ABSTRACT. At start of the 21st century, we are in an exciting time in planetary science with an increased cadence of missions, an increasing number of nations and entities engaged in exploration, and near-term human missions to the Moon and beyond. These are occurring within a broader set of changes in aerospace: lowered barriers to accessing deep space, more space-qualified off-the-shelf commercial hardware and software, and a vibrant start-up culture. From the perspective of a planetary scientist working missions to the Moon, Mars, Venus, and the outer solar system as well as Earth observation, I will highlight some of the challenges and opportunities at different destinations and mission classes. On-board science data processing with real-time feedback to mission operations, autonomous fault tolerance/handling, lower cost avionics as mission enabling technology, and approaches for integration of real-time science instrument data into astronaut lunar traverses are some key opportunities in planetary exploration.

ABSTRACT. Introduction

ABSTRACT. The Lightweight Surface Manipulation System (LSMS) is a family of robotically operated cranes designed to handle a wide range of payload off-loading, and general coarse manipulations on a planetary surface, such as the Moon or Mars. In this talk, some of the sources of uncertainty that arise in the manipulation of this family of cable actuated cranes will be discussed, as well as some of the mechanisms leveraged to mitigate the uncertainty.

ABSTRACT. This presentation will discuss a subset of the sources of uncertainty that impact autonomous in-space servicing, assembly, and manufacturing missions. These include robotic manipulator modeling uncertainties in both kinematics and dynamics, perception error associated machine learning models for pose estimation, and sensor noise. Mitigation strategies will be discussed including the incorporation of capture envelopes in the design of robotic tools and selection of robot goal poses to minimize end-effector sensitivity in manipulators with redundant degrees of freedom.

ABSTRACT. Discussion

ABSTRACT. Software designed to run in space has traditionally been bespoke, one-off, low-compute-overhead applications that perform a specific task on specific hardware. Scalability and composability have not been design requirements for spaceflight software because there’s been no need. However, as Earth orbit is commoditized and deep space infrastructure is deployed for the next wave of interplanetary exploration, the need to scale, compose, and upgrade services over the duration of a mission goes from being a fault recovery mechanism to being a necessity.

ABSTRACT. NASA and our industrial and international partners are planning to establish a sustained presence on the lunar surface as part of the Artemis campaign. Robotics and autonomous systems are key technology areas which will enable remote operations on the lunar surface in applications such as logistics, maintenance, science utilization, construction and outfitting. The lunar surface will serve as a proving ground for robotics with increasing levels of autonomy, flexibility, and resilience that will enable the human exploration of Mars. I will be giving a brief overview of the challenges associated with robotic remote operations in architectures designed around human explorers and the plans that NASA is developing to bridge the gap between terrestrial innovation and the demands of space applications.

ABSTRACT. Deployment of robots will revolutionize space exploration in the coming years, both for manned and unmanned missions; however, the success of these robots is linked as much to advances in sensors, manipulators, and AI algorithms as it is to the robustness of the underlying computational architectures that support the software & hardware. Most space missions require the use of specialized – computationally limited – radiation tolerant hardware, which in turn depends upon specialized flight software (FSW). This is as true for robots as it is for the ISS or Gateway. Because of this specialization, FSW has traditionally been developed via “clone-and-own” processes, where software from a previous mission is copied and adapted. An alternative approach, increasingly accepted by both the robotics and space-flight communities, suggests that developing and sharing component-based, reusable software will facilitate the number, scope, and innovation of space missions. This will require that complex robot and flight software is developed through the use of a common framework, such as ROS2, of shared libraries and tools. As such, TRACLabs and Johns Hopkins APL have recently been developing a BRASH (Bridge for ROS2 Application to Space Hardware) toolkit for software components to enable ROS2 to interoperate with existing flight software tools. As there is no “one size fits all” mission architecture, we instead have focused on developing a number of components that mission designers can draw upon to meet their specific needs. While still a work in progress, our toolkit includes ROS2-to-FSW bridge utilities for message translation and conversion between ROS2 and commons FSW frameworks such as NASA core Flight Software (cFS) and JPL’s F’; plugins for integration with both ground and flight systems; utilities for time synchronization, parameter and event management; and integration with TRACLabs' PRIDE electronic procedure application software. You can find more about our tools, which are being made available to the community, at https://traclabs-brash.bitbucket.io/.

