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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.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS1980

Simulators for Safer Shipping

Each year one ship out of every five afloat collides with another vessel, rams a dock, or runs a ground. CAORF (Computer Aided Operations Research Facility), designed and built by Sperry Rand Corporation, incorporates technology developed in a wide variety of aerospace simulation and technical training programs. CAORF can be set up to duplicate the exact handling qualities of any vessel under various conditions of wind, tide and current. Currently a dozen different ships can be "plugged in." Bridge instrumentation is typical of modern shipboard equipment including radar, internal and external c.ommunications and new collision avoidance systems. From repetitive operation of simulated ships, MarAd is building a valuable data base for improving marine safety.

Source record↗
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

Integration of Automated Systems (IAS) Flight Test - Hazard Perception & Avoidance (HPA) Results

The Integration of Automated Systems (IAS) flight test series concluded in October 2023 in support of NASA's Advanced Air Mobility (AAM) project. These flights include crewed, test (i.e., ownship) and traffic (i.e., intruder) aircraft that flew with unique technologies onboard. The presentation includes overviews of the flight tet itself and the specific results that pertain to the Hazard Perception and Avoidance (HPA) technical areas. HPA tested the FAA's Airborne Collision Avoidance System X (ACAS X), a next-generation collision avoidance tool developed to support different aircraft types and operations. The rotorcraft variant, ACAS Xr, is designed to accommodate existing helicopter platforms and in-development, vertical takeoff and landing (VTOL) concepts, which are critical to the emerging AAM concept of operations. Two configurations of ACAS Xr were examined: Collision Avoidance System (CAS, similar to the Traffic Collision Avoidance System [TCAS] II) and Detect and Avoid (DAA, previously developed to provide added situational awareness for remote pilots). Additionally, this system was flown in cruise and low-speed flight regimes as well as within en-route and (emulated), structured (i.e., dense/urban), and terminal airspaces. Results include the types of alerts generated by ACAS Xr across the different configurations, the distances at which the alerts were generated, response times, manuever sizes, miss distances, and general comments from pilots. Key takeaways and next steps are also provided.

detect and avoid↗
NASA NTRS2005

Magnetically Attached Multifunction Maintenance Rover

A versatile mobile telerobot, denoted the magnetically attached multifunction maintenance rover (MAGMER), has been proposed for use in the inspection and maintenance of the surfaces of ships, tanks containing petrochemicals, and other large ferromagnetic structures. As its name suggests, this robot would utilize magnetic attraction to adhere to a structure. As it moved along the surface of the structure, the MAGMER would perform tasks that could include close-up visual inspection by use of video cameras, various sensors, and/or removal of paint by water-jet blasting, laser heating, or induction heating. The water-jet nozzles would be mounted coaxially within compressed-air-powered venturi nozzles that would collect the paint debris dislodged by the jets. The MAGMER would be deployed, powered, and controlled from a truck, to which it would be connected by hoses for water, compressed air, and collection of debris and by cables for electric power and communication (see Figure 1). The operation of the MAGMER on a typical large structure would necessitate the use of long cables and hoses, which can be heavy. To reduce the load of the hoses and cables on the MAGMER and thereby ensure its ability to adhere to vertical and overhanging surfaces, the hoses and cables would be paid out through telescopic booms that would be parts of a MAGMER support system. The MAGMER would move by use of four motorized, steerable wheels, each of which would be mounted in an assembly that would include permanent magnets and four pole pieces (see Figure 2). The wheels would protrude from between the pole pieces by only about 3 mm, so that the gap between the pole pieces and the ferromagnetic surface would be just large enough to permit motion along the surface but not so large as to reduce the magnetic attraction excessively. In addition to the wheel assemblies, the MAGMER would include magnetic adherence enhancement fixtures, which would comprise arrays of permanent magnets and pole pieces that could be adjusted to maximize or minimize the overall attractive magnetic force. Even with a paint thickness of 2 mm, a preliminary design provides a safety factor of 5 in the magnetic force in the upside- down, water-jets-operating condition, in which the total load (including the weight of the MAGMER and cables and the water-jet force) would be about 260 lb (the weight of 118 kg). Optionally, the MAGMER could carry magnetic shielding and/or could be equipped with a demagnetizing module to remove residual magnetism from the structure. The MAGMER would carry four charge-coupled-device cameras for visual inspection, monitoring of operation, navigation, and avoidance of collisions with obstacles. The control system of the MAGMER would include navigation and collision-avoidance subsystems that would utilize surface features as landmarks, in addition to direct images of obstacles.

