2022 IEEE Aerospace Conference (AERO)最新文献

{"title":"MMX Rover Locomotion Subsystem - Development and Testing towards the Flight Model","authors":"Stefan Barthelmes, T. Bahls, Ralph Bayer, W. Bertleff, Markus Bihler, Fabian Buse, Maxime Chalon, F. Hacker, Roman Holderried, Viktor Langofer, R. Lichtenheldt, Sascha Moser, K. Sasaki, Hans-Juergen Sedlmayr, Juliane Skibbe, L. Stubbig, B. Vodermayer","doi":"10.1109/AERO53065.2022.9843723","DOIUrl":"https://doi.org/10.1109/AERO53065.2022.9843723","url":null,"abstract":"Wheeled rovers have been successfully used as mobile landers on Mars and Moon and more such missions are in the planning. For the Martian Moon eXploration (MMX) mission of the Japan Aerospace Exploration Agency (JAXA), such a wheeled rover will be used on the Marsian Moon Phobos. This is the first rover that will be used under such low gravity, called milli-g, which imposes many challenges to the design of the locomotion subsystem (LSS). The LSS is used for unfolding, standing up, driving, aligning and lowering the rover on Phobos. It is a entirely new developed highly-integrated mechatronic system that is specifically designed for Phobos. Since the Phase A concept of the LSS, which was presented two years ago [1], a lot of testing, optimization and design improvements have been done. Following the tight mission schedule, the LSS qualification and flight models (QM and FM) assembly has started in Summer 2021. In this work, the final FM design is presented together with selected test and optimization results that led to the final state. More specifically, advances in the mechanics, electronics, thermal, sensor, firmware and software design are presented. The LSS QM and FM will undergo a comprehensive qualification and acceptance testing campaign, respectively, in the first half of 2022 before the FM will be integrated into the rover.","PeriodicalId":219988,"journal":{"name":"2022 IEEE Aerospace Conference (AERO)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133829688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-timescale Sensor Fusion and Control","authors":"Sarah Kitchen, Josef Paki","doi":"10.1109/AERO53065.2022.9843274","DOIUrl":"https://doi.org/10.1109/AERO53065.2022.9843274","url":null,"abstract":"Networked autonomous systems are a rapidly expanding area of research and development across academic, commercial, and military endeavors. Significant challenges exist in extending traditional detection and estimation methods to such distributed systems of sensors when we relax assumptions on full communications connectivity and global observability of the network. Global observability can be interpreted as a persistent coverage of all degrees of freedom associated with a object's feature vector - this can be satisfied by a combination of physical diversity of homogeneous sensors and/or diversity across sensing domains for heterogeneous sensors, and the role of resource allocation across the network is to determine configurations, and reconfigurations, of platforms that achieve said diversity. In a general heterogeneous sensor network, persistent global observability across the entire area of operations requires control decisions at a much longer timescale than the feature estimate updates that provide locally full rank observability. In this paper, we temporally separate the long-timescale resource allocation control process from the parameter estimation through the use of a decentralized Partially Observable Markov Decision Process (POMDP) control model that employs consensus estimates on object features as observations and benchmark this multi-timescale approach against centralized Linear Quadratic Gaussian (LQG) control for a fully connected network with simultaneous estimation and control updates.","PeriodicalId":219988,"journal":{"name":"2022 IEEE Aerospace Conference (AERO)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115440941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Flight Software Dictionary Development for the Mars2020 Rover","authors":"M. Muszynski, E. Fosse, Andrew Plave, G. Pyrzak","doi":"10.1109/AERO53065.2022.9843621","DOIUrl":"https://doi.org/10.1109/AERO53065.2022.9843621","url":null,"abstract":"The Mars2020 project, developed and operated by the Jet Propulsion Laboratory (JPL), successfully landed the Perseverance rover and its flying companion Ingenuity on the surface of Mars on February 18th 2021. Perseverance com-bines heritage and cutting-edge flight software and hardware to accomplish crucial mission requirements related to Martian surface sampling. The design, development, and operation of NASA's large strate-gic science missions require the ability to communicate space-craft capabilities to hundreds of engineers across multiple dis-ciplines. The interactions between flight and ground software development, Verification and Validation (V&V), Assembly, Test, and Launch Operations (ATLO), and management each demand quick understanding of unique slices of