Michael Lee Rilee

ANTS for Human Exploration and Development of Space

The proposed Autonomous Nano-technology Swarm (ANTS) is an enabling architecture for human/robotic mission envisaged by NASAs mission for the Human Exploration and Development of Space (HEDS). ANTS design principles draw on successes observed in the realm of social insect colonies, which include task specialization and sociality. ANTS spacecraft act as independent, autonomous agents for specific functions, while cooperating to achieve mission goals. For example, the Prospecting ANTS Mission (PAM) is a long-term mission concept for the 2020-2025 time frame involving individual spacecraft agents that are optimized for specific asteroid prospecting functions. The objective of PAM is to characterize at least one thousand asteroids during each year of operations in the main belt. To achieve this objective, PAM spacecraft, individually and as a group, must achieve a high level of autonomy. This high degree of autonomy opens the possibility of a new kind of interaction between humans and these spacecraft, where human explorers and developers could interact with ANTS enabled resources by communicating high-level goals and data products. Thus ANTS enables new kinds of missions in which human and robotic agents work together to achieve mission goals. In this paper we review and discuss the ANTS architecture in the context of the HEDS mission.

Tetrahedral Robotics for Space Exploration

A reconfigurable space filling robotic architecture has a wide range of possible applications. One of the more intriguing possibilities is mobility in very irregular and otherwise impassable terrain. NASA Goddard Space Flight Center is developing the third generation of its addressable reconfigurable technology (ART) tetrahedral robotics architecture. An ART-based variable geometry truss consisting of 12 tetrahedral elements made from 26 smart struts on a wireless network has been developed. The primary goal of this development is the demonstration of a new kind of robotic mobility that can provide access and articulation that complement existing capabilities. An initial set of gaits and other behaviors are being tested, and accommodations for payloads such as sensor and telemetry packages are being studied. Herein, we describe our experience with the ART tetrahedral robotics architecture and the improvements implemented in the third generation of this technology. Applications of these robots to space exploration and the tradeoffs involved with this architecture will be discussed.

Neural Basis Function Control of Super Micro Autonomous Reconfigurable Technology (SMART) Nano-Systems

** Nanotechnology, taken to its full three-dimensional potential, will place within the volume of a cube of sugar systems of vast complexity that far exceed the quantitative and qualitative capabilities of today’s largest supercomputers. Currently, the uncertainty and imprecision of the real world is tamed, rigidly fixed, by addressable, digital techniques and the careful orchestration of digital patterns within our machines. How to handle the interaction between our digitally implemented systems and continuous, disorganized nature is a key question. NASA is currently researching ways to move beyond autonomy implemented as bruteforce control over every degree of freedom we can discover in our systems. Our systems operate in natural environments: inhumanly harsh, unfamiliar, unknown, and uncontrolled environments. Nature often surprises us, and so we turn to natural systems for clues about how to make massively complex systems more robust, reliable, and truly autonomous. Turning to Computer Science we draw on what we’ve learned about multi-agent systems running continuously and autonomously to understand information flow at the highest semantic levels. From physics we recall that the behaviors of systems may often be enumerated in a basis of fundamental behaviors. Non-linear physics contains clues about how to connect the physical world with the patterns of electric signals that make up the soft, information component of the systems. Genetics and control theory instruct how to handle long and short-term feedbacks throughout the system. Chemistry and biology provide important guiding principles governing system functions.

Mobile science platforms for impassable terrain

Some of the most scientifically interesting terrain is among the most inaccessible, presenting problems for all mobility strategies. Lava flows, for example, can have structure at all scale sizes rendering traversal via appendage or wheel difficult at best. NASA researchers have been developing an innovative mechanical structure that provides mobility in terrain unnavigable by wheeled or even legged vehicles. We are developing a mobile science platform (MSP) that is completely symmetric, with neither top nor bottom, so that it cannot fall down and fail to get up. The MSP actively navigates its environment to place its payload or gathers samples in places otherwise unreachable, e.g., the lunar highlands or rugged volcanic terrains on Mars

Evolving a self -organizing neuromechanical system for self -healing aerospace structures

§NASA is developing a novel articulated truss built from a highly redundant, highly integrated network of actuators. Near -term implementations of this truss a rchitecture make use of Addressable Reconfigurable Technology (ART) and can be centrally controlled as, for example, one’s hand is directed by the central nervous system. Mid -to -far term implementations make increasing use of micro - and nano -technologies to allow truss systems to scale eventually through thousands of nodes and beyond into the realm of Super Miniaturized Addressable Reconfigurable Technology (SMART). Structures made from this material may take on shapes as required to meet mission requirem ents for deployment, storage, locomotion, shape control, and so on. One of the key applications of this technology is for nano - and pico -spacecraft. Furthermore, the large number of redundant elements opens up many possibilities to address and mitigate fa ults and failures within the truss. Not only does this architecture provide physical reconfiguration, system control must be able to adapt to its new configuration. In this work, we describe a new control architecture for a synthetic neural system design ed to meet this challenge. Genetic algorithmic evolution within supercomputer -based simulations allows the system components to situate themselves amongst each other and their environment. The synthetic neural system is based on composable behavioral uni ts called Neural Basis Functions (NBF) that provide a way to unify low -level autonomic and high -level reasoning in a single operational architecture. Distributed systems fit naturally within this framework. We describe fault modes of and recoveries enable d by the architecture and the results of our first attempts to construct a synthetic neural system based on NBFs with a focus on the self -organizing and self -healing properties of the system. We emphasize the scaling issues associated with the large number of nodes in nano -technology -based SMART structures and how distributed systems, e.g. multi -spacecraft systems, are controlled.

