OceanWATERS Ocean Worlds Autonomy Testbed for Exploration Research and Simulation
Autonomy Testbed for Ocean World Surface Missions
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Autonomy Testbed for Ocean World Surface Missions
The Mission Simulation Facility (MSF) supports research in autonomy technology for planetary exploration vehicles. Using HLA (High Level Architecture) across distributed computers, the MSF connects users autonomy algorithms with provided or third-party simulations of robotic vehicles and planetary surface environments, including onboard components and scientific instruments. Simulation fidelity is variable to meet changing needs as autonomy technology advances in Technical Readiness Level (TRL). A virtual robot operating in a virtual environment offers numerous advantages over actual hardware, including availability, simplicity, and risk mitigation. The MSF is in use by researchers at NASA Ames Research Center (ARC) and has demonstrated basic functionality. Continuing work will support the needs of a broader user base.
Perception plays a key role in autonomous and semi-autonomous planetary exploration vehicles. For instance, landers can use computer vision techniques for identifying safe landing locations, aerial vehicles use cameras as navigation sensors, and planetary rovers use them for localization and hazard detection. Engineering simulations of such systems requires the accurate modeling of perception and vision sensors for simulating autonomy scenarios. In addition, the modeling of sensors for landers, aerial and ground vehicles requires the ability to handle large and high-resolution terrains, the accurate modeling of illumination, hi-fidelity rendering via ray/path tracing and the inclusion of sensor characteristics. Vision sensor models strive to simulate sensor reality by using physics principles to model the interaction of light and objects. Furthermore, high frame rate performance is highly desirable for in-the-loop simulations involving vehicle dynamics and control software. In this paper we describe a new sensor modeling capability called Inter-planetary Rendering for Imaging and Sensors (IRIS) that meets these requirements for the real-time and high-fidelity simulation of vision sensors for planetary aerospace and robotics applications.
Autonomy is a key factor in remote robotic exploration and there is significant activity addressing the application of autonomy to remote robots. It has become increasingly important to have simulation tools available to test the autonomy algorithms. While indus1;rial robotics benefits from a variety of high quality simulation tools, researchers developing autonomous software are still dependent primarily on block-world simulations. The Mission Simulation Facility I(MSF) project addresses this shortcoming with a simulation toolkit that will enable developers of autonomous control systems to test their system s performance against a set of integrated, standardized simulations of NASA mission scenarios. MSF provides a distributed architecture that connects the autonomous system to a set of simulated components replacing the robot hardware and its environment.
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Progress on a project for the development of a high-autonomy intelligent command and control architecture for process plants used to produce oxygen from local planetary resources is reported. A distributed command and control architecture is being developed and implemented so that an oxygen production plant, or other equipment, can be reliably commanded and controlled over an extended time period in a high-autonomy mode with high-level task-oriented teleoperation from one or several remote locations. During the reporting period, progress was made at all levels of the architecture. At the remote site, several remote observers can now participate in monitoring the plant. At the local site, a command and control center was introduced for increased flexibility, reliability, and robustness. The local control architecture was enhanced to control multiple tubes in parallel, and was refined for increased robustness. The simulation model was enhanced to full dynamics descriptions.
We report here on a survey of a lava tube cave by a rover that is instrumented for astrobiology missions. The NASA Ames testbed rover, CaveR, was deployed in Valentine Cave in Lava Beds National Monument (N. CA, USA) during August of 2018. The rover carried an instrument package consisting of Near Infrared and Visible Spectrometer System (NIRVSS) a point spectrometer operating in 1590-3400 nm range, sensitive to H2O and -OH bearing minerals, pyroxenes, and carbonates (Roush, et al 2018); the bore sighted Drill Operations Camera (DOC), a monochrome imager illuminated by LEDs at 410, 540, 640, 740, 905 and 940 nm; a Realsense ™ depth sensor system for 3D model generation; and a high resolution DSLR stereo camera. The payload was mounted on a tiltable instrument platform attached to the left side of the rover. The rover was driven manually in the cave by field operators, following instructions from a remote science operations team, and simulating a mission concept with science-guided autonomy. A simulated mission took place for 3 days with a team of 3 scientists selecting targets and interpreting data from the payload. To begin the mission, the rover drove along one wall of the cave imaging continuously with the Realsense in 20 m cave segments, three total. At the start of each day, the images from a 20m segment and a panorama stitched from them were provided to the science team to examine. The science team used these data to prioritize specific points along the cave wall for the collection of NIRVSS, DOC, and DSLR data. The objective of the data collection was to identify and study putative biological and mineralogical features in the cave. The data were delivered in xGDS, a customized mapping, planning, and data base management software developed at NASA Ames (Lee, et al 2013). Once the targets for further observations were selected, a plan for collecting the observations (positions in the cave and pointing for each requested observation) was constructed using xGDS and delivered to a rover team to execute the science data collection plan. Acquired data were delivered back to the science team for analysis. Preliminary results from the experiment illustrate the utility of the system (rover plus payload) to study the cave geology and mineralogy and its potential for identifying biomineral features.