Bar-Cohen, Yoseph↗
NASA NTRSDate not supplied

Integration of Automated Systems (IAS) Flight Test Overview

The Integration of Automated Systems (IAS) Project is conducting a series of 2023 flight tests supporting NASA's Advanced Air Mobility (AAM) and National Campaign efforts. These flights include crewed, test (i.e., ownship) and traffic (i.e., intruder) aircraft that will fly with unique technologies onboard. The presentation will include overviews of the Hazard Perception and Avoidance (HPA) and Flight Path Management (FPM) technical areas but will focus primarily on HPA. HPA will test the FAA's Airborne Collision Avoidance System X (ACAS X), a next-generation collision avoidance tool developed to support different aircraft types and operations. The rotorcraft variant, ACAS Xr, is designed to accommodate existing helicopter platforms and in-development, vertical takeoff and landing (VTOL) concepts, which are critical to the emerging AAM concept of operations. Two configurations of ACAS Xr will be examined: Collision Avoidance System (CAS, similar to the Traffic Collision Avoidance System [TCAS] II) and Detect and Avoid (DAA, previously developed to provide added situational awareness for uncrewed aircraft). Additionally, this system will be explored during cruise and low-speed flight as well as flights within en-route, structured (i.e., dense/urban), and terminal airspaces. Scripted flight conflicts will be conducted, and these conflicts will be mitigated through maneuvers that are manual (i.e., performed by the pilots) or automated (i.e., achieved by the cooperation of the program middleware and onboard ownship systems). Objective data will be collected involving system and pilot performance as well as pilot decisions; subjective data will include pilot opinions of ACAS Xr's alerting and guidance as well as the automated maneuvers.

detect and avoid↗
NASA NTRS2017

A Fuzzy Logic Approach for Separation Assurance and Collision Avoidance for Unmanned Aerial Systems

In the coming years, operations in low altitude airspace will vastly increase as the capabilities and applications of small Unmanned Aerial Systems (sUAS) continue to multiply. Therefore, solutions to managing sUAS in highly congested airspace must be explored. In this study, a Fuzzy Logic based approach was used to help mitigate the risk of collisions between aircraft using separation assurance and collision avoidance techniques. The system was evaluated for its effectiveness to mitigate the risk of mid-air collisions between aircraft. This system utilizes only current state information and can resolve potential conflicts without knowledge of intruder intent. The avoidance logic was verified using formal methods and shown to select the correct action in all instances. Additionally, the Fuzzy Logic Controllers were shown to always turn the vehicles in the correct direction. Numerical testing demonstrated that the avoidance system was able to prevent a mid-air collision between two sUAS in all tested cases. Simulations were also performed in a three-dimensional environment with a heterogenous fleet of sUAS performing a variety of realistic missions. Simulations showed that the system was 99.98 effective at preventing mid-air collisions when separation assurance was disabled (unmitigated case) and 100 effective when enabled (mitigated case).

Verification & Validation of a Fuzzy Logic Control↗
NASA NTRS2006

Collision Avoidance Functional Requirements for Step 1. Revision 6

This Functional Requirements Document (FRD) describes the flow of requirements from the high level operational objectives down to the functional requirements specific to cooperative collision avoidance for high altitude, long endurance unmanned aircraft systems. These are further decomposed into performance and safety guidelines that are backed up by analysis or references to various documents or research findings. The FRD should be considered when establishing future policies, procedures, and standards pertaining to cooperative collision avoidance.

