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In this paper, we describe the development of an integrated suite of autonomous, adaptive hardware/software control methods called SMART (System for Mobility and Access to Rough Terrain) that enables mobile robots to explore potentially important science sites currently beyond the reach of conventional rover designs.
The input output unit (IOU) for the Attitude and Articulation Subsystem (AACS) for the Cassini spacecraft uses an embedded microprocessor to format and interpret data packets sent over a bus. This new approach is part of a trend toward "smart" peripherals and instruments communicating over much higher bandwidth optical buses.
A new CMOS active pixel image sensor is reported. The sensor uses a 2.0mue double-poly, double-metal foundry CMOS process and is realized as a 28 x 28 array of 40 mue x 40 mue pixels. The Sensor features TTL compatible voltages, low noise and large dynamic range, and will be useful in machine vision and smart sensor applications.
A joint research team from California Institute of Technology and Jet Propulsion Laboratory is developing a fully miniaturized, smart implantable neuro-prosthetic system, by combining integrated electronics with Micro Electro Mechanical Structure (MEMS) based electrode arrays. The packaging group of this project team has been tasked with designing and fabricating a package to interface electrically between a MEMS electrode array, capable of extracting signals from brain, and an electronic chip, capable of providing on chip conditioning/processing of extracted data, while delivering the output data to a detector located outside the body.
Mars exploration requires multiple orbiters and surface infrastructures on Mars. Location determination is essential to support various human and robotic activities on the Mars surface and in orbits. Robust communication coverage is required for data transmission between assets on Mars surface and orbiters in addition to the communication between orbiters and Earth. We proposed a notional Mars Regional Navigation Satellite System (MRNSS) that provides continuous or near-continuous in-situ navigation and timing services for the robotic and human missions in the vicinity of a Mars landing site. For such a system, the Earth’s deep space stations have to perform orbit determination (OD) for the Mars orbiters so that their locations are known to a reasonable degree of accuracy. This provides the surface and orbiting user vehicles the ability to estimate their locations using the orbiter-vehicle range measurements. Two important inputs for accurate OD are 2-way Doppler and ranging measurements derived from the transmitted and received radio signals. We also introduced a new flight-ground collaborative architecture that provides simultaneous Doppler and ranging measurements for multiple spacecraft in the Mars vicinity, using an example of the Mars landing site Utopia Planitia and four Mars orbiters, all communicating with Earth via twoway communications in the X-band frequency range. One key component of this architecture is that each spacecraft uses a smart frequency sweeping algorithm that acquires and tracks the uplink signals with high Doppler and Doppler rate residuals. In this paper, we simulate the Doppler and Doppler rate profiles of the Mars orbiters of the MRNSS and assessed the acquisition and tracking performances.
Historically, we have been pound foolish and penny wise for decades, and it is time to reverse this. We present an approach that will maintain critical NASA ablative TPS capabilities through small, sustained, smart and targeted investment. NASA’s ablative TPS history is one of developing new capabilities and letting them atrophy. When needed, there are two options: (1) revive the capability at considerable cost impact or (2) develop an alternate capability with considerable schedule impact. Either of these options have had considerable mission impact. The third alternate is to maintain proven capabilities in a cost effective manner to allow the science community to propose missions with confidence. NASA and DoD have had a working group to periodically assess and make recommendations on at-risk items. This working group typically focuses on raw materials availability. While we agree that at-risk raw materials are an important and necessary part of the overall capability, focus solely on them is not sufficient. Instead, we advocate for a broader focus on the critical “technology capability” that includes not only raw material but also industrial manufacturing and processing capabilities and in-house expertise in design, testing and flight hardware certification. In this proposed presentation, we will highlight a case study related to PICA capability. We will present the rationale for sustaining PICA and HEEET as two minimum essential ablative TPS capabilities for future NASA missions. We will present an approach for how small investment could lead to maintaining mission critical capabilities over a longer period. While HEEET and PICA are NASA unique, the raw material and some of the vendor capabilities involved also support DoD TPS. Hence a partnership between NASA and DoD is recommended for broader benefit.
