order to place utmost importance on the safety of the spacecraft and stowed sample, while also closely emulating previously obtained imaging conditions to provide a close comparison of site pre- and post-contact. Significant analysis was necessary in order to reliably reacquire the asteroid after several months without optical navigation imagery. The final design required five maneuvers to return the spacecraft to Bennu and perform a final flyby of the asteroid at a distance of 3.8 kilometers. Successful execution of the phase provided key insights regarding the performance of the sample collection activities and the subsurface composition of the asteroid.

Daniel R Wibben↗
NASA NTRSDate not supplied

Cross-Calibration of GNC and OLA LIDAR Systems Onboard OSIRIS-REx

The Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) mission carried two distinct light detection and ranging (LIDAR) systems: A scanning LIDAR called OSIRIS-REx Laser Altimeter (OLA) as part of the science payload, and a flash LIDAR system has part of the Guidance, Navigation, & Control (GNC) subsystem to serve as a navigation sensor during TAG. This presents a unique opportunity to compare the performance of the two LIDAR systems in close proximity to a small asteroid body. During the Orbital B mission phase, between June to August 2019, the OSIRIS-REx spacecraft orbited Bennu in a near-circular terminator orbit during which the altitude above the surface varied between 645 m to 740 m. Over five-week period observations were recorded with the OLA instrument that were subsequently used to construct a global digital terrain map (DTM) with a resolution of 5 cm and accuracy of ±20 cm. This model provides an excellent reference for assessing the performance of the GNC LIDAR system. Two different GNC LIDAR checkout activities were also conducted during the Orbital B phase: A limb-crossing check-out featured a series of slews to collect GNC LIDAR data across varying ranges and phase angles and operate the automatic gain control modes of the device; An OLA-GNC LIDAR cross calibration was designed to collect data from both the OLA and GNC LIDAR devices with overlapping footprints while the spacecraft was pointed nadir. This paper compares the on-orbit performance observed during the cross-calibration activity. The OLA-based global DTM and point clouds are used to evaluate the GNC LIDAR not available during previous analysis. The GNC LIDAR measurements were found to be well within accuracy and precision specified for the instrument, but much noisier than the measurements from OLA within this operating regime.

Jason M Leonard↗
NASA NTRSDate not supplied

Comparing Pre-Launch Assumptions to In-Flight Navigation Performance of OSIRIS-REx

The Sample Return Capsule (SRC) onboard the NASA Origins, Spectral Inter-pretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx)spacecraft is currently carrying samples of the B-type asteroid Bennu for safe re-turn to Earth at the Utah Test and Training Range on September 24, 2023. These samples were collected during the Touch And Go (TAG) sampling event on October 20, 2020, when the spacecraft contacted the surface for a few seconds at a location less than 1 meter from the target. The unprecedented navigation performance achieved during that event was the culmination of experience gained during two years of cruise and two years of increasingly challenging operation sat Bennu. As we had hoped, the proximity navigation performance at Bennu exceeded pre-launch analysis. This paper will compare the navigation performance through the proximity operation phases to our pre-launch analysis and will quantify how refinements of the small force models governing the spacecraft motion near Bennu considerably improved the down-track state predictions leading up to the successful TAG event. It was evident to the team and to expert peer reviewers during the design phase that exquisite model fidelity and aggressive operational concepts, which challenged and advanced the state of the art for deep space proximity operations, would be required to meet the mission’s objectives. This paper summarizes the superlative achievements of the team in rising to and overcoming these challenges.

Peter G Antresian↗
NASA NTRSDate not supplied

Reinforcement Learning for Spacecraft Navigation & Environment Characterization in the Planar-Restricted Two-Body Problem

During mission planning and execution, spacecraft operators must balance data collection and downlink, systems constraints, human factors, and navigation. As missions become increasingly complex and ambitious, these factors become more intricately entwined and conflicted. For example, a spacecraft’s position must be known accurately in order to point to and image a target. Large position errors may cause missed observations or require additional scanning that increases operations complexity and data volume. Some observations require imaging from specific relative geometries which adds orbit control and timing considerations. Adjusting the orbit may allow for optimal observability of environmental parameters and/or enable more efficient sensor coverage, but maneuver execution error adds uncertainty to the current state which impacts both characterization and coverage objectives.

Navigation↗
NASA NTRSDate not supplied

Reinforcement Learning for Spacecraft Navigation & Environment Characterization in the Planar-Restricted Two-Body Problem

As science, exploration, and commercial space missions become increasingly complex, so does the need for efficient, autonomous, and integrated spacecraft navigation and operations techniques. Key operational functions, including data collection and transmission, environment characterization, systems constraints, human factors, and navigation, often are intertwined and conflicted. Deep Reinforcement Learning (DRL) offers a framework for addressing integrated spacecraft navigation and planning in an uncertain dynamical environment. The goal of this study is to evaluate the utility of DRL for integrated spacecraft navigation and planning. This is achieved by developing a simple environmental characterization training environment in the Planar-Restricted 2-Body Problem (PR2BP), establishing benchmarks and heuristic baselines, and designing a previously unstudied Markov Decision Process (MDP) formulation. This MDP formulation enables the spacecraft DRL agents to appropriately balance navigation and actuation capabilities. The resulting DRL-derived policy exceeds a random or untrained policy and meets or exceeds the level of performance of a heuristic without actuation. In the process, valuable intuition is gained about the problem with insight into how DRL methods could scale to increasingly more realistic scenarios, including net-work design and training architectures, efficient state space representations, and methods for encouraging exploration in a parametric action space, among others.

navigation↗