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NASA NTRSDate not supplied

Monocular Ranging for Small Unmanned Aerial Systems in the Far-Field

Recent proliferation of small Unmanned Aerial Systems (sUAS) applications requires onboard collision avoidance systems to mitigate the risk of collision with non-cooperative aircraft and manned aircraft, which may not see sUAS in time to perform an avoidance maneuver. An attractive avenue for onboard collision avoidance is the utilization of machine vision cameras due to their low size, weight and power (SWaP) requirements. In this paper, we characterize the range performance of a machine vision system developed in-house and mounted onto an sUAS. The technique was designed to estimate the performance of a sense-and-avoid system to ensure that the sensing components meet the well-clear requirements for the chosen platform and avoidance strategy. Experimental flight-test data was acquired from test-flights flown along multiple collision geometries for two intruders: a general Aviation (GA) aircraft and a fixed-wing sUAS. The ownship and both intruders were instrumented with inertial navigation systems (INS) recording position and attitude information. The range at first detection, 𝑹𝟎, was extracted from in-flight imagery of head-on collision course geometry synchronized with INS data from both aircraft and ground-truth values extracted from the raw imagery. This initial detection distance, 𝑹𝟎, scales with atmospheric attenuation. Therefore, under clear sky conditions, the derived 𝑹𝟎 value represents the upper bound on the detection range achievable by the test configuration of the detector. Results indicate that the maximum initial detection distance for a 4k resolution action camera fitted with a 41º Field of View (FOV) lens is 2.763 ± 0.037 km for a GA aircraft and 0.881 ± 0.061 km for a fixed-wing sUAS, respectively. The in results this study suggest that a vision-based detect and track system may be analyzed using the sensor characterization and contextualized within aircraft well-clear volumes.

Chester V. Dolph↗

NASA NTRS2018

Realistic Covariance Generation for the GPM Spacecraft

A covariance realism process for NASA's Global Precipitation Measurement (GPM) spacecraft is detailed. The GPM spacecraft is in a low earth orbit, and performs collision avoidance maneuvers few times a year. Currently GPM is below the International Space Station (ISS). So, in addition to cataloged debris objects, GPM must contend with smallsat/cubesat objects that are deployed from the ISS. Both operational scenarios require complete knowledge of the expected GPM prediction errors as a function of time. In this study, we present a method for generating realistic predicted covariance that uses linear propagation of the covariance with the addition of process noise. Further analyses are presented for the process noise ''tuning'' that generates an inflation factor based on the observed error statistics of the predictive satellite trajectories when compared to the definitive ones. Different tuning strategies are considered and compared via a Goodness-of-Fit testing for the Gaussian properties of the scaled covariance. SpaceNav's realistic covariance generation approach takes into account the contribution of predicted maneuver errors in the increased propagation uncertainty. Corresponding maneuver uncertainty is injected into the state uncertainty, and is used within the collision avoidance process to determine the collision risk for close approach events that follow a maneuver. This is a critical step in the maneuver planning process that provides the satellite operator with an accurate quantification of the collision probability for planned maneuvers. Using this information, an informed decision can be made to proceed with a maneuver if the collision risk is acceptable. This approach is validated by Monte-Carlo simulations and results are presented.

spacecraft uncertainty propagation↗

NASA NTRS2013

A Geometric Analysis to Protect Manned Assets from Newly Launched Objects - Cola Gap Analysis

A safety risk was identified for the International Space Station (ISS) by The Aerospace Corporation, where the ISS would be unable to react to a conjunction with a newly launched object following the end of the launch Collision Avoidance (COLA) process. Once an object is launched, there is a finite period of time required to track, catalog, and evaluate that new object as part of standard onorbit COLA screening processes. Additionally, should a conjunction be identified, there is an additional period of time required to plan and execute a collision avoidance maneuver. While the computed prelaunch probability of collision with any object is extremely low, NASA/JSC has requested that all US launches take additional steps to protect the ISS during this "COLA gap" period. This paper details a geometric-based COLA gap analysis method developed by the NASA Launch Services Program to determine if launch window cutouts are required to mitigate this risk. Additionally, this paper presents the results of several missions where this process has been used operationally.