We plan to present an approach for NASA to maintain critical ablative TPS capabilities through small, sus-tained, smart and targeted investment. Historically, we have been pound foolish and penny wise for decades and paid very little attention to sustainability of abla-tive TPS and as a result, spent couple of decades in learning what we lost and inventing newer technology solutions. NASA’s ablative TPS history has been one of in-vesting in new and unique capabilities in anticipation of needs, and once flight proven, letting the capability atrophy due to lack of subsequent near-term mission needs [1]. When the need arises, NASA has resorted to two options: (1) revive the capability at considerable cost impact, or (2) develop an alternate capability with considerable schedule impact. Both these options have had considerable mission impact. The third alternate is to maintain proven capabilities in a cost effective manner. A risk informed decision process in terms of maintaining ablative TPS capabilities is feasible with planning and sustained support. Assured TPS availa-bility will allow the science community to propose competed missions with confidence and at a lower risk for selection. We submitted a white paper to the cur-rent Decadal Survey [2] on the need for and im-portance of sustainability. Since then, we have been formulating an executable cost effective strategy. The intent of this presentation is to outline the develop-ments since the submission of the white paper. NASA and DoD have had a working group related to critical technologies to periodically assess and make recommendations on at-risk items from a national need perspective. This inter-agency working group typically focuses on raw materials availability. While we agree that at-risk raw materials are important, fo-cus solely on them is not sufficient. Instead, we advo-cate for a broader focus that includes not only raw material but also industrial manufacturing and processing, and NASA expertise in design, testing and flight hardware certification. In this proposed presentation, we will give a brief overview of NASA’s ablative TPS history along with recent examples of atrophy to highlight case studies related to the PICA and Carbon-Phenolic TPS. We will present the rationale for sustaining PICA, HEEET and 3MDCP for future NASA missions. We will present an approach for how targeted small investments could lead to maintaining mission critical capabilities over long periods.
Over the period 2016 to 2020, Integrated Demand Management (IDM) concept, procedures, and tools have been developed - first for clear-weather and a single airport constraint and then expanded to handle a multi-constraint problem during convective weather at Newark (EWR), LaGuardia (LGA) and Philadelphia (PHL) airports. The concept was evaluated in a series of human-in-the-loop simulations to confirm concept benefits in terms of better schedule predictability, reduction of delays, and increased throughput, especially during convective weather. In addition, other conditions with mixed participation of multi-trajectories from the airlines were evaluated that showed significant benefits to the individual airlines as well as the overall traffic flow. The IDM concept was initiated in the SMART-NAS project, was completed under the ATM-X project, and was developed collaboratively with the FAA and airline partners. Over the course of IDM development, NASA researchers produced 19 conference papers and publications. Outside supporting organizations, funded by IDM, produced 20 additional conference papers and publications, in which they advanced fundamental research on topics such as better stochastic traffic demand prediction, and application of machine learning techniques for modeling traffic management initiatives. Final concept procedures and tool specifications have been transferred to the FAA Air Traffic Organization Operational Concepts, Validation, and Requirements group.
Innovation requires continually using and coordinating new knowledge on the current, anticipated, and even unanticipated developments in a given area. You do not want to simply repeat what others are doing, so you must remain aware of other activities in your area of interest. You also have to maintain awareness of where the cutting edge is, because the limit of what’s possible is continually pushed and redefined. Finally, “smart” innovation involves not only seeking innovative solutions to anticipated problems but also being prepared to respond to unanticipated problems or even unanticipated solutions. As the leading U.S. agency for aeronautics development, cutting-edge scientific discovery, and human space exploration, the National Aeronautics and Space Administration (NASA) must remain at the forefront of innovation. NASA enables innovation in technology development through a coordinated effort among its internal mission directorates and its partners in government, commercial, and academic sectors. As an independent office, NASA’s Office of Technology, Policy, and Strategy (OTPS) provides the coordination effort necessary to ensure innovation. OTPS uses and coordinates inputs from a broad community of experts. With these inputs, OTPS identifies and communicates shared needs and opportunities for partnership. OTPS provides strategic advice to synergize and ultimately facilitate innovative technology development both within and outside NASA. This paper summarizes NASA’s approach to ensuring innovation through a deliberately coordinated effort. In this paper, we first provide examples of how this coordination has enabled innovative technology development. We then describe how NASA’s mission directorates use input from a broad community of experts to inform innovative technology development. Finally, we describe OTPS’s role in coordinating inputs from both internal and external experts, including standardizing communication about technology developments, facilitating conversations with external partners on shared technology needs and solutions, and advising on strategies and policies to ensure innovative technology development. This paper can help members of the aerospace community understand their role in NASA’s innovation efforts and how to engage OTPS in conversations about potential partnership.