ABSTRACT. Ransomware is a prevailing concern across sectors, wreaking havoc on operations and causing cascading failures - often cyber-physical in nature. The space sector has not been immune to ransomware attacks; however, there has yet to be a publicly disclosed ransomware built for a space vehicle itself. A ransomware attack against a space vehicle would need to be carefully crafted to mitigate the risk of destroying the underlying functionality of the spacecraft, while still achieving its purpose - denying access until a ransom is paid. Through static code analysis, this paper proposes an approach for deploying a ransom attack against a space vehicle that engages NASA's core Flight System (cFS).

ABSTRACT. Space flight software is no longer a closely guarded secret for space vehicle developers, owners and operators - it is open-sourced and available as a commercial-off-the-shelf module. Despite its wide availability, limited security research has been conducted on flight software in an unclassified environment. This paper proposes a research agenda that outlines critical challenges for space flight software and proposes a series of research and development efforts that could ultimately aid in developing inherently secure space vehicles.

ABSTRACT. Two issues to be discussed in the available 30-minute time slot: 1) mission critical networks for space flight systems, and 2) deep nano-meter, radiation hardened embedded FPGA technology advancements and advantages.

ABSTRACT. As aerospace and defense firms are working towards developing future air and space platforms, decisions involving which hardware and software elements to use in their design needs to be made. One significant design choice is the underlying instruction set architecture (ISA) that defines the interface between the software and hardware for a processor. A relatively new ISA called RISC-V has emerged as an open source alternative to commercial ISAs. Verifiable security, frozen base specification for long-term stability, designed for extensibility and no license fee for modifications makes RISC-V particularly well suited for aerospace and defense applications. It is important for the architect to evaluate not only the RISC-V core but the interaction of the core with other subsystems, data accesses, and interrupts, in the scope of target application at early design stages inorder to minimize design bugs, reduce cost and optimize design. In this work, we developed the system models of not only the RISC-V core but also the entire SoC where the RISC-V cores were plugged in. Using the system model, we are able to run target applications/benchmarks on RISC-V core and evaluate the performance and power for different clock frequencies, custom instructions, topology, cache associativity degrees, cache replacement policies, cache sizes, write back policies, bus width, buffer sizes, bus speeds, memory types, memory width, and interconnect protocols like the TileLink. For each of the system model run, the simulation model generates various statistics including details on pipeline stalls, pipeline utilization, execution unit utilization, execution unit buffer occupancy, instruction and data cache accesses, cache hit ratio, number of evictions, write backs, memory throughput, cycles per instruction, memory access latency and network latency. Using the system model, we were able to obtain debug logs from each SoC subsystems including the RISC-V cores. Using the pipeline traces for each instruction, we able to verify the behaviour of custom instructions which were introduced to improve our application performance. Performance and functional requirements were provided as input to the system model and faults were injected into the system model to determine the expected performance of the system under failure. Our SoC design was then updated to improve the fault tolerance and application performance by 30% under faults by adding redundant cores and error correction mechanisms, based on early results.