Hametz, Mark E.↗

NASA NTRS1989

Improved computer simulation of the TCAS 3 circular array mounted on an aircraft

The Traffic advisory and Collision Avoidance System (TCAS) is being developed by the Federal Aviation Administration (FAA) to assist aircraft pilots in mid-air collision avoidance. This report concentrates on the computer simulation of the enchanced TCAS 2 systems mounted on a Boeing 727. First, the moment method is used to obtain an accurate model for the enhanced TCAS 2 antenna array. Then, the OSU Aircraft Code is used to generate theoretical radiation patterns of this model mounted on a simulated Boeing 727 model. Scattering error curves obtained from these patterns can be used to evaluate the performance of this system in determining the angular position of another aircraft with respect to the TCAS-equipped aircraft. Finally, the tracking of another aircraft is simulated when the TCAS-equipped aircraft follows a prescribed escape curve. In short, the computer models developed in this report have generality, completeness and yield reasonable results.

Rojas, R. G.↗

NASA NTRS2017

Interpretable Categorization of Heterogeneous Time Series Data

We analyze data from simulated aircraft encounters to validate and inform the development of a prototype aircraft collision avoidance system. The high-dimensional and heterogeneous time series dataset is analyzed to discover properties of near mid-air collisions (NMACs) and categorize the NMAC encounters. Domain experts use these properties to better organize and understand NMAC occurrences. Existing solutions either are not capable of handling high-dimensional and heterogeneous time series datasets or do not provide explanations that are interpretable by a domain expert. The latter is critical to the acceptance and deployment of safety-critical systems. To address this gap, we propose grammar-based decision trees along with a learning algorithm. Our approach extends decision trees with a grammar framework for classifying heterogeneous time series data. A context-free grammar is used to derive decision expressions that are interpretable, application-specific, and support heterogeneous data types. In addition to classification, we show how grammar-based decision trees can also be used for categorization, which is a combination of clustering and generating interpretable explanations for each cluster. We apply grammar-based decision trees to a simulated aircraft encounter dataset and evaluate the performance of four variants of our learning algorithm. The best algorithm is used to analyze and categorize near mid-air collisions in the aircraft encounter dataset. We describe each discovered category in detail and discuss its relevance to aircraft collision avoidance.

Drones↗

NASA NTRS2015

An Updated Process for Automated Deepspace Conjunction Assessment

There is currently a high level of interest in the areas of conjunction assessment and collision avoidance from organizations conducting space operations. Current conjunction assessment activity is mainly focused on spacecraft and debris in the Earth orbital environment [1]. However, collisions are possible in other orbital environments as well [2]. This paper will focus on the current operations of and recent updates to the Multimission Automated Deep Space Conjunction Assessment Process (MADCAP) used at the Jet Propulsion Laboratory for NASA to perform conjunction assessment at Mars and the Moon. Various space agencies have satellites in orbit at Mars and the Moon with additional future missions planned. The consequences of collisions are catastrophically high. Intuitive notions predict low probability of collisions in these sparsely populated environments, but may be inaccurate due to several factors. Orbits of scientific interest often tend to have similar characteristics as do the orbits of spacecraft that provide a communications relay for surface missions. The MADCAP process is controlled by an automated scheduler which initializes analysis based on a set timetable or the appearance of new ephemeris files either locally or on the Deep Space Network (DSN) Portal. The process then generates and communicates reports which are used to facilitate collision avoidance decisions. The paper also describes the operational experience and utilization of the automated tool during periods of high activity and interest such as: the close approaches of NASA's Lunar Atmosphere & Dust Environment Explorer (LADEE) and Lunar Reconnaissance Orbiter (LRO) during the LADEE mission. In addition, special consideration was required for the treatment of missions with rapidly varying orbits and less reliable long term downtrack estimates; in particular this was necessitated by perturbations to MAVEN's orbit induced by the Martian atmosphere. The application of special techniques to non-operational spacecraft with large uncertainties is also studied. Areas for future work are also described. Although the applications discussed in this paper are in the Martian and Lunar environments, the techniques are not unique to these bodies and could be applied to other orbital environments.

collision↗

NASA NTRSDate not supplied

Exploring the Impact of Compliance With Maneuvering Guidelines for Space Traffic Management