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The Human Autonomy Teaming Laboratory at NASA Ames Research Center (ARC) is planning to conduct an experiment investigating system-level effects operational vertiport disruptions on Urban Air Mobility (UAM) traffic in a simulated vertiport network in Denver, CO. The simulation will involve two parties, one at NASA ARC and another at NASA Langley Research Center (LaRC), each playing the role of a UAM Operator. This study, referred to as the "High Complexity Sim," features a 3-by-2-by-2 mixed subjects design. The variables to be manipulated are the scale of the disruption (Disruption, three levels: Small, Medium, and Large, affecting 1, 2, and 3+, vertiports, respectively), m:N ratio (Ratio, two levels: 2:6 and 2:12), and the assumption of UAM corridors (Corridors, two levels: With and Without). Local and system-wide capacity (i.e., throughput), efficiency (scale and number of flight delays, filed-vs-flown flight times), and human performance metrics (workload, situation awareness, heart rate/heart rate variability, eye gaze/fixation and saccades, stress) will be measured. This presentation details the experimental design and planned timeline for the study.
This paper is concerned with the physics-based simulation of light tracked vehicles operating on rough deformable terrain. The focus is on small autonomous vehicles, which weigh less than 100 lb and move on deformable and rough terrain that is feature rich and no longer representable using a continuum approach. A scenario of interest is, for instance, the simulation of a reconnaissance mission for a high mobility lightweight robot where objects such as a boulder or a ditch that could otherwise be considered small for a truck or tank, become major obstacles that can impede the mobility of the light autonomous vehicle and negatively impact the success of its mission. Analyzing and gauging the mobility and performance of these light vehicles is accomplished through a modeling and simulation capability called Chrono::Engine. Chrono::Engine relies on parallel execution on Graphics Processing Unit (GPU) cards.
The Airspace Operations Laboratory at NASA Ames conducts research to provide a better understanding of roles, responsibilities, and requirements for human operators and automation in future air traffic management (ATM) systems. The research encompasses developing, evaluating, and integrating operational concepts and technologies for near-, mid-, and far-term air traffic operations. Current research threads include efficient arrival operations, function allocation in separation assurance and efficient airspace and trajectory management. The AOL has developed powerful air traffic simulation capabilities, most notably the Multi Aircraft Control System (MACS) that is used for many air traffic control simulations at NASA and its partners in government, academia and industry. Several additional NASA technologies have been integrated with the AOL's primary simulation capabilities where appropriate. Using this environment, large and small-scale system-level evaluations can be conducted to help make near-term improvements and transition NASA technologies to the FAA, such as the technologies developed under NASA's Air Traffic Management Demonstration-1 (ATD-1). The AOL's rapid prototyping and flexible simulation capabilities have proven a highly effective environment to progress the initiation of trajectory-based operations and support the mid-term implementation of NextGen. Fundamental questions about accuracy requirements have been investigated as well as realworld problems on how to improve operations in some of the most complex airspaces in the US. This includes using advanced trajectory-based operations and prototype tools for coordinating arrivals to converging runways at Newark airport and coordinating departures and arrivals in the San Francisco and the New York metro areas. Looking beyond NextGen, the AOL has started exploring hybrid human/automation control strategies as well as highly autonomous operations in the air traffic control domain. Initial results indicate improved capacity, low operator workload, good situation awareness and acceptability for controllers teaming with autonomous air traffic systems. While much research and development needs to be conducted to make such concepts a reality, these approaches have the potential to truly transform the airspace system towards increased mobility, safe and efficient growth in global operations and enabling many of the new vehicles and operations that are expected over the next decades. This paper describes how the AOL currently contributes to the ongoing air transportation transformation.
The LRTF provides an accessible planetary analog surface environment for surface mobility testing, autonomous roving operations, developing advanced navigation techniques and operations development.
Ocean Worlds Autonomy Testbed for Exploration Research and Simulation (OceanWATERS) is an open-source simulator for developing onboard autonomy software for robotic exploration of ocean worlds, such as Europa, Enceladus, and Titan, built on the Robot Operating System (ROS) and Gazebo simulation environment. Inevitable ground communication delays increase demand for a high degree of autonomy during excavation, collection and transfer of samples to scientific instruments for in-situ analysis. This paper offers a detailed discussion of the robotic arm design and operation for such autonomous surface exploration, taking as reference the Europa Lander mission. The lander arm, which is designed primarily to acquire icy surface and subsurface samples within the arm’s workspace, is a 6-degree-of-freedom manipulator with two end effectors: a sample excavation tool and a trenching end-effector. The robotic arm’s modes and operations can be summarized as follows: stowed arm, intended as the lander arm default configuration characterized by zero-power consumption; un-stowed arm, target arm configuration after its first deployment; selection and deployment of the end-effector to use next; guarded move, to detect ground level at the desired trenching location; drill ice using the grinder; dig trench at a particular location using the scoop; deliver sample to the sample transfer dock; discard redundant samples. The motion planning tool used for the lander arm is MoveIt, a ROS package. MoveIt uses sampling-based planning and collision checking libraries to determine safe paths. The Rapidly Exploring Random Trees* (RRT*) has been chosen as default planning algorithm as it provides optimal plans with an exponential speed and is guaranteed to find a solution, if feasible solutions exist. Furthermore, this work quantifies and discusses the energy requirements for excavating and collecting samples. In OceanWATERS, force feedback from the terrain, which influences the arm dynamics, is modelled using a discrete element method (DEM) simulation. The DEM and Gazebo software run in parallel and communicate through a co-simulation plugin. This paper presents an analysis and comparison of three DEM open source software (YADE, ESyS-Particle, Project Chrono) for implementation in OceanWATERS and motivates the choice of YADE as most suitable candidate.
As part of its research, NASA investigates concepts, aircraft, and operations related to Advanced Air Mobility (AAM). One of the most challenging scenarios for AAM will be enabling safe routine access near densely populated urban centers. AAM flight operations over a regional area require a moderately high-fidelity simulation capability to develop and evaluate autonomy technologies in the urban environment. This paper aims to describe a system to simulate flight operations around regions such as the San Francisco-Oakland Bay area at a moderately-high scale (tens to hundreds of flights) that incorporates detailed vehicle models and control necessary to support research in airborne autonomy. The flight vehicle utilizes NASA AAM concept vehicle dynamics integrated with a custom flight management system and flight control system to simulate all flight phases accurately. The simulation incorporates a detailed simulated urban environment and includes glass cockpit displays to monitor aircraft operations. Simulation models integrate to simulate air and ground-based sensors, such as Radar and LiDAR. As a commercially available rendering engine, X-Plane 11 is used as the renderer to simulate vision-based sensors (such as onboard and ground-based cameras) with a detailed graphical model of a city at different times of the day and weather conditions. This paper presents the simulation and software architecture used for simulating AAM traffic over this urban region. This system enables the evaluation of NASA research concepts in autonomy for urban AAM operations on the path toward flight test evaluation.
As part of its research, NASA investigates concepts, aircraft, and operations related to Advanced Air Mobility (AAM). One of the most challenging scenarios for AAM will be enabling safe routine access near densely populated urban centers. AAM flight operations over a regional area require a moderately high-fidelity simulation capability to develop and evaluate autonomy technologies in the urban environment. This paper aims to describe a system to simulate flight operations around regions such as the San Francisco-Oakland Bay area at a moderately-high scale (tens to hundreds of flights) that incorporates detailed vehicle models and control necessary to support research in airborne autonomy. The flight vehicle utilizes NASA AAM concept vehicle dynamics integrated with a custom flight management system and flight control system to simulate all flight phases accurately. The simulation incorporates a detailed simulated urban environment and includes glass cockpit displays to monitor aircraft operations. Simulation models integrate to simulate air and ground-based sensors, such as Radar and LiDAR. As a commercially available rendering engine, X-Plane 11 is used as the renderer to simulate vision-based sensors (such as onboard and ground-based cameras) with a detailed graphical model of a city at different times of the day and weather conditions. This paper presents the simulation and software architecture used for simulating AAM traffic over this urban region. This system enables the evaluation of NASA research concepts in autonomy for urban AAM operations on the path toward flight test evaluation.
The Engineering and Mission Operations Directorates at NASA Johnson Space Center are combining laboratories and expertise to establish the Human Centered Autonomous and Assistant Systems Testbed for Exploration Operations. This is a testbed for human centered design, development and evaluation of intelligent autonomous and assistant systems that will be needed for human exploration and development of space. This project will improve human-centered analysis, design and evaluation methods for developing intelligent software. This software will support human-machine cognitive and collaborative activities in future interplanetary work environments where distributed computer and human agents cooperate. We are developing and evaluating prototype intelligent systems for distributed multi-agent mixed-initiative operations. The primary target domain is control of life support systems in a planetary base. Technical approaches will be evaluated for use during extended manned tests in the target domain, the Bioregenerative Advanced Life Support Systems Test Complex (BIO-Plex). A spinoff target domain is the International Space Station (ISS) Mission Control Center (MCC). Prodl}cts of this project include human-centered intelligent software technology, innovative human interface designs, and human-centered software development processes, methods and products. The testbed uses adjustable autonomy software and life support systems simulation models from the Adjustable Autonomy Testbed, to represent operations on the remote planet. Ground operations prototypes and concepts will be evaluated in the Exploration Planning and Operations Center (ExPOC) and Jupiter Facility.
NASA is conducting investigations into Advanced Air Mobility (AAM) concepts, aircraft, and operations. One of the most challenging scenarios for AAM will be enabling safe routine access into densely populated urban centers. To address challenges in the urban environment, a moderately high-fidelity simulation capability is needed to investigate AAM flight operations over a regional area for the development and evaluation of autonomy technologies. This paper describes a system to simulate flight operations around regions such as the San Francisco-Oakland Bay area at a moderately-high scale (10's-100's of flights) that incorporates detailed vehicle models and control necessary to support research in airborne autonomy. The flight vehicle utilizes NASA AAM concept vehicle dynamics integrated with a custom flight management system and flight control system to accurately simulate all phases of flight. Glass cockpit displays have been developed for monitoring aircraft operation over a detailed simulated urban environment. Simulation models have been integrated to simulate air and ground-based sensors, such as radar and LIDAR. The commercial X-Plane software package is used as a rendering engine to mimic vision-based sensors (such as onboard and ground-based cameras) at various times of day and in various weather conditions over a relatively detailed graphical model of the city. The paper presents a detailed illustration of the simulation and software architecture used for traffic over this urban region. This system is enabling the evaluation of NASA research concepts in autonomy for urban AAM operations on the path toward aircraft flight test evaluation.
As aircraft become able to autonomously respond to a range of situations with performance surpassing human operators, we are compelled to look for new methods that help understand their use and guide the design of new, more effective forms of automation and interaction. The "H-mode" is one such method and is based on the metaphor of a well-trained horse. The concept allows the pilot to manage a broad range of control automation functionality, from augmented manual control to FMS-like coupling and automation initiated actions, using a common interface system and easily learned set of interaction skills. The interface leverages familiar manual control interfaces (e.g., the control stick) and flight displays through the addition of contextually dependent haptic-multimodal elements. The concept is relevant to manned and remotely piloted vehicles. This paper provides an overview of the H-mode concept followed by a presentation of the results from a recent evaluation conducted in a motion-based simulator. The evaluation focused on assessing the overall usability and flying qualities of the concept with an emphasis on the effects of turbulence and cockpit motion. Because the H-mode results in interactions between traditional flying qualities and management of higher-level flight path automation, these effects are of particular interest. The results indicate that the concept may provide a useful complement or replacement to conventional interfaces, and retains the usefulness in the presence of turbulence and motion.