Field Testing of Utility Robots for Lunar Surface Operations

Since 2004, NASA has been working to return to the Moon. In contrast to the Apollo missions, two key objectives of the current exploration program is to establish significant infrastructure and an outpost. Achieving these objectives will enable long-duration stays and long-distance exploration of the Moon. To do this, robotic systems will be needed to perform tasks which cannot, or should not, be performed by crew alone. In this paper, we summarize our work to develop "utility robots" for lunar surface operations, present results and lessons learned from field testing, and discuss directions for future research

Spacecraft Mission Agent for Autonomous Robust Task Execution

Autonomy in space systems can drastically reduce the workload of ground crews for satellite missions, especially for clusters of satellites. Additionally, autonomy can increase the efficiency of missions by maximizing the utilization of resources and rapidly handling any issues that arise without having to wait for instructions from the ground. This research presents an agent-based, task-execution approach to onboard spacecraft autonomy. Instead of the traditional approach requiring onboard planning and scheduling, this method uses a combination of constraint and priority parameters associated with every task to ensure robust task execution with behavior as intended. Using this method, tasks will only run under safe conditions (e.g. no conflict with any running tasks), which enables conflicting tasks to be scheduled closer together or even overlapping for lower-priority tasks. This approach manages the execution of tasks on the timescale of seconds, enabling conflicting tasks to run sequentially, thereby increasing productivity if earlier tasks finish ahead of schedule. This framework leverages the NASA-developed, open-source projects cFE and PLEXIL and was tested on development boards comparable to flight hardware

Robotic Planetary Drill Tests

Several proposed or planned planetary science missions to Mars and other Solar System bodies over the next decade require subsurface access by drilling. This paper discusses the problems of remote robotic drilling, an automation and control architecture based loosely on observed human behaviors in drilling on Earth, and an overview of robotic drilling field test results using this architecture since 2005. Both rotary-drag and rotary-percussive drills are targeted. A hybrid diagnostic approach incorporates heuristics, model-based reasoning and vibration monitoring with neural nets. Ongoing work leads to flight-ready drilling software

Rewriting Modulo SMT and Open System Analysis

This paper proposes rewriting modulo SMT, a new technique that combines the power of SMT solving, rewriting modulo theories, and model checking. Rewriting modulo SMT is ideally suited to model and analyze reachability properties of infinite-state open systems, i.e., systems that interact with a nondeterministic environment. Such systems exhibit both internal nondeterminism, which is proper to the system, and external nondeterminism, which is due to the environment. In a reflective formalism, such as rewriting logic, rewriting modulo SMT can be reduced to standard rewriting. Hence, rewriting modulo SMT naturally extends rewriting-based reachability analysis techniques, which are available for closed systems, to open systems. The proposed technique is illustrated with the formal analysis of: (i) a real-time system that is beyond the scope of timed-automata methods and (ii) automatic detection of reachability violations in a synchronous language developed to support autonomous spacecraft operations.NSF Grant CNS 13-19109 and NASA Research Cooperative Agreement No. NNL09AA00AOpe

Executable semantics for PLEXIL : simulating a task-scheduling language in Haskell

An interdisciplinary study into the theory of design decisions has yielded a model for tracking design changes in hardware/software systems, but it still needs to be applied to a larger system to test its efficiency at tracking important data. This thesis creates an implementation of PLEXIL, a language in development at NASA for controlling various hardware systems, as a testbed for applying the model of design decisions. This PLEXIL implementation is embedded in the functional language Haskell to take advantage of its static typing and lazy evaluation, and it accurately follows the semantics defined by NASA. The external world representation in this thesis improves upon NASA’s current simulator through the definition of new data types for more dynamic runtime behavior

An Overview of Distributed Spacecraft Autonomy at NASA Ames

Autonomous decision-making significantly increases mission effectiveness by mitigating the effects of communication constraints, like latency and bandwidth, and mission complexity on multi-spacecraft operations. To advance the state of the art in autonomous Distributed Space Systems (DSS), the Distributed Spacecraft Autonomy (DSA) team at NASA\u27s Ames Research Center is developing within five relevant technical areas: distributed resource and task management, reactive operations, system modeling and simulation, human-swarm interaction, and ad hoc network communications. DSA is maturing these technologies - critical for future large autonomous DSS - from concept to launch via simulation studies and orbital deployments. A 100-node heterogenous Processor-in-the-Loop (PiL) testbed aids distributed autonomy capability development and verification of multi-spacecraft missions. The DSA software payload deployed to the D-Orbit SCV-004 spacecraft demonstrates multi-agent reconfigurability and reliability as part of an ESA-sponsored in-orbit technology demonstration. Finally, DSA\u27s primary flight mission showcases collaborative resource allocation for multipoint science data collection with four small spacecraft as a payload on NASA\u27s Starling 1.0 satellites