ABSTRACT. The growing need to provide reliable high bandwidth, low latency communication services to remote and underserved locations is being addressed in both the commercial and defense space sectors. The result is the proliferation of low earth orbit (LEO) satellite constellations with complex radio frequency (RF) communications payloads. To fully realize the potential of networked communications satellites in LEO, satellite designers are creating electronically-steerable multi-beam antenna systems. These antenna systems rely on digital beamforming, a technique which enables an active electronic array antenna to efficiently direct RF energy to the intended recipient. Digital beamforming is a complex technique which requires significant parallel computing resources to operate in real-time at rates of hundreds of billions of complex multiply and accumulate operations per second. Availability of highly integrated semiconductor devices with heterogeneous computing resources is enabling unprecedented flexibility for designers of satellite RF systems. With newly available products, designers can implement reconfigurable processing platforms which, under software control, can respond to evolving mission parameters with a variety of disparate compute resources which enable efficient digital beamforming as well as optimal real-time control of modulation techniques, in-flight updates of waveforms, and AI-based decision-making. AMD Versal ACAP (Adaptive Compute Acceleration Platform) devices combine application class processors, real-time processors with lock-step capabilities, AI acceleration vector processors, DSP blocks capable of 27 x 24 multiplication operations with 58-bit accumulation, and an infinitely reconfigurable programmable logic fabric in a single chip form-factor with sufficient radiation tolerance for LEO and GEO space missions. The density, variety, and performance of the various computing resources in the Versal ACAP devices enable a significant leap forward in the drive toward miniaturization of satellite payload processing systems, and allow designers to achieve autonomous decision making on orbit based on highly complex analysis of high bandwidth data from multiple sensors. In this paper we review the application of Versal ACAP technology to a digital beamforming application, including a discussion of the implementation of the matrix multiplication using the array of vector processing engines integrated into the monolithic Versal device. We will also provide the latest information on the radiation characterization of the Versal ACAP family, including test data on the Adaptive Intelligent Engines and the gigabit transceivers. Current status of qualification for space-flight applications and product availability will also be covered.

ABSTRACT. Commercial, government, and defense space systems will operate in some level of naturally occurring radiation. Additionally, terrestrial systems are often exposed to natural or man-made radiation effects. These radiation environments include space, nuclear events, and atmospheric neutrons.

In this session we will discuss how designers can use automated methods to meet their radiation environmental requirements in their microelectronic designs. There are three key areas of focus: device-level radiation modeling, functional safety (FuSa) design methodologies, and fault tolerant IPs for radiation designs. An interesting aspect of this discussion is that Synopsys is working with JPL and other partners to leverage commercial capabilities that enable various levels of radiation environments, from low earth orbit to strategic radiation hardened solutions.

Another area of focus is high performance space computing. An existing IP library and suite of EDA tools allows designers to prioritize low power, processing bandwidth, fault tolerance and radiation performance. The EDA tools can manage dual core lock step (DCLS) processors with very little manual intervention with an emulation and verification environment that can model single or multiple core designs.

ABSTRACT. Access to space is increasingly fundamental to our modern world. Launch volume is rapidly increasing annually, requiring increasing spaceport and ground support capabilities. The design, ownership, and operation of spaceports are bespoke, where their cybersecurity considerations are always an afterthought. This paper focuses on the critical role spaceports play in the access to space and their vulnerabilities to cyber attacks. Further, it demonstrates that without strong cybersecurity, adversaries can easily deny access to space.

ABSTRACT. The Cyber Analysis Visualization Environment (CAVE) software system is a collaboration between the Systems Engineering and Mission Systems and Operations Divisions at JPL. CAVE is an easy to use, model-based cyber threat visualization, analysis, and assessment platform designed to respond to the growing threat of cyber attacks targeting space missions. CAVE was originally designed and developed as a Python-based desktop application with integrated 3D visualization, user interface, and a cyber analysis engine, before a recent redesign to the current client/server web-based application architecture. This new client/server web application architecture allows users to use CAVE on multiple platforms, requiring minimal or no software installation on their local systems while still allowing for a level of customizability at the user level. CAVE’s client/server architecture ensures that sensitive or proprietary data always resides securely on the CAVE analysis server, thereby avoiding the storage of any sensitive data on the end user‘s local machine. At the high level, the CAVE web server exposes a REST API to allow the web GUI to communicate with the analysis engine. The web server has user authentication capabilities, and restricts access to model data based on the assigned group of the authenticated user. The server is architected with a plugin structure in mind for analyses, so that anyone opting to deploy the CAVE server can easily write and deploy additional model analyses. Model data is stored in a Mongo database and delivered to the client via authenticated HTTPS request. The client handles all interactions with the web server and provides feedback to the user on the status of server requests. CAVE’s web-browser user interface provides an intuitive, 3D presentation of complex network models, containing both physical and virtual network assets, which allows mission cybersecurity engineers to easily swap between a selection of visual layouts and execute a variety of cybersecurity analyses algorithms designed to help users better understand possible network vulnerabilities and defenses. This web-based user interface model allows for ease of expansion and potential future additions to the user experience, including real-time collaboration and sharing capabilities, and the interactive exploration of large cybersecurity datasets. In this paper we present CAVE's software architecture and use cases, and discuss CAVE's value as an intuitive tool for cyber threat identification and assessment.

