SUPERball v2: A Tensegrity Rover for Planetary Exploration
A presentation of our SUPERball v2 robotic prototype as an invited speaker for a tensegrity based workshop at the International Conference of Robotics and Automation.
Bruce, Jonathan E.↗
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Adding Fresh Produce to the Space Diet
NASA’s Human Research Program has identified a Risk of inadequate nutrition and food stability as a risk of high concern for future long duration exploration missions such as a mission to Mars. One possible solution to help reduce this risk is the addition of in situ produced supplemental fresh produce to supplement and enhance the astronaut diet. A number of ground and ISS-based studies are being conducted which are helping to reduce this risk by filling the gaps in knowledge and technology associated with space crop production. These include studies with the Veggie vegetable production system on the International Space Station. Key focus areas include water and nutrient delivery, food safety and the crop microbiome, crop selection and testing, automation and robotics, radiation impacts, seed storage and handling, and scalability for different concepts and architectures. All of these areas have significant challenges that need to be addressed prior to successful integration of crop production into the crew diet. Data from these studies is feeding the development of new technologies and systems for space crop production. This research was funded by NASA Space Biology and NASA’s Human Research Program.
Gioia Massa↗
Adding Fresh Produce to the Space Diet
NASA’s Human Research Program has identified a Risk of inadequate nutrition and food stability as a risk of high concern for future long duration exploration missions such as a mission to Mars. One possible solution to help reduce this risk is the addition of in situ produced supplemental fresh produce to supplement and enhance the astronaut diet. A number of ground and ISS-based studies are being conducted which are helping to reduce this risk by filling the gaps in knowledge and technology associated with space crop production. These include studies with the Veggie vegetable production system on the International Space Station. Key focus areas include water and nutrient delivery, food safety and the crop microbiome, crop selection and testing, automation and robotics, radiation impacts, seed storage and handling, and scalability for different concepts and architectures. All of these areas have significant challenges that need to be addressed prior to successful integration of crop production into the crew diet. Data from these studies is feeding the development of new technologies and systems for space crop production. This research was funded by NASA Space Biology and NASA’s Human Research Program.
Space Crop Production↗
What are Needed Innovations for Space Crop Production That May Also Benefit Vertical Farming?
I am speaking about the space crop production gaps and challenges we face and how we need innovations in the area of plant health monitoring, food safety contamination monitoring, CEA IPM, understanding of and manipulation of the microbiome/ecosystem, sustainability needs, novel sensors needed, automation and robotics, and also new crops that could be developed and ways that our needs parallel those of the vertical farming industry. (Note: This presentation is in video mp4 format is only viewable by download)
Gioia Massa↗
Parametric Mechanism Design Through Numerical Optimization and Physics Simulation
Design-Build-Test approaches for developing spaceflight hardware are prohibitively time and cost intensive and often lead to suboptimal mechanism designs. Approaches that couple machine learning and high-fidelity physics simulation could eliminate the need for hardware prototyping and dramatically accelerate the engineering design cycle, ultimately reducing cost. This work presents a modular NASA-developed toolchain to optimize hardware mechanisms in a virtual environment using numerical optimization and multi-body physics simulation. The toolchain enables multi-objective optimization, generates parametric CAD files that can be further post-processed by an end user, and can be expanded to optimize full systems and non-mechanical parameters such as feedback control variables. We demonstrate the toolchain through an independently verifiable design problem that optimizes wheel radius to achieve a desired linear velocity in a rigid-body physics environment when the wheel rotates at a constant angular speed, and then post-process the parametric CAD file of the optimal design generated by the tool before ultimately manufacturing it via 3D printing. We end with a discussion of how the toolchain can incorporate other analysis tools, including finite element analysis, computational fluid dynamics, and granular media simulations.
Optimization↗
An Optimization-Based Toolchain for Parametric Mechanism Design
Design-Build-Test approaches for developing spaceflight hardware are prohibitively time and cost intensive and often lead to suboptimal mechanism designs. Approaches that couple machine learning and high-fidelity physics simulation could eliminate the need for hardware prototyping and dramatically accelerate the engineering design cycle, ultimately reducing cost. This work presents a modular NASA-developed toolchain to optimize hardware mechanisms in a virtual environment using numerical optimization and multi-body physics simulation. The toolchain enables multi-objective optimization, generates parametric CAD files that can be further post-processed by an end user, and can be expanded to optimize full systems and non-mechanical parameters such as feedback control variables. We demonstrate the toolchain through an independently verifiable design problem that optimizes wheel radius to achieve a desired linear velocity in a rigid-body physics environment when the wheel rotates at a constant angular speed, and then post-process the parametric CAD file of the optimal design generated by the tool before ultimately manufacturing it via 3D printing. We end with a discussion of how the toolchain can incorporate other analysis tools, including finite element analysis, computational fluid dynamics, and granular media simulations.