Source record↗
DOE OSTI2024

The ReSWARM microgravity flight experiments: Planning, control, and model estimation for on‐orbit close proximity operations

Abstract On‐orbit close proximity operations involve robotic spacecraft maneuvering and making decisions for a growing number of mission scenarios demanding autonomy, including on‐orbit assembly, repair, and astronaut assistance. Of these scenarios, on‐orbit assembly is an enabling technology that will allow large space structures to be built in situ, using smaller building block modules. However, like many of these scenarios, robotic on‐orbit assembly involves several technical hurdles, such as changing system models. For instance, grappled modules moved by a free‐flying “assembler” robot can cause significant changes in the combined system inertia, which have cascading impacts on motion planning and control portions of the autonomy stack. Further, on‐orbit assembly and other scenarios require collision‐avoiding motion planning, particularly when operating in a “construction site” scenario of multiple assembler robots and structures. Multiple key technologies that address these complicating factors for autonomous microgravity close proximity operations are detailed in this work, in particular: (1) application of global long‐horizon planning, accomplished using offline and online sampling‐based planner options that consider the system dynamics; (2) adaptation of the recently proposed RATTLE information‐aware planning framework for on‐orbit reconfiguration model learning; and (3) connection with robust control tools to provide low‐level control robustness using current system knowledge. These approaches were demonstrated for an autonomous on‐orbit assembly use case by the RElative Satellite sWarming and Robotic Maneuvering (ReSWARM) experiments using NASA's Astrobee robots on the International Space Station. Results of the ReSWARM experiments are provided along with significant operational and implementation detail discussing the practicalities of hardware implementation and unique aspects of working with the Astrobee free‐flyer robots in microgravity. ReSWARM provides a base set of planning and control tools for robotic close proximity operations, demonstrates them in microgravity, and outlines some of the important hardware aspects that future autonomous free‐flyers will need to consider.

Robotics↗
NASA NTRS2011

Engineering and Technology Challenges for Active Debris Removal

After more than fifty years of space activities, the near-Earth environment is polluted with man-made orbital debris. The collision between Cosmos 2251 and the operational Iridium 33 in 2009 signaled a potential collision cascade effect, also known as the "Kessler Syndrome", in the environment. Various modelling studies have suggested that the commonly-adopted mitigation measures will not be sufficient to stabilize the future debris population. Active debris removal must be considered to remediate the environment. This paper summarizes the key issues associated with debris removal and describes the technology and engineering challenges to move forward. Fifty-four years after the launch of Sputnik 1, satellites have become an integral part of human society. Unfortunately, the ongoing space activities have left behind an undesirable byproduct orbital debris. This environment problem is threatening the current and future space activities. On average, two Shuttle window panels are replaced after every mission due to damage by micrometeoroid or orbital debris impacts. More than 100 collision avoidance maneuvers were conducted by satellite operators in 2010 to reduce the impact risks of their satellites with respect to objects in the U.S. Space Surveillance Network (SSN) catalog. Of the four known accident collisions between objects in the SSN catalog, the last one, collision between Cosmos 2251 and the operational Iridium 33 in 2009, was the most significant. It was the first ever accidental catastrophic destruction of an operational satellite by another satellite. It also signaled the potential collision cascade effect in the environment, commonly known as the "Kessler Syndrome," predicted by Kessler and Cour-Palais in 1978 [1]. Figure 1 shows the historical increase of objects in the SSN catalog. The majority of the catalog objects are 10 cm and larger. As of April 2011, the total objects tracked by the SSN sensors were more than 22,000. However, approximately 6000 of them had yet to be fully processed and entered into the catalog. This population had been dominated by fragmentation debris throughout history. Before the anti-satellite test (ASAT) conducted by China in 2007, the fragmentation debris were almost all explosion fragments. After the ASAT test and the collision between Iridium 33 and Cosmos 2251, the ratio of collision fragments to explosion fragments was about one-to-one. It is expected that accidental collision fragments will further dominate the environment in the future.

Liou, Jer-Chyi↗
NASA NTRS1989

Magneto-inductive skin sensor for robot collision avoidance: A new development

Safety is a primary concern for robots operating in space. The tri-mode sensor addresses that concern by employing a collision avoidance/management skin around the robot arms. This rf-based skin sensor is at present a dual mode (proximity and tactile). The third mode, pyroelectric, will complement the other two. The proximity mode permits the robot to sense an intruding object, to range the object, and to detect the edges of the object. The tactile mode permits the robot to sense when it has contacted an object, where on the arm it has made contact, and provides a three-dimensional image of the shape of the contact impression. The pyroelectric mode will be added to permit the robot arm to detect the proximity of a hot object and to add sensing redundancy to the two other modes. The rf-modes of the sensing skin are presented. These modes employ a highly efficient magnetic material (amorphous metal) in a sensing technique. This results in a flexible sensor array which uses a primarily inductive configuration to permit both capacitive and magnetoinductive sensing of object; thus optimizing performance in both proximity and tactile modes with the same sensing skin. The fundamental operating principles, design particulars, and theoretical models are provided to aid in the description and understanding of this sensor. Test results are also given.