ABSTRACT. Mirabilis Design enables creation and reuse across the continuum of concept to mission lifecycle. VisualSim Architect is a system modeling and simulation platform with IP modules for rapid model construction and simulation framework with decision support system including reporting, optimization capabilities and visualization. The environment integrates with SysML use cases, workload traces, requirements database and existing simulators. VisualSim can be used in the design of semiconductors, embedded systems, software and networking.

ABSTRACT. Autonomous agents performing in-space assembly tasks must make informed decisions about the sequence in which they assemble a structure. In order for these algorithms to be trustworthy, care must be taken to ensure that there is reproducibility between low fidelity and high-fidelity simulation results as well as a strong correlation between the simulation results and the physical behavior of the hardware performing the operations. A method of assembly sequence optimization using surrogate models will be presented.

ABSTRACT. In this talk, we examine the issue of maintaining desirable safety characteristics in an operational environment under uncertain conditions. While operating in an uncertain environment, agents are faced with both an uncertain model of themselves, and of other agents. This leads to challenges in enforcing operational safety measures, such as minimum separation, desired speed, or spatial constraints. To address this challenge, we consider the issue of an autonomous quadcopter operating in an unsafe and uncertain environment. We first demonstrate derivation of an Exponential Control Barrier Function (ECBF) from quadrotor dynamics, assuming no parametric uncertainty or external disturbance, in order to enforce minimum separation. This exercise highlights both the impact of external disturbances on vehicle safety and establishes a baseline comparative method to build upon. We will then extend this derivation to adapt to parametric uncertainty, resulting from (for example) external disturbance or faulty sensors. After establishing this uncertainty-robust control method, we will explore ways to incorporate desirable airspace safety measures using Control Lyapunov Functions (CLFs), and how such safety measures can be made likewise uncertainty-robust in order to assure both safe flight (through ECBF satisfaction) and coordinated flight (through CLF optimization). Finally, we will discuss how approaches used in atmospheric flight uncertainty management and adaptation can be extended to in-space autonomy.

ABSTRACT. All actions in any system are a result of solving various decision-making problems, at various scales. Successful and safe functioning of the system depends on the ability of decision makers, human or machine, to reach successful solutions in time – the factor to which system uncertainty may be reduced. In this talk, we discuss one approach to reducing system uncertainty: increasing the likelihood that the upcoming decision-making problems are solvable on a required time budget, in principle. As a side effect, the approach points to a dynamic determination of the entity entrusted with solving a specific decision-making problem in multiagent environments.

ABSTRACT. The architecture of robotic systems must take into account many unique factors not readily apparent to the outside observer. Using the Mars Sample Recovery Helicopter’s tube acquisition sub system as a case study, the factors driving the architecture decisions and methods used to determine the path forward will be discussed. The current limitations in space robotics that are pain points will be highlighted as community opportunities for advancement of the field.