information for each discipline. This information includes the current capabil-ities of the flight system as well as future capabilities and their status as they are developed and tested. Despite the fundamental and critical nature of this information, the flight software dictionaries used to track it are a stumbling block for many projects. These dictionaries provide the cor-nerstone for the interpretation of data sent from the spacecraft, allowing for quick comprehension by engineers on the ground. During both spacecraft development and operations, flight soft-ware dictionary management includes significant challenges due to the large number of interfacing systems and the subtle yet distinct needs of each. The engineering of flight software dictionaries for Mars2020 had numerous challenges, most-notably: parallel dictionary development to support simultaneous separate flight software build campaigns for each mission phase (cruise and surface), managing requests for operations-enabling information without perturbing the heritage interface with the rover, and the intro-duction of new tools by the dictionary stakeholders that forced the dictionary team to innovate and redesign the heritage tool chain. These challenges generated guiding principles for the dictionary development effort: emphasize coding best practices and unit testing in the dictionary code development tool chain, use institutionally provided COTS (commercial-off-the-shelf) tools whenever possible, and maintain the heritage flight-ground interface all while advancing operations-enabling information via a loosely coupled interface. Throughout development and operations, the Mars2020 dictionary toolchain included IBM DOORS Next Generation, GitHub, Microsoft Excel, Docker, Jenkins, and a significant custom-built Python codebase. Significant interfaces included JPL's command and control software, heritage flight software team tools and processes, and the many cloud-based ground tools developed for the mission. This paper will discuss the requirements for the Mars2020 dictionary development, the development team's response to those requirements, lessons learned throughout the process, steps taken towards automated deliveries","PeriodicalId":219988,"journal":{"name":"2022 IEEE Aerospace Conference (AERO)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115570750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Space Applications of a Trusted AI Framework: Experiences and Lessons Learned","authors":"L. Mandrake, G. Doran, Ashish Goel, H. Ono, R. Amini, M. Feather, L. Fesq, Philip C. Slingerland, Lauren Perry, Benjamen Bycroft, James Kaufman","doi":"10.1109/AERO53065.2022.9843322","DOIUrl":"https://doi.org/10.1109/AERO53065.2022.9843322","url":null,"abstract":"Artificial intelligence (AI), which encompasses machine learning (ML), has become a critical technology due to its well-established success in a wide array of applications. However, the proper application of AI remains a central topic of discussion in many safety-critical fields. This has limited its success in autonomous systems due to the difficulty of ensuring AI algorithms will perform as desired and that users will understand and trust how they operate. In response, there is growing demand for trustability in AI to address both the expectations and concerns regarding its use. The Aerospace Corporation (Aerospace) developed a Framework for Trusted AI (henceforth referred to as the framework) to encourage best practices for the implementation, assessment, and control of AI-based applications. It is generally applicable, being based on terms and definitions that cut across AI domains, and thus is a starting point for practitioners to tailor to their particular application. To help demonstrate how the framework can be tailored into mission assurance guidance for the space domain, Aerospace sought the involvement of the Jet Propulsion Laboratory (JPL) to engage with actual examples of AI-based space autonomy. We report here on the framework's application to two JPL projects. The first, Machine learning-based Analytics for Automated Rover Systems (MAARS), is a suite of algorithms that is intended to run onboard a rover to enhance its safety and productivity. The second, the Ocean Worlds Life Surveyor (OWLS), is comprised of an instrument suite and onboard software that is designed to search for life on an icy moon using microscopy and mass spectrometry while judiciously summarizing and prioritizing science data for downlink. Both MAARS and OWLS are intended to have minimal manual control while relying on complex autonomy software to operate within the unforgiving environment of deep space. Therefore, trusted AI for these systems is required for successful adoption of the autonomy software. To capture the needs for trust, interviews with a variety of JPL personnel responsible for developing autonomy solutions were conducted and are summarized here. Additionally, the application of the framework is presented