This paper presents ARIEL (Autonomous Ranking and Interrogation of Excavation Location), an autonomy system for selecting an excavation site on-board for NASA’s Europa Lander Mission Concept. Historically, excavation site selection has been performed by a lengthy ground-in-the-loop (GITL) process involving manual inspections, assessments, and decision making in past missions. However, as Europa Lander would have approximately 20 days of lifetime after the landing, many surface activities, including excavation site selection, must be autonomously performed on-board. This paper describes the overall system of ARIEL as well as its two major algorithmic components: vision-based candidate selection and smart interrogation, which estimates the physical properties of the icy surface through physical contact with the robotic arm’s endeffector. Preliminary results are presented using images from Earth analogue sites. The Europa Lander mission returned to the formulation phases in early 2019 while ARIEL was at an early stage of development. Described in this paper is a snapshot of ARIEL as of the project suspension. This paper also describes the remaining challenges to be solved, should the mission resume in the future.
This paper proposes a risk-aware framework for Safe Multi-Agent Planning (SafeMAP) that unifies disparate models for multi-agent systems in a Markovian process that allows for simultaneous system health monitoring, decision making under uncertainty, and multi-agent system collaboration. As operations beyond low earth orbit mature, there is an increased need for autonomous cyber-physical systems with onboard decision making capabilities. Multi-agent cyber-physical systems in particular offer the potential of increased efficiency, resiliency, and mission capabilities for future applications such as multi-rover terrain operations, distributed satellite operations, and management of smart lunar habitats. SafeMAP utilizes physics-based models of each agent and the relevant components, probability models of the environment and component operational states, and reward models for mission-specific objectives such as scientific task completion or resource consumption. The output of SafeMAP is a set of mission plans that satisfy the mission objective under specified risk/reward constraints. A readable interpretation of each of these generated mission plans is provided as an additional output. SafeMAP has been demonstrated on a simulated case study involving a four-rover system performing surface mapping operations and science tasks. Results of this paper demonstrate SafeMAP’s ability to generate explainable mission plans that satisfy the mission objective while minimizing risk under nominal and off-nominal conditions.
An Earth-independent permanent extraterrestrial habitat system must function as intended under continuous disruptive conditions, and with significantly limited Earth support and extended uncrewed periods. Designing for the demands that extreme environments such as wild temperature fluctuations, galactic cosmic rays, destructive dust, meteoroid impacts (direct or indirect), vibrations, and solar particle events, will place on long-term deep space habitats represents one of the greatest challenges in this endeavor. This context necessitates that we establish the know-how and technologies to build habitat systems that are resilient. Resilience is not simply robustness or redundancy: it is a system property that accounts for both anticipated and unanticipated disruptions via the design choices and maintenance processes, and adapts to them in operation. We currently lack the frameworks and technologies needed to achieve a high level of resilience in a habitat system. The Resilient Extra Terrestrial Habitats Institute (RETHi) has the mission of leveraging existing novel technologies to provide situational awareness and autonomy to enable the design of habitats that are able to adapt, absorb and rapidly recover from expected and unexpected disruptions. We are establishing both fully virtual and coupled physical-virtual simulation capabilities that will enable us to explore a wide range of potential deep space Smart Hab configurations and operating modes.