ABSTRACT. The lunar environment is significantly harsher than that of Mars. Lunar surface explorers will be operating in vacuum and have to cope with extreme temperature swings, extreme cold (30-60 K, 18K at coldest Permanently Shadowed Region), extended periods of darkness, tribocharging (static electricity build up), and abrasive and hard to remove lunar dust. In addition, many areas of interest such as PSRs and lava tubes have steep slopes, uneven terrain, and temporary or permanent communication blackouts that preclude teleoperation. As a result, the model of supervised autonomy developed for operating Martian rovers is unlikely to meet the needs of lunar surface robots. Operations that have been customarily performed by mission ops on earth will need to be performed by software on the robot instead. This presentation will discuss the environmental challenges of the Moon and the ways in which they will demand new capabilities from robotic software.

ABSTRACT. In this talk we will discusses the need for a new paradigm in robotic space exploration that can effectively explore challenging destinations beyond Mars. The current incremental approach used for Mars exploration, based on detailed environmental knowledge, is not applicable in environments with long cruise times and limited launch opportunities. The proposed paradigm, called "into the unknown," emphasizes versatility and intelligent, risk-aware autonomy. The talk introduces the Exobiology Extant Life Surveyor (EELS), a highly versatile and intelligent snake-like robot designed for exploring Enceladus vents and other challenging targets. EELS has a high degree of mechanical flexibility, allowing it to adapt to unknown environments. It is equipped with a novel autonomy framework called NEO, which enables control and decision-making in extreme and unknown environments. Prototypes of EELS have been developed and successfully tested for surface and vertical mobility. The talk highlights the importance of versatility and intelligence in addressing environmental uncertainty and illustrates how EELS outperforms Mars rovers in coping with unknown terrain. The proposed paradigm and the development of EELS represent a significant shift in robotic exploration strategies for future missions beyond Mars.

ABSTRACT. Field Programmable Gate Array (FPGA) technology has been used extensively in space applications where the natural radiation environment presents major challenges to electronic parts. Commercial FPGA technology is trending to deep nano-meter silicon processes, which impacts the availability of radiation resilience FPGA chips. Space systems require long timeframes for development and launch, and often the electronics and code may become obsolete or require updating before the system can be launched. FPGA logic/fabric-size continues to grow dramatically which allows and practically requires more and more IP cores to be integrated within a chip. New IP cores and tools will be needed to enable space designs with commercial FPGA technology to withstand radiation. This paper discusses the challenges in designing FPGA-based space systems and potential open-source and commercial technologies that will be useful to space application developers. It also references an ongoing FPGA based space telescope spectrometer design to discuss different aspects of complex FPGA design with mixed analog and digital circuits.

ABSTRACT. Advanced algorithms dictated by future mission needs are pushing space-borne computing requirements. Current platforms used in manned and unmanned spaceflight are limited by the intersection of radiation tolerance, power consumption, computing performance and safety critical features. Until recently, advanced instruction set architectures (ISAs), algorithm specific instructions, high speed external interfaces and high performance on-chip networks were eschewed from processor designs and current production spacecraft processors are based on past computing paradigms.

Advances in processor manufacturing, emerging ISAs and machine learning techniques will significantly impact future system-on-chip (SoC) designs, enabling true high-performance space computing. To better understand the computational requirements of modern spacecraft, a comprehensive set of benchmarks that include basic system characterization, high performance computing, navigation and landing, image recognition, route finding, data mining and machine learning are necessary to characterize candidate architectures. The analysis of these space flight focused algorithms will drive the design of next generation space computing SoCs.

ABSTRACT. SiFive is the Founder and Brand Standard for RISC-V processor IP. The RISC-V portfolio includes robust processor offerings that are integrated into solutions supporting many aspects required for present and future space missions, including hi-rel. SiFive will provide an overview of the RISC-V portfolio, in addition to engagement overviews with both the government and DIB and SiFive's desire to provide differentiation to the space community.

ABSTRACT. Welcome and introductory comments

ABSTRACT. He will explain how JPL’s Chief Technology and Innovation Office explores and researches AI, Analytics, and Innovation in the Information Technology and Solutions Directorate (ITSD) supports advanced analytics, AI and Machine Learning for Smarter Rovers, a Smarter Campus, and beyond.