Thriving in the irregular and the unknown: system control for space exploration

Crucial to the development of a system of systems infrastructure for space exploration is a truly scalable control architecture. This architecture must be built on the reliable, adaptable operation and co-operation of autonomous systems at multiple levels. Advances in hardware and software computing technology allow us to consider anew the range of control systems from reactive, lowlevel to deliberate, heuristic systems. At NASA’s Goddard Space Flight Center (GSFC), we have been developing the means to create space and surface systems that are active participants in their environment rather than being merely visitors that withstand space’s hazards as our extended remotely controlled tools. Central to this work has been the development of the Autonomous Nano-Technology Swarm (ANTS) mission architecture and the Neural Basis Function Synthetic Neural System (NBF/SNS) which are included among the subjects of several GSFC provisional patent applications. These are scalable systems with non-linear dynamics built in to deal with irregularity, uncertainty, and unpredictability in their environments. These system architectures outline pathways from existing near-term capabilities to far-term enabling technologies.

Frontier, a decision engine for designing stable adaptable complex systems: Adaptive framework

Frontier is a web-based framework for concurrent multidisciplinary collaboration on the analysis and design of system architectures associated with systems of systems. Supported by DARPA TTO System F6 (Future, Fast, Flexible, Fractionated, Free-Flying spacecraft architecture), the aim is to develop a framework that can be used to analyze alternative approaches to the use of space. In particular, Frontier aims to support analyses involving radically different viewpoints and approaches that are nonetheless equally legitimate views of the system or architecture under study. Furthermore, Frontier aims to federate these different approaches in a single extensible framework of interoperability standards. Frontier provides a methodology for integrating resources such as engineering tools and libraries for use on Frontier. The plan is to allow Frontier users to use their existing tool chains to produce and consume resources for a world-wide web-enabled aerospace market. Eventually, the goal is to enable aerospace stakeholders, analysts, suppliers, and others to work on distributed projects ranging through entire life cycles, from preliminary studies, collaborative bidding, design, implementation, operation, maintenance, decommissioning, replacement, and so forth. Frontier makes aggressive use of emerging standards and web technologies, especially the semantic web, and with its focus on open source methods it aims to become the way to exchange and collaborate on aerospace data and projects. A semantic bus is used to discipline the exchange of information and to manage translations between different contexts. Social media are available providing multiple ways for Frontier users to interact with each other and the tools and resources available on Frontier. Being a web-based and standards-based framework, new technologies can be brought into Frontier enabling new modalities of use and interaction. A central element of Frontier is a synthetic neural system (SNS) that monitors the performance of system tools and aids in both the selection of tools and the development of resources for use on Frontier. Since a key use case of Frontier is the development of analyses to decide between candidate architectures, the SNS aids in the selection and development of analysis tools and even the elements of the candidate architectures themselves. The behavior of the SNS is implemented with Neural Basis Functions that interact via an Evolvable Neural Interface. Chief among these NBFs is an executive function, a ToolUser, and a social interface to the user-facing human interface. Linked together and governed by the Stability Algorithm for Neural Entities (SANE), these NBFs provide the core adaptability of the system. Critical for the future development of space is creation of an adaptable and flexible system into which multiple stakeholders can provide modular capabilities that address their individual needs in as coherent and stable a manner as possible. A stable process for determining candidate architectures that meet as many goals of a community or marketplace of space services is a central goal of the DARPA F6 project. The pathway towards this goal is the SANE approach developed at NASA. In this work, Frontier researchers describe how the development of flexible, viable systems of aerospace systems and services is enabled by the use of artificial intelligence technology originally developed to address the needs of autonomous spacecraft operation in uncertain and irregular (chaotic) situations.

Frontier: Intelligent Decision Engine for Stable Adaptable Complex Systems

Frontier is a framework for a highly adaptable, stably reconfigurable, web–accessible intelligent decision engine developed to a) optimize the design of complex (particularly multi– asset or fractionated) systems, as well as b) simulate and operate systems distributed spatially and temporally in response to evolving needs and environments in support of the DARPA Tactical Technology Office (TTO) System F6 (Future, Fast, Flexible, Fractionated, Free–Flying) program. Innovatively and uniquely, Frontier is capable of absorbing and utilizing lessons learned and thus evolving from tool to tool user with tools via an adaptable framework utilizing an Intelligent Decision Engine (IDE) in a Web Support Environment (WSE). The IDE is based on a

Processing Information and Data

Tracking and processing information in all forms is essential at every stage of a remote sensing project. Increasingly sophisticated processing capabilities have been both driven by remote sensing requirements and a driving force behind the development of remote sensing. In fact, due to the large volume of information and the large number and complexity of steps involved in transforming it, sophisticated data handling capabilities became an absolute necessity for remote sensing long ago. Thus, computer science and engineering have played major roles in remote sensing.

Ray Region: X-rays, Alpha Particles, Gamma-rays, Neutrons, UV

Remote measurements of the high energy spectra generated from high energy interactions on planetary surfaces with minimal atmospheres are crucial in determination of a planet’s bulk composition and major geochemical provinces, particularly when combined with in situ surface or sample measurements. Derivable from such measurements are models for planetary origin and geochemical differentiation as well as for the exterior (bombardment) and interior (volcano-tectonic activity) driven processes which shape major terrane and feature formation on planetary surfaces. Inferences about composition can be drawn from visible and infrared data in the form of major mineral components, providing constraints on models of origin. Elemental abundance maps can be derived indirectly from such data, when assumptions are made about elemental abundance ratios in major minerals, but only nuclear and near nuclear particle interactions produce characteristic transitions in the ray region which can be measured to provide direct elemental abundances.