Chauhan, D. S.↗
NASA NTRS1980

Collision avoidance in space

Collisions in earth orbital space between operational payloads and various forms of space debris (nonoperational payloads, nonfunctional mission-related objects and fragments resulting from collisions and explosions) are discussed and possible means of avoiding them are considered. From 10,000 to 15,000 objects are estimated to be in earth orbital space, most of which represent spacecraft fragments and debris too small to be detected and tracked by earth-based sensors, and it is considered likely that some of them will be or have already been involved in direct collisions with the ever increasing number of operational satellites and space stations. Means of protecting proposed large space structures and smaller spacecraft from significant damage by larger space objects, particularly in the 400-4000 km altitude range where most debris occurs, include structural redundancy and the double shielding of sensitive components. Other means of collision avoidance are the collection or relocation of satellites, rocket bodies and other objects by the Space Shuttle, the prevention of explosions and the disposal of spent rocket parts by reentry. Finally, a management structure would be required to administer guidelines for the prevention and elimination of space debris.

Kessler, D. J.↗
NASA NTRS2006

Sense and Avoid Safety Analysis for Remotely Operated Unmanned Aircraft in the National Airspace System. Version 5

This document describes a method to demonstrate that a UAS, operating in the NAS, can avoid collisions with an equivalent level of safety compared to a manned aircraft. The method is based on the calculation of a collision probability for a UAS , the calculation of a collision probability for a base line manned aircraft, and the calculation of a risk ratio given by: Risk Ratio = P(collision_UAS)/P(collision_manned). A UAS will achieve an equivalent level of safety for collision risk if the Risk Ratio is less than or equal to one. Calculation of the probability of collision for UAS and manned aircraft is accomplished through event/fault trees.

Carreno, Victor↗
NASA NTRS1991

Collision Avoidance System

Ames Research Center teamed with the Federal Aviation Administration (FAA) to study human performance factors associated with the use of the Traffic Alert and Collision Avoidance system (TCAS II) in an operational environment. TCAS is designed to alert pilots of the presence of other aircraft in their vicinity, to identify and track those who could be a threat, and to recommend action to avoid a collision. Ames conducted three laboratory experiments. The first showed that pilots were able to use the TCAS II correctly in the allowable time. The second tested pilots' response to changes in the avoidance advisories, and the third examined pilots' reactions to alternative displays. After a 1989 congressional mandate, the FAA ruled that TCAS would be required on all passenger carrying aircraft (to be phased in completely by 1995).

Source record↗
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

A Collision Avoidance Strategy for a Potential Natural Satellite around the Asteroid Bennu for the OSIRIS-REx Mission

The cadence of proximity operations for the OSIRIS-REx mission may have an extra induced challenge given the potential of the detection of a natural satellite orbiting the asteroid Bennu. Current ground radar observations for object detection orbiting Bennu show no found objects within bounds of specific size and rotation rates. If a natural satellite is detected during approach, a different proximity operation cadence will need to be implemented as well as a collision avoidance strategy for mission success. A collision avoidance strategy will be analyzed using the Wald Sequential Probability Ratio Test.

satellite↗
NASA NTRS2016

A Collision Avoidance Strategy for a Potential Natural Satellite Around the Asteroid Bennu for the OSIRIS-REx Mission

The cadence of proximity operations for the OSIRIS-REx mission may have an extra induced challenge given the potential of the detection of a natural satellite orbiting the asteroid Bennu. Current ground radar observations for object detection orbiting Bennu show no found objects within bounds of specific size and rotation rates. If a natural satellite is detected during approach, a different proximity operation cadence will need to be implemented as well as a collision avoidance strategy for mission success. A collision avoidance strategy will be analyzed using the Wald Sequential Probability Ratio Test.

bennu↗