If the current estimate of proposed large constellations is realized, the near-Earth space environment will see more than 50,000 new satellites added to the catalog of resident space objects (RSOs) in the coming decade. This is an order of magnitude increase from the current population and poses new policy challenges as global operators seek to leverage the benefits these new satellite systems provide while also ensuring a sustainable approach to collision avoidance. Various guidelines have been proposed to date to support this effort, including the development of right of way rules to guide how a collision avoidance maneuver should be performed, and how the maneuver burden should be shared between the two satellites involved. However, it is very difficult to evaluate and compare proposed guidelines due to the complex nature of space traffic and the rapidly changing space environment. This study seeks to address this issue by utilizing the Virtual Environment for Space Traffic Analysis (VESTA), a high-fidelity simulation tool that has been developed at Georgia Tech over the past few years with the explicit purpose of evaluating the future of space traffic environment. Using this tool, a sensitivity study is performed that incorporates a likely set of future large constellations and provides metrics on the impact that a select set of proposed maneuvering guidelines would have on operators given realistic variations in spacecraft capabilities (e.g. maneuverability and propulsion capabilities), and other factors (owner-country, public vs. private, etc.). Specifically, this study compares three potential right of way rules: 1) a rule based on maneuverability proposed by the Space Safety Coalition, 2) a rule based on the geometry of the spacecraft rendezvous, and 3) a rule that equally distributes the maneuver burden between two operators, The results highlight general observations on the effectiveness and limitations of each of the proposed maneuvering guidelines. In addition to the choice of right of way rule, success of space traffic management will be significantly impacted by compliance – which operators, or how many operators, comply with the space traffic rules. The findings of this analysis have important implications for future methods that could be pursued to put in place right of way rules. For example, non-binding right of way rules may have variable levels of compliance that differ across actors. The impact of compliance by just one nation, or non-compliance by just one nation, help to demonstrate the impact of ensuring all major space actors coordinate on this effort. Overall, this analysis provides insight into the relative gains in safety (decrease in collision risk) that would likely result from more politically intense efforts to increase the number of countries implementing space traffic management rules.

conjunction assessment↗

NASA NTRSDate not supplied

NASA CARA Prelaunch Analysis and Process

NASA implemented an official Procedural Requirement (NPR) 8079.1 in June 2023, establishing the minimum collision avoidance requirements and associated operational protocols for NASA space flight programs, projects, and spacecraft to protect the space environment by reducing the risk of collision to an acceptable level. Part of the requirement employs a two-fold approach to analyze the satellite design process with conjunction assessment and risk mitigation in mind, during the pre-launch process, led by the Conjunction Assessment Risk Analysis (CARA) Program for non-Human Space Flight (HSF) Missions. This presentation outlines CARA coordination with missions, informed by the NPR, that spans early mission development to operations. CARA is an Agency-level resource that provides support to all NASA non-HSF missions. CARA protects the orbital environment from collision between NASA non-HSF missions and other tracked on-orbit objects. During the pre-formulation and formulation phases, NASA missions undergo a series of conjunction assessment analyses captured in the Orbital Collision Avoidance Plan (OCAP) prior to transitioning to the implementation phase (typically at the Preliminary Design Review (PDR) or equivalent). The OCAP analyses consist of a thorough review of the spacecraft(s) orbit selection and placement, deployment, cataloguing performance, trackability, ephemeris generation, conjunction mitigation options, autonomous maneuvering, and risk assessment parameters which are performed by a dedicated CARA Analysis Team. The results of these analyses, CARA’s formal recommendations, and the mission’s methods for implementing them, are documented in the OCAP. The intent of engaging in this process so early in the mission design phase, is to ensure that conjunction assessment is considered from the outset, thus mitigating costly design changes and operational risks down the road. NASA missions are also required to coordinate their operational processes and conjunction mitigation procedures with CARA in a Conjunction Assessment Operations Implementation Agreement (CAOIA). The aim of this process is to document the conjunction assessment screening process, conjunction risk assessment parameters, conjunction mitigation steps, flight dynamics operations concepts and maneuvers, and the communication and coordination process between the mission’s project manager and CARA. The intent of the CAOIA document is for it to be completed iteratively, and as missions update these elements, corresponding changes are made in the CAOIA. With this process in place, the engagement and coordination between the missions and CARA from early in the design process into mission operations, helps to ensure that missions not only have a robust conjunction assessment concept of operations to reduce conjunction risk for space sustainability, but are also able to achieve their science goals and have a successful mission.

conjunction assessment↗

NASA NTRSDate not supplied

NASA CARA Prelaunch Analysis and Process

conjunction assessment↗

NASA NTRS2016

NASA UAS Integration into the NAS Project: Human Systems Integration

This presentation provides an overview of the work the Human Systems Integration (HSI) sub-project has done on detect and avoid (DAA) displays while working on the UAS (Unmanned Aircraft System) Integration into the NAS project. The most recent simulation on DAA interoperability with Traffic Collision Avoidance System (TCAS) is discussed in the most detail. The relationship of the work to the larger UAS community and next steps are also detailed.