as a means to lower the barrier for AI deployment. The intent of this document is to encourage researchers, engineers, and program managers to adopt new strategies when considering whether to leverage AI in autonomous systems.","PeriodicalId":219988,"journal":{"name":"2022 IEEE Aerospace Conference (AERO)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115668396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust and Gain-Scheduled Flight Control of Fixed-Wing UAVs in Wind and Icing Conditions","authors":"Ruben Kleiven, K. Gryte, T. Johansen","doi":"10.1109/AERO53065.2022.9843837","DOIUrl":"https://doi.org/10.1109/AERO53065.2022.9843837","url":null,"abstract":"Simulations of a fixed-wing unmanned aerial vehicle with varying degrees of icing, both symmetric and asymmetric, and both ice accretion and ice shedding, show the applicability of robust $H_{infty}$ inner-loop control for both the lateral and the longitudinal axes to extend the flight envelope. A gain-scheduled extension of the controller compares favourably in performance, at the cost of complexity and needing to know current the icing level. The results also show that higher airspeeds reduces the disturbance related to instantaneous removal of ice. A simple analysis of the open-loop dynamics of the aerodynamic model show that only the spiral-mode eigenvalue moves into the right hand plane with increased icing levels.","PeriodicalId":219988,"journal":{"name":"2022 IEEE Aerospace Conference (AERO)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124525022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Mathematical Model for the Analysis of Jet Engine Fuel Consumption during Aircraft Take-off","authors":"Francisco Velásquez-SanMartín, Xabier Insausti, M. Zárraga-Rodríguez, Jesús Gutiérrez-Gutiérrez","doi":"10.1109/AERO53065.2022.9843276","DOIUrl":"https://doi.org/10.1109/AERO53065.2022.9843276","url":null,"abstract":"This paper proposes a mathematical model for the performance of a jet engine aircraft during take-off. The mathematical model provides a closed-form formula of the aircraft's velocity as a function of time, which further enables obtaining a closed-form formula of the aircraft's fuel flow rate and hence, of the fuel consumption during take-off. Furthermore, the model is tested for a specific jet engine aircraft under given aerodynamic take-off conditions.","PeriodicalId":219988,"journal":{"name":"2022 IEEE Aerospace Conference (AERO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114826847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Formal Property Verification of a Remote Memory Access Protocol IP-Core","authors":"K. Borchers, T. Firchau","doi":"10.1109/AERO53065.2022.9843263","DOIUrl":"https://doi.org/10.1109/AERO53065.2022.9843263","url":null,"abstract":"Formal Property Verification (FPV) of Register-Transfer Level (RTL) designs have been adopted in many industry domains. It provides the ability to evaluate full state spaces rather than selecting a subset as it is done for functional simulation. This, in turn, drastically decreases the appearance of bug escapes. This paper demonstrates how FPV is applied to a packet-based Field Programmable Gate Array (FPGA) design. It shows how additional code alongside property definitions can help to capture and compare packet data fields. Additionally, encountered FPV specific problems and possible solutions are discussed.","PeriodicalId":219988,"journal":{"name":"2022 IEEE Aerospace Conference (AERO)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114648036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Operational Tools and Data Management for OSIRIS-REx Optical Navigation","authors":"L. McCarthy, J. Pelgrift, Erik J. Lessac-Chennen, E. Sahr, B. Carcich, C. Adam, D. Nelson, R. Gaskell, D. Lauretta","doi":"10.1109/AERO53065.2022.9843730","DOIUrl":"https://doi.org/10.1109/AERO53065.2022.9843730","url":null,"abstract":"The Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer (OSIRIS-REx) spacecraft was launched in September 2016 and arrived at its target, near-Earth asteroid (101955) Bennu in late 2018. After executing nearly two years of multi-phase proximity operations navigation and mapping campaigns, on October 20, 2020 OSIRIS-REx successfully performed the Touch-And-Go (TAG) maneuver to become the first American mission to collect a sample from an asteroid. As Bennu is one of the smallest objects ever to be visited by a planetary spacecraft, the mission presented many navigational challenges, and optical navigation (OpNav) techniques were essential to the successful execution of the mission. The specific challenges of OSIRIS-REx required nimble OpNav planning, robust data management, and quick, automated analyses and data-product delivery capabilities. In addition to the two primary image processing tools, centroid-based and landmark-based OpNav, a host of support and planning tools and procedures were developed. The multi-year operations timeline, fast maneuver