ABSTRACT. We introduce model-agnostic approaches for explaining machine learning predictions and testing algorithms to ensure their robustness. The goal is to create tools that are broadly applicable to any existing and future machine learning algorithms and produce intuitive, user-friendly descriptions to encourage wider adoption. Our ultimate objective is to advocate for more responsible use of machine learning, which can have a deep and transformative impact on its applications.

ABSTRACT. Intelligent exploration is the practice of using AI to guide a user through the analysis of multidimensional, massive, datasets. Generative AI can be used to create content (such as text and visualizations) and can be used to ask questions in a more intuitive way. By coupling intelligent exploration and LLMs we can create tools that can explore complex datasets more effectively. This can lead to new discoveries and insights that would not be possible with traditional methods.

ABSTRACT. Panel Discussion: Explainable AI

ABSTRACT. This talk looks back at the Descent Image Motion Estimation System (DIMES), used successfully to reduce a critical mission risk during the landings of NASA’s two Mars Exploration Rovers on Mars in 2004. We will see how the DIMES’ designers addressed challenges of uncertainty present both in the environment, and those inevitable in the time and computationally constrained envelope within which their system had to operate.

ABSTRACT. Panel and Open-Mic Discussion

ABSTRACT. Space and planetary surface missions will continue to rely on and leverage robotics technology. As the scope of missions to new science and exploration destinations evolves, so will the robotics problems and associated software-centric solutions. Persistent robotics software gaps remain while new gaps will emerge driven by future mission needs. What would help is a greater collective effort to address the needs via use of common software frameworks that offer a range of solutions spanning robotic In-space Servicing, Assembly, and Manufacturing capabilities. This talk considers robotics software-related topics encountered over the course of several decades of experience. It touches on a variety of what are considered to be current gaps related to software-based robotic capabilities needed for on-orbit and surface missions. Considerations associated with flight and ground software for robotics are highlighted along with mission/flight system matters, including relationships to computing, cybersecurity, sensing & perception, control modes, interoperability, testing, and human interaction.

ABSTRACT. Panelists: Ivan Perez Dominguez, Shuan Azimi and Kalind Carpenter

ABSTRACT. Reinforcement Learning (RL) has become an increasingly important research area as the success of machine learning algorithms and methods grows. To combat the safety concerns surrounding the freedom given to RL agents while training, there has been an increase in work concerning Safe Reinforcement Learning (SRL). However, these new and safe methods have been held to less scrutiny than their unsafe counterparts. For instance, comparisons among safe methods often lack fair evaluation across similar initial condition bounds and hyperparameter settings, use poor evaluation metrics, and cherry-pick the best training runs rather than averaging over multiple random seeds. In this work, we conduct an ablation study using evaluation best practices to investigate the impact of Run Time Assurance (RTA), which monitors the system state and intervenes to assure safety, on effective learning. By studying multiple RTA approaches in both on-policy and off-policy RL algorithms, we seek to understand which RTA methods are most effective, whether the agents become dependent on the RTA, and the importance of reward shaping versus safe exploration in RL agent training. Our conclusions shed light on the most promising directions of SRL, and our evaluation methodology lays the groundwork for creating better comparisons in future SRL work.

ABSTRACT. This paper describes the Autonomy System that flew on the Double Asteroid Redirection Test (DART) mission. A detailed description of the rules-based logic is provided, including the heritage of the system, how it was tailored to meet DART’s fault management philosophy, build process, testing, and necessary in-flight updates. The purpose of the Autonomy software was to implement fault protection against components’ off-nominal behaviors and to implement a safing strategy designed to place the spacecraft in a known, safe configuration in response to critical faults while Mission Operations worked on a resolution. Aside from maintenance and fault protection tasks, the onboard Autonomy software was key to guaranteeing the execution of time-critical sequences during the mission. The resulting system was also instrumental in being the tool of choice to address anomalies in other subsystems that were discovered as early as a month into flight and as late as a month before impact.