unmanned aircraft systems↗

NASA NTRS1992

Robot path planning for space-truss assembly

Construction, repair, and maintenance of space-based structures will require extensive planning of operations in order to effectively carry out these tasks. The path planning algorithm described here is a general approach to generating paths that guarantee collision avoidance for a single chain nonredundant or redundant robot. The algorithm uses a graph search of feasible points in position space, followed by a local potential field method that guarantees collision avoidance among objects, structures, and the robot arm as well as conformance to joint limit constraints. This algorithm is novel in its computation of goal attractive potential fields in Cartesian space, and computation of obstacle repulsive fields in robot joint space. These effects are combined to generate robot motion. Computation is efficiently implemented through the computation of the robot arm Jacobian and not the full inverse arm kinematics. These planning algorithms have been implemented and evaluated using existing space-truss designs, and are being integrated into the RPI-CIRSSE Testbed environment.

Muenger, Rolf↗

NASA NTRS1988

Collision detection for planning collision-free motion of two robot arms

The authors focus on collision detection for planning collision-free motion of two robot arms in a common workspace. A collision-free motion is obtained by detecting collisions along the straight-line trajectories of the robots using a sphere model for the wrists and then replanning the paths and/or trajectories of one or both of the robots to avoid collisions. A novel approach to collision detection is presented and a discussion on collision avoidance is given.

Basta, Robert A.↗

NASA NTRSDate not supplied

Ground to Air Testing of a Fused Optical-Radar Aircraft Detection and Tracking System

Onboard detection and tracking capability are integral to the sensing component in collision avoidance systems needed to safely operate autonomous urban air taxis and small Unmanned Aircraft Systems. Ground-based validation of detection and tracking systems is an important milestone towards the end goal of real-time collision avoidance using onboard sensors and algorithms. In this work, we evaluate three Extended Kalman Filter(EKF)based fusion trackers with radar and vision detection inputs, and compare them with baseline trackers for each sensor type. Performance is assessed using field collected data of ground to air test flights with the sensors co-located on a stable platform with an instrumented multirotor acting as the intruder performing a waypoint pattern at a distance of1.1km to 0.3km to simulate a head-on collision geometry. Fusing an image-based morphological detector with a radar detector using an EKF covered 74% of the ground truth position updates logged by the flight controller on the multirotor within 50 meters after accounting for alignment offsets while covering 15% more ground truth updates relative to radar only. Removing timestamps when the intruder aircraft is occluded by trees and only considering timestamps where the radar has an update, the EKF image-based morphological detector combined with the radar detector covered 90% of the of the ground truth position updates within 50metersand 97% within 100 meters.

Chester V Dolph↗

NASA NTRSDate not supplied

Aerial Object Trajectory Classification by Training on Flight Controller Data and Testing on RADAR Generated Tracks

Onboard collision avoidance is needed to enable safe, autonomous flight operations for NASA projects such as Advanced Air Mobility (AAM), as well as many commercial applications. Real-time aerial object classification will improve onboard collision avoidance algorithm decision making and may reduce unnecessary activation of avoidance systems. This work trains an aircraft trajectory classifier using trajectories from flight controller logs and tests the classifier using RADAR collected trajectories during air to air experiments and ground to air experiments. In contrast to RADAR data, these flight controller logs are relatively abundant, which makes the possibility of substituting flight data for RADAR data an attractive, cost-effective option. The SVM model developed in this work achieved a 79.7% classification accuracy on the first second of radar trajectories of GA, multirotor sUAS, and fixed wing sUAS. Findings from this work indicate that it is feasible to classify sensor collected trajectories using a classifier trained on flight controller data.

Henry Holbrook↗

NASA NTRS2022

DataSet: Aerial Object Trajectory Classification by Training on Flight Controller Data and Testing on RADAR Generated

Onboard collision avoidance is needed to enable safe, autonomous flight operations for NASA projects such as Advanced Air Mobility (AAM), as well as many commercial applications. Real-time aerial object classification will improve onboard collision avoidance algorithm decision making and may reduce unnecessary activation of avoidance systems. This work trains an aircraft trajectory classifier using trajectories from flight controller logs and tests the classifier using RADAR collected trajectories during air to air experiments and ground to air experiments. In contrast to RADAR data, these flight controller logs are relatively abundant, which makes the possibility of substituting flight data for RADAR data an attractive, cost-effective option. The SVM model developed in this work achieved a 79.7% classification accuracy on the first second of radar trajectories of GA, multirotor sUAS, and fixed wing sUAS. Findings from this e that it is feasible to classify sensor collected trajectories using a classifier trained flight controller data.