cadence, and multi-phase nature of proximity operations for OSIRIS-REx motivated a streamlined and reactive image planning process. A suite of tools was developed to ensure that the mission navigation requirements were continually satisfied. On approach, the Op-N av Opportunity Analyzer (OpOpp) was used to deconflict the imaging schedule with interference from bright background stars. In-flight instrument calibration was performed using an in-house distortion calibration toolset. An Exposure Time Calculator was used to determine optimal exposure times and verify that the images would produce sufficient OpNav image data. Additionally, Fly-Point-Shoot (FPS) software was used to analyze and mitigate the effect of trajectory and pointing uncertainties on image planning and coverage. This paper describes in further detail the operational challenges of the OSIRIS-REx OpNav subsystem, as well as the tools, procedures, and strategies developed to ensure the satisfaction of navigation requirements.","PeriodicalId":219988,"journal":{"name":"2022 IEEE Aerospace Conference (AERO)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114914380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving Automatic Target Recognition (ATR) Performance with Electro Optics (EO) and Infrared (IR) Sensor Fusion","authors":"Hai-Wen Chen, R. Kapadia","doi":"10.1109/AERO53065.2022.9843797","DOIUrl":"https://doi.org/10.1109/AERO53065.2022.9843797","url":null,"abstract":"In previous years, we developed Automatic Target Recognition (ATR) algorithms by combining layers of the open-source You Only Look Once (YOLOv2) detection model with customized Convolutional Neural Network (CNN) feature extraction layers to recognize targets in Infrared (IR) images. Our work showed that ATR for IR performed significantly better during night-time than during daytime. In this study, we demonstrate that fusing EO and IR images using pixel-based and decision-based sensor fusion can improve daytime ATR performance significantly. Traditional Automatic Target Detection (ATD) metrics do not account for misclassification while traditional target classification metrics do not count missed detections. We have developed a novel approach for evaluating ATR performance that bridges traditional target detection and target classification metrics based on an extended confusion matrix (ECM) which allows us to accurately characterize Probability of Detection (Pd), Probability of False Alarm (Pfa), and the tradeoffs between the two for ATR applications. After running the ATR detector at multiple Confidence Score thresholds, we can obtain and describe the detection performance at different Pfa levels using the Receiver Operating Characteristic (ROC) curves to show the comprehensive relationship between Pd vs. Pfa. Combining EO and IR fusion approaches with the ECM, we demonstrate improvements in Pd of 11% with pixel-based fusion and 13-17% with decision-based fusion, respectively.","PeriodicalId":219988,"journal":{"name":"2022 IEEE Aerospace Conference (AERO)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115036443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Venus High Temperature Motor and Rotary Percussive Drill for Pneumatic Acquisition of Samples","authors":"K. Zacny, Jeffery L. Hall, J. Bailey, B. Yen, F. Rehnmark, E. Cloninger, Jerry Moreland, K. Sherrill, J. Melko, L. Nakley, J. Tims, Raymond Zheng","doi":"10.1109/AERO53065.2022.9843732","DOIUrl":"https://doi.org/10.1109/AERO53065.2022.9843732","url":null,"abstract":"Venus surface missions have been limited to the Venera and Vega programs. Venera 13 and 14, as well as Vega 1 and 2, used a rotary drill for penetrating 3 cm into Venus surface and pneumatic transfer of samples for analysis by a x-ray fluorescence spectrometer (XRF). Following up on these successful missions, there is a need for landing in different locations on Venus surface and analyzing samples from deeper depths. To advance sampling technology in extreme conditions, Honeybee Robotics, in partnership with NASA Jet Propulsion Laboratory, has been developing high temperature motors and drills. In addition, preliminary material testing for long duration missions has been conducted in partnership with NASA Glenn Research Center. The Venus sampling technology work started almost two decades ago and has recently culminated with an end-to-end demonstration of drilling and sample delivery under Venus-like conditions of high Temperature (465 °C) high pressure (92 bar) and CO2 atmosphere. This paper provides details related to the development of high temperature motors and a rotary-percussive drill for the purpose of acquiring samples from up to 5 cm depth. This technology would be directly infused into a New Frontiers class mission Venus In-Situ Explorer (VISE).","PeriodicalId":219988,"journal":{"name":"2022 IEEE Aerospace Conference (AERO)","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116904523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}