ABSTRACT. Accurate mapping of software requirements-to-tests can assure high software reliability as a result of robust traceability, test coverage, and improved transparency. Software requirements change frequently across mission phases. A testable and measurable requirement maintenance and tracing are essential in all phases of mission life cycle. In a development phase, a predictable and controlled software system deployment, test, and integration can firmly support mission’s rapid innovations. In an operation phase, patches need to be applied periodically and prompt evaluation and verification turnaround are critical. By integrating and exercising Natural Language Processing (NLP) and Machine Learning (ML) assisted model-based test engineering (MBTE) many of these challenges can be overcome. This paper will present a new and novel method and lesson learned from formalizing and automating the software requirement-to-test mapping, which allows engineers to review the recommendations generated by the automated system.

ABSTRACT. AI inferencing in space opens new opportunities for enhanced payload data processing and autonomous decision making on orbit. In this talk we review the silicon architecture and software tool flows which enable designers to use AMD’s radiation tolerant Versal adaptive SoCs for AI inferencing in space.

ABSTRACT. A method and design are described for a system that processes multiple data streams, utilizing a multicore asymmetric processing architecture, that eliminates data interrupts to the application processors. The design supports a deterministic environment for flight software in NASA’s Safe and Precise Landing – Integrated Capabilities Evolution (SPLICE) project. The SPLICE project develops sensor, algorithm, and compute technologies for Precision Landing and Hazard Avoidance (PL&HA) capabilities. The compute technology for SPLICE is the Descent and Landing Computer (DLC). The DLC hosts several SPLICE algorithms with high computational resource requirements that must be executed in a real-time and deterministic manner. The software runs on a custom Single Board Computer (SBC), with a Xilinx Ultrascale+ Multiprocessor System-on-a-Chip (MPSoC). Input data for the flight software is from a variety of sensors, unique with respect to data rate and packet size. A data path between the SPLICE sensors and algorithms is designed to efficiently deliver this data to the flight software using the MPSoC asymmetric processing cores and Field Programmable Gate Array (FPGA) fabric. This is implemented in a manner that isolates the application processors running the flight software from interrupts associated with the input data. By leveraging real-time processors on the MPSoC, and a structure with the appropriate interfaces in the shared memory on the SBC, the flight software can use the full set of application processors. The available utilization for each processor in this set is also maximized for the SPLICE applications, providing a sufficiently deterministic execution environment without the cost and overhead of a real-time operating system.

ABSTRACT. ROS2 (Robot Operating System Version 2) is an open-source software framework and suite of libraries that facilitates robotics application development and multi-core processing. ROS2 is becoming the de-facto standard for autonomous robotics application and recently, with the sponsorship of NASA and Blue Origin, space-ros -a pre-qualified implementation of ROS2- it is also started to be used for Space autonomous applications, like landers and space robotics.

ROS2 data workflow design has been thought out to provide a robust and uniform access to sensor data via different supported middleware including DDS and memory sharing solutions. While this design has largely simplified the development effort required to write applications based on ROS2, the processing performance has been compromised and it is not uncommon to face long latencies in receiving sensor data or even non-negligible data losses.

The ROS2 community -and specifically the Real-Time Working Group (RTWG)- has made a big effort in trying to improve this situation that resulted in the creation of the Static Single Thread and the Multi-thread Executors. These executors have yielded substantial performance improvements for some specific scenarios respectively. However, the community has been unable to find a general approach to solve the performance bottlenecks in ROS2 and has left to the developer to find the optimal combination of executors for the use case at hand.

Other industries like the financial industry have reached ground-breaking speed by parallelization of data processing algorithms that enable real-time applications based on the so-called lock-free programming technique. This technique, although very complex to program efficiently, can yield performance results when used appropriately. The authors of this paper have implemented a lock-free ring-buffer and validated it in different onboard computers with outstanding performance results for sensor data aggregation, processing, and even artificial intelligence.