This paper presents a general purpose, plug-and-play simulation platform for the use of future aviation stakeholders, such as urban airspace planners, air vehicle operators, ground operation managers, air traffic controllers and aviation researchers. The presented simulator platform is envisioned to serve as a toolkit to visualize, evaluate, and configure future advanced air mobility (AAM) operations. Highlighting features of this toolkit include a modular architecture that allows multiple smart unmanned aerial systems (UASs) to remotely connect to the simulation server and participate in decentralized decision-making scenario simulations. As an example of the decentralized decision-making scenario, an inter-agent negotiation-based conflict resolution use case is considered in this paper, where the UASs leverage the on-board/on-the-edge artificial intelligence (AI) capability to continually build situational awareness, and use this information to predict future conflicts and resolve them through machine-to-machine negotiation. As such operations are non-existent at scale currently, the presented simulation platform offers a viable and cost-effective alternative for assessing the efficacy of AAM research outcomes and challenges in future shared airspace usage. The simulation platform allows plug-n-play connectivity with AI and non-AI compute modules representing individual UAS’s flight control. Each module can interact with the simulation platform independently to communicate current and desired future states, situational awareness, and conflict resolution utilization costs for inter-agent negotiation. The simulation environment orchestrates realistic operational scenarios with spatiotemporal details, dynamic events, tactical conflict-resolution methods, interfaces for customizing air traffic control parameters, and information exchange uncertainties. In the future, this can serve as a community focused cloud simulation platform, incorporating multi-stakeholder airspace constraints from regulatory, government, city, and local agencies.
This paper presents a general purpose, plug-and-play simulation platform for the use of future aviation stakeholders, such as urban airspace planners, air vehicle operators, ground operation managers, air traffic controllers and aviation researchers. The presented simulator platform is envisioned to serve as a toolkit to visualize, evaluate, and configure future advanced air mobility (AAM) operations. Highlighting features of this toolkit include a modular architecture that allows multiple smart unmanned aerial systems (UASs) to remotely connect to the simulation server and participate in decentralized decision-making scenario simulations. As an example of the decentralized decision-making scenario, an inter-agent negotiation-based conflict resolution use case is considered in this paper, where the UASs leverage the on-board/on-the-edge artificial intelligence (AI) capability to continually build situational awareness, and use this information to predict future conflicts and resolve them through machine-to-machine negotiation. As such operations are non-existent at scale currently, the presented simulation platform offers a viable and cost-effective alternative for assessing the efficacy of AAM research outcomes and challenges in future shared airspace usage. The simulation platform allows plug-n-play connectivity with AI and non-AI compute modules representing individual UAS’s flight control. Each module can interact with the simulation platform independently to communicate current and desired future states, situational awareness, and conflict resolution utilization costs for inter-agent negotiation. The simulation environment orchestrates realistic operational scenarios with spatiotemporal details, dynamic events, tactical conflict-resolution methods, interfaces for customizing air traffic control parameters, and information exchange uncertainties. In the future, this can serve as a community focused cloud simulation platform, incorporating multi-stakeholder airspace constraints from regulatory, government, city, and local agencies.
Emerging concepts for Advanced Air Mobility (AAM) envisions responsive air transportation capabilities that can safely move people and cargo between places - including local, regional, intraregional, and urban - previously not served or underserved by aviation. Expanding traditional aviation services to these environments, particularly when autonomous operations are involved, face a number of challenges that will require advances beyond the state-of-the-art techniques for airborne sensing and perception, which goes beyond the limits of scalability and applicability of the current air transportation system infrastructure. Additionally, the emerging field of distributed sensing and ‘smart spaces’– where sensing, processing, communication, and actuation are embedded in the environment in which agents are acting and can be exploited by the agents through real-time wireless communication – may provide realistic near-time solutions to limitations imposed by traditional aviation techniques. This paper outlines the needs, challenges, and opportunities for advanced perception and distributed sensing techniques to meet the emerging needs for advanced AAM operations in the national airspace. A general roadmap for research, development, and maturation of perception and distributed sensing (P&DS) technologies is proposed to guide future development through verification, validation, certification into airborne systems. Through this analysis of challenges and the proposed roadmap for technology maturation, we hope to accelerate transition of advanced research techniques from other disciplines into this domain.