Chester V Dolph↗

NASA NTRS2016

UAS in the NAS Flight Test Series 4 Overview

Flight Test Series 4 (FT4) provides the researchers with an opportunity to expand on the data collected during the first flight tests. Following Flight Test Series 3, additional scripted encounters with different aircraft performance and sensors will be conducted. FT4 is presently planned for Spring of 2016 to ensure collection of data to support the validation of the final RTCA Phase 1 DAA (Detect and Avoid) Minimum Operational Performance Standards (MOPS). There are three research objectives associated with this goal: Evaluate the performance of the DAA system against cooperative and non-cooperative aircraft encounters Evaluate UAS (Unmanned Aircraft Systems) pilot performance in response to DAA maneuver guidance and alerting with live intruder encounters Evaluate TCAS/DAA (Traffic Alert and Collision Avoidance System/Detect and Avoid) interoperability. This flight test series will focus on only the Scripted Encounters configuration, supporting the collection of data to validate the interoperability of DAA and collision avoidance algorithms.

flight test↗

NASA NTRS1995

Issues in Airborne Systems for Closely-Spaced Parallel Runway Operations

Efforts to increase airport capacity include studies of aircraft systems that would enable simultaneous approaches to closely spaced parallel runways in Instrument Meteorological Conditions (IMC). The time-critical nature of a parallel approach results in key design issues for current and future collision avoidance systems. These issues are being studied in two ways. First, a part-task flight simulator study has examined the procedural and display issues inherent in such a time-critical task. Second, a prototype collision avoidance logic capable of generating this maneuver guidance has been designed using a recently developed methodology.

Pritchett, A.↗

NASA NTRS2016

A Numerical Approach to Estimate the Ballistic Coefficient of Space Debris from TLE Orbital Data

Low Earth Orbit (LEO) is full of space debris, which consist of spent rocket stages, old satellites and fragments from explosions and collisions. As of 2009, more than 21,000 orbital debris larger than 10 cm are known to exist], and while it is hard to track anything smaller than that, the estimated population of particles between 1 and 10 cm in diameter is approximately 500,000, whereas small as 1 cm exceeds 100 million. These objects orbit Earth with huge kinetic energies speeds usually exceed 7 kms. The shape of their orbit varies from almost circular to highly elliptical and covers all LEO, a region in space between 160 and 2,000 km above sea level. Unfortunately, LEO is also the place where most of our active satellites are situated, as well as, International Space Station (ISS) and Hubble Space Telescope, whose orbits are around 400 and 550 km above sea level, respectively.This poses a real threat as debris can collide with satellites and deal substantial damage or even destroy them.Collisions between two or more debris create clouds of smaller debris, which are harder to track and increase overall object density and collision probability. At some point, the debris density couldthen reach a critical value, which would start a chain reaction and the number of space debris would grow exponentially. This phenomenon was first described by Kessler in 1978 and he concluded that it would lead to creation of debris belt, which would vastly complicate satellite operations in LEO. The debris density is already relatively high, as seen from several necessary debris avoidance maneuvers done by Shuttle, before it was discontinued, and ISS. But not all satellites have a propulsion system to avoid collision, hence different methods need to be applied. One of the proposed collision avoidance concepts is called LightForce and it suggests using photon pressure to induce small orbital corrections to deflect debris from colliding. This method is very efficient as seen from theoretical simulations, even few continuous mode 10 kW ground-based lasers, focused by 1.5 m telescopes with adaptive optics, were enough to prevent significant amount of the debris collisions. Simulations were done by propagating all space objects in LEO by 1 year into the future and checking whether the probability of collision was high. For those space objects different ground-based lasers were used to divert them, afterwards collision probabilities were reevaluated. However, the actual accuracy of the LightForce software, which has been developed at NASA AmesResearch Center, depends on the veracity of the input parameters, one of which is the objects ballistic coefficient. It is a measure of bodys ability to overcome air resistance, which has a significant impact on the debris in LEO, and thus it is responsible for the shape of the trajectory of the debris. Having the exact values of the ballistic coefficient would make significantly better collision predictions, unfortunately, we do not know what are the values for most of the objects.In this research, we were working with part of LightForce code, which estimates the ballistic coefficient from ephemerides. Previously used method gave highly inaccurate values, when compared to known objects, and it needed to be changed. The goal of this work was to try out a different method of estimating the ballistic coefficient and to check whether or not it gives noticeable improvements.

Collisions↗