The research presented here shows a ROS2 executor implemented of our lock-free ring-buffer that can not only increase by several times the data processing rate with respect to the above-mentioned executors, but also reduce the processing consumption substantially. Moreover, a ROS2 multi-node approach of this executor has been implemented combined with offline optimization based in genetic algorithms, that can improve the performance of virtually any scenario for ROS2 applications.

Successful performance results of this novel ring-buffer ROS2 executor and the multi-node variation are presented using the so-called reference system and validated in several Space computers including Unibap's iX5, Teledyne e2v's LS1046 and Xilinx Ultrascale+.

ABSTRACT. NASA’s space-based observation platforms generate unprecedented amount of data, requiring new capabilities, ranging from enabling significantly greater computing capabilities onboard and across robotic spacecraft, to drive autonomy, interpretation and automation, and to support new computing architectures that bring space and ground systems seamlessly together. We will discuss the challenges and opportunities for future observing systems for space, and the integration of these capabilities into the next generation of space observing systems.

ABSTRACT. For space missions that venture far from Earth, real-time decision-making isn't always possible due to numerous challenges. Many deep-space science cases revolve around change detection, and anomaly detection. We will outline a few science cases, and show how advances in data fusion and machine learning pave a way for progress and innovation in such contexts. Finally, we propose steps to equip near-future missions to make the best of such capabilities.

ABSTRACT. The Director of the National Reconnaissance Office, Dr. Chris Scolese, remarked in his keynote address at the Space Symposium in April 2023, “We’re also increasing automation, multi-intelligence processes and machine learning capabilities so we can operate at the speed of machines, and deliver the right information at the right time to the right place”. This contribution will highlight the potential impact of advancements in scalable in-space computing, data and networking services from the perspective of Intelligence, Surveillance, and Reconnaissance applications.

ABSTRACT. Panel Discussion: Space Clouds

ABSTRACT. Concluding Remarks

ABSTRACT. The German Aerospace Center (DLR) is developing ScOSA (Scalable On-board Computing for Space Avionics) as a distributed on-board computing architecture for future space missions. The ScOSA architecture consists of commercial off-the-shelf (COTS) and radiation-tolerant nodes interconnected by a SpaceWire network. The system software provides services to enable parallel computing and system reconfiguration. This allows ScOSA to adapt to node errors and failures that COTS hardware is susceptible to in the space environment. In the ongoing ScOSA Flight Experiment project, a ScOSA system consisting of eight Xilinx Zynq systems-on-chip with dual-core ARM-based processors and a LEON3 radiation-tolerant processor is being built for launch on DLR's next CubeSat in late 2024. In this flight experiment, not only all 18 cores but also the programmable logic will be used for high performance on-board data processing. This paper presents the current hardware and software architecture of ScOSA. The scalability of ScOSA is highlighted from both hardware and software perspectives. We present benchmark results of the ScOSA system and experiments of the ScOSA system software on ESA's OPS-SAT in orbit in combination with a machine learning application for image classification.

ABSTRACT. The James Webb Space Telescope is the successor to the Hubble Space Telescope. It is the largest space telescope ever constructed and is giving humanity its first high-definition view of the infrared universe. The Webb is observing early epochs of the universe that the Hubble cannot see to reveal how its galaxies and structure have evolved over cosmic time. The Webb is exploring how stars and planetary systems form and evolve and is searching exoplanet atmospheres for evidence of life. The Webb’s science instrument payload includes four sensor systems that provide imagery, coronagraphy, and spectroscopy over the near- and mid-infrared spectrum. NASA developed the JWST in partnership with the European and Canadian Space Agencies, with science observations proposed by the international astronomical community in a manner like the Hubble. Launch of the Webb occurred during Christmas day 2021. In-flight commissioning was completed during June 2022 and science operations are now underway.