Degradation of Earth’s inland water resources due to anthropogenic perturbations and climate anomalies at both local and global scales continues to place human health at substantial risk. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This presentation will be discussing the progress made developing SWIPE: Spectral Water Inversion Processor and Emulator. SWIPE is a platform for advanced modeling of coastal and inland aquatic habitats. The goal is create a comprehensive and cohesive system to leverage recent advancements in computation and machine learning to develop a synthetic training ground for sensitivity studies and algorithm development. The four principal facets of SWIPE include: 1. Advanced two-layer coated sphere bio-optical modeling and GPU radiative transfer modeling, 2. Big Data involving massive synthetic spectral libraries of optical properties of various global aquatic particles, surface reflectance, and top-of-atmosphere reflectance, all at hyperspectral resolution leveraging high-end computing systems at NASA Ames Research Center, 3. Deep Learning for algorithm development for water quality inversion of concentrations of common biogeophysical variables as well as optics, full uncertainty characterization by water type, and forward emulation, and lastly, 4. Image Processing for application of developed retrieval algorithms for both hyperspectral and multispectral sensors with experimental corrections for global adjacency, noise, sunglint, and benthic reflectance. This presentation will demonstrate the Equivalent Algal Populations (EAP) two-layer coated sphere scattering model which has been used develop spectral libraries of hyperspectral inherent optical properties of roughly 80 species of phytoplankton, covering 15 different classes and nine taxonomic functional types. The EAP model was also used to derive spectral properties of 10 different non-algal particle functional types. Examples of how the SMART-G (Speed-up Monte-carlo Advanced Radiative Transfer using GPU) radiative transfer code is used to model optically complex aquatic signals will be presented and discussed in the context of creating a massive synthetic database which can leverage the full power of next generation machine learning techniques and high end computing for water quality inversion. We will discuss our active investigation in things like appropriate model architectures, dimensionality reduction techniques such as PCA and autoencoders, uncertainty quantification and abstaining, and which variables actually benefit most from hyperspectral information versus multispectral resolution. We are also curious about questions relating to cost/benefit analysis in terms of computation resources, neural network complexity, and data volumes. Answers to these questions will hopefully elaborate on cost efficiency for potential future sensor design considerations.
The emergence and maturation of wireless communication technologies continue to transform the aviation industry and are enabling new solutions to challenges faced by NASA’s Advanced Air Mobility (AAM) initiative. AAM is leading towards high-density autonomous aircraft operations in areas underserved by traditional aviation, such as over densely populated urban centers. In this paper, we build on concepts from Smart Spaces - where sensing, processing, and communication are embedded in an environment, and agents are operating within the space can exploit these capabilities in real-time through collaborative information sharing networks. Building from these concepts, we propose a framework to enable a dynamic, topologically-adaptive, and distributed estimation system for man-rated aviation to address challenges faced by autonomous AAM operations. This paper presents the initial concept of operations and system design for this framework, presents a mathematical formulation for abstraction of the problem, identifies requirements and constraints for operation, and presents algorithmic constructs and mathematical formalisms to demonstrate operation. The proposed framework will be evaluated on a regional AAM flight scenario and will focus on two initial applications: (1) GPS-free navigation supporting precision approach and landing (PAL), and (2) surveillance and conformance monitoring of aircraft in vertiport airspaces. Such approaches show promise in addressing gaps in current technologies needed to enable future AAM concepts, while promising greater capabilities, performance, robustness, and safety over current aviation systems and operations.
This paper proposes a risk-aware framework for Safe Multi-Agent Planning (SafeMAP) that unifies disparate models for multi-agent systems in a Markovian process that allows for simultaneous system health monitoring, decision making under uncertainty, and multi-agent system collaboration. As operations beyond low earth orbit mature, there is an increased need for autonomous cyber-physical systems with onboard decision making capabilities. Multi-agent cyber-physical systems in particular offer the potential of increased efficiency, resiliency, and mission capabilities for future applications such as multi-rover terrain operations, distributed satellite operations, and management of smart lunar habitats. SafeMAP utilizes physics-based models of each agent and the relevant components, probability models of the environment and component operational states, and reward models for mission-specific objectives such as scientific task completion or resource consumption. The output of SafeMAP is a set of mission plans that satisfy the mission objective under specified risk/reward constraints. A readable interpretation of each of these generated mission plans is provided as an additional output. SafeMAP has been demonstrated on a simulated case study involving a four-rover system performing surface mapping operations and science tasks. Results of this paper demonstrate SafeMAP’s ability to generate explainable mission plans that satisfy the mission objective while minimizing risk under nominal and off-nominal conditions.