Survey of Command Execution Systems for NASA Spacecraft and Robots

NASA spacecraft and robots operate at long distances from Earth Command sequences generated manually, or by automated planners on Earth, must eventually be executed autonomously onboard the spacecraft or robot. Software systems that execute commands onboard are known variously as execution systems, virtual machines, or sequence engines. Every robotic system requires some sort of execution system, but the level of autonomy and type of control they are designed for varies greatly. This paper presents a survey of execution systems with a focus on systems relevant to NASA missions

A Robust Compositional Architecture for Autonomous Systems

Space exploration applications can benefit greatly from autonomous systems. Great distances, limited communications and high costs make direct operations impossible while mandating operations reliability and efficiency beyond what traditional commanding can provide. Autonomous systems can improve reliability and enhance spacecraft capability significantly. However, there is reluctance to utilizing autonomous systems. In part this is due to general hesitation about new technologies, but a more tangible concern is that of reliability of predictability of autonomous software. In this paper, we describe ongoing work aimed at increasing robustness and predictability of autonomous software, with the ultimate goal of building trust in such systems. The work combines state-of-the-art technologies and capabilities in autonomous systems with advanced validation and synthesis techniques. The focus of this paper is on the autonomous system architecture that has been defined, and on how it enables the application of validation techniques for resulting autonomous systems

Characterizing and evaluating autonomous controllers

Premio Extraordinario de Doctorado de la UAH en el año académico 2016-2017La autonomía en robótica por medio de técnicas de Inteligencia Artificial, particularmente mediante el empleo sistemas de Planning & Scheduling (P&S), presenta un amplio campo de investigación con gran interés en aplicaciones como la robótica de exploración para entornos hostiles o difícilmente accesibles para los humanos. Sin embargo, las pruebas experimentales realizadas en los artículos de divulgación científica sobre controladores autónomos generalmente no están correctamente realizadas, ya que se carece de una metodología de estudio común. En este sentido se hace complicado comparar los nuevos sistemas con los trabajos previos, práctica habitual en otras disciplinas. Por ello, en esta tesis se propone un entorno de trabajo llamado On-Ground Autonomy Test Environment (OGATE) para permitir la evaluación de controladores autónomos. Este desarrollo consta de una metodología para estructurar la fase experimental, así como de un conjunto de métricas independientes tanto del dominio como del campo de aplicación del sistema robótico. La unión de estos elementos, mediante un software que automatiza el proceso experimental, permite obtener evaluaciones reproducibles y objetivas sobre los controladores autónomos bajo estudio. Para demostrar la efectividad del entorno de trabajo, se han utilizado dos controladores autónomos basados en diferentes paradigmas para P&S. Primero se ha utilizado el Goal Oriented Autonomous Controller (GOAC), desarrollado bajo contrato de la Agencia Espacial Europea. Segundo, durante esta tesis se ha implementado la Model-Based Architecture (MoBAr). MoBAr está diseñado con el objetivo de probar diferentes planificadores basados en el Planning Domain Definition Language (PDDL) para conseguir autonomía a bordo. En este sentido, en la tesis también se introduce un nuevo planificador llamado Unified Path Planning and Task Planning Architecture (UP2TA). Dicho sistema integra un planificador general basado en PDDL y algoritmos de planificación de rutas con el objetivo de generar planes más seguros y eficientes para robots de exploración. Referente a la planificación de rutas, en la tesis se incluye la definición de dos nuevos algoritmos enfocados en la movilidad de los robots de exploración: S-Theta* y 3D Accurate Navigation Algorithm (3Dana). S-Theta* permite obtener rutas con un menor número de cambios de dirección que algoritmos previos, mientras que 3Dana genera rutas más seguras y restringidas en función de la pendiente del entorno, empleando para ello Modelos Digitales de Terreno (MDT) y mapas de costes trasversales. Partiendo de GOAC y MoBAr, se ha empleado OGATE para evaluar ambos controladores, siendo posible caracterizar aspectos relevantes de la integración entre Planning & Execution (P&E) difícilmente accesibles mediante otros enfoques. Además, los resultados obtenidos son objetivos y reproducibles, permitiendo realizar comparaciones entre controladores autónomos con diferentes tecnologías y/o paradigmas de P&S