Optimization↗
Parametric Optimization of Rigid Wheels for Planetary Surface Mobility Applications
Design-Build-Test approaches for spaceflight hardware are time and cost intensive, which can result in suboptimal mechanism designs. Optimization-based approaches that utilize high-fidelity models and physics simulation could overcome these limitations while simultaneously speeding up the mechanical design process and reducing cost. In this work, we present a toolchain that enables the multi-objective optimization of rigid rover wheels for planetary surface mobility applications. The toolchain uses Chrono’s Continuous Representation Model (CRM) functionality to simulate granular soil and performs multi-objective parametric optimization on candidate rover wheels to meet a desired performance criterion. The resulting wheel design is then evaluated experimentally using a single-wheel testbed. We end with a discussion of how the toolchain can be extended to simultaneously co-optimize other system parameters, such as system power consumption and feedback control gains.
Optimization↗
The use of interactive computer vision and robot hand controllers for enhancing manufacturing safety
Current available robotic systems provide limited support for CAD-based model-driven visualization, sensing algorithm development and integration, and automated graphical planning systems. This paper describes ongoing work which provides the functionality necessary to apply advanced robotics to automated manufacturing and assembly operations. An interface has been built which incorporates 6-DOF tactile manipulation, displays for three dimensional graphical models, and automated tracking functions which depend on automated machine vision. A set of tools for single and multiple focal plane sensor image processing and understanding has been demonstrated which utilizes object recognition models. The resulting tool will enable sensing and planning from computationally simple graphical objects. A synergistic interplay between human and operator vision is created from programmable feedback received from the controller. This approach can be used as the basis for implementing enhanced safety in automated robotics manufacturing, assembly, repair and inspection tasks in both ground and space applications. Thus, an interactive capability has been developed to match the modeled environment to the real task environment for safe and predictable task execution.
Marzwell, Neville I.↗
Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements
The Aerosol Robotic Network (AERONET) has provided highly accurate, ground-truth measurements of the aerosol optical depth (AOD) using Cimel Electronique Sun–sky radiometers for more than 25 years. In Version 2 (V2) of the AERONET database, the near-real-time AOD was semiautomatically quality controlled utilizing mainly cloud-screening methodology, while additional AOD data contaminated by clouds or affected by instrument anomalies were removed manually before attaining quality-assured status (Level 2.0). The large growth in the number of AERONET sites over the past 25 years resulted in significant burden to the manual quality control of millions of measurements in a consistent manner. The AERONET Version 3 (V3) algorithm provides fully automatic cloud screening and instrument anomaly quality controls. All of these new algorithm updates apply to near-real-time data as well as post-field-deployment processed data, and AERONET reprocessed the database in 2018. A full algorithm redevelopment provided the opportunity to improve data inputs and corrections such as unique filter-specific temperature characterizations for all visible and near-infrared wavelengths, updated gaseous and water vapor absorption coefficients, and ancillary data sets. The Level 2.0 AOD quality-assured data set is now available within a month after post-field calibration, reducing the lag time from up to several months. Near-real-time estimated uncertainty is determined using data qualified as V3 Level 2.0 AOD and considering the difference between the AOD computed with the pre-field calibration and AOD computed with pre-field and post-field calibration. This assessment provides a near-real-time uncertainty estimate for which average differences of AOD suggest a +0.02 bias and one sigma uncertainty of 0.02, spectrally, but the bias and uncertainty can be significantly larger for specific instrument deployments. Long-term monthly averages analyzed for the entire V3 and V2 databases produced average differences (V3–V2) of +0.002 with a ±0.02 SD (standard deviation), yet monthly averages calculated using time-matched observations in both databases were analyzed to compute an average difference of −0.002 with a ±0.004 SD. The high statistical agreement in multiyear monthly averaged AOD validates the advanced automatic data quality control algorithms and suggests that migrating research to the V3 database will corroborate most V2 research conclusions and likely lead to more accurate results in some cases.
David M. Giles↗
Simplifying applications software for vision guided robot implementation
A simple approach to robot applications software is described. The idea is to use commercially available software and hardware wherever possible to minimize system costs, schedules and risks. The U.S. has been slow in the adaptation of robots and flexible automation compared to the fluorishing growth of robot implementation in Japan. The U.S. can benefit from this approach because of a more flexible array of vision guided robot technologies.
Duncheon, Charlie↗
Rationale and requirements for lunar exploration
The moon has had a complex and geologically fascinating history. It is a natural laboratory in which to study planetary processes and is readily accessible, only 2.5 days away. The moon is the only planetary object from which there are samples of known geologic context, and its history is understood well enough to permit the posing and answering of sophisticated scientific questions. The moon also preserves information about the earth's early history and cratering record, and about the sun's history. To retrieve all this information, it is necessary to return to the moon and study it from orbit, establish surface geophysical networks, obtain reconnaissance samples, and perform field work. These ventures will require development of interesting technological systems, such as a robotic field geologist, automated roving vehicles (both pressurized for humans and unpressurized for robotic devices), a host of sampling tools, automated sample-return spacecraft, and all of the associated navigation, communication, and power systems.
Spudis, Paul D.↗
Process development for automated solar cell and module production. Task 4: Automated array assembly
A process sequence which can be used in conjunction with automated equipment for the mass production of solar cell modules for terrestrial use was developed. The process sequence was then critically analyzed from a technical and economic standpoint to determine the technological readiness of certain process steps for implementation. The steps receiving analysis were: back contact metallization, automated cell array layup/interconnect, and module edge sealing. For automated layup/interconnect, both hard automation and programmable automation (using an industrial robot) were studied. The programmable automation system was then selected for actual hardware development.
Source record↗
Automation of Vapor-Diffusion Growth of Protein Crystals
Some improvements have been made in a system of laboratory equipment developed previously for studying the crystallization of proteins from solution by use of dynamically controlled flows of dry gas. The improvements involve mainly (1) automation of dispensing of liquids for starting experiments, (2) automatic control of drying of protein solutions during the experiments, and (3) provision for automated acquisition of video images for monitoring experiments in progress and for post-experiment analysis. The automation of dispensing of liquids was effected by adding an automated liquid-handling robot that can aspirate source solutions and dispense them in either a hanging-drop or a sitting-drop configuration, whichever is specified, in each of 48 experiment chambers. A video camera of approximately the size and shape of a lipstick dispenser was added to a mobile stage that is part of the robot, in order to enable automated acquisition of images in each experiment chamber. The experiment chambers were redesigned to enable the use of sitting drops, enable backlighting of each specimen, and facilitate automation.
Hamrick, David T.↗
Automating CapCom Using Mobile Agents and Robotic Assistants
We have developed and tested an advanced EVA communications and computing system to increase astronaut self-reliance and safety, reducing dependence on continuous monitoring and advising from mission control on Earth. This system, called Mobile Agents (MA), is voice controlled and provides information verbally to the astronauts through programs called personal agents. The system partly automates the role of CapCom in Apollo-including monitoring and managing EVA navigation, scheduling, equipment deployment, telemetry, health tracking, and scientific data collection. EVA data are stored automatically in a shared database in the habitat/vehicle and mirrored to a site accessible by a remote science team. The program has been developed iteratively in the context of use, including six years of ethnographic observation of field geology. Our approach is to develop automation that supports the human work practices, allowing people to do what they do well, and to work in ways they are most familiar. Field experiments in Utah have enabled empirically discovering requirements and testing alternative technologies and protocols. This paper reports on the 2004 system configuration, experiments, and results, in which an EVA robotic assistant (ERA) followed geologists approximately 150 m through a winding, narrow canyon. On voice command, the ERA took photographs and panoramas and was directed to move and wait in various locations to serve as a relay on the wireless network. The MA system is applicable to many space work situations that involve creating and navigating from maps (including configuring equipment for local topology), interacting with piloted and unpiloted rovers, adapting to environmental conditions, and remote team collaboration involving people and robots.
Clancey, William J.↗
Automating CapCom Using Mobile Agents and Robotic Assistants
Mobile Agents (MA) is an advanced Extra-Vehicular Activity (EVA) communications and computing system to increase astronaut self-reliance and safety, reducing dependence on continuous monitoring and advising from mission control on Earth. MA is voice controlled and provides information verbally to the astronauts through programs called "personal agents." The system partly automates the role of CapCom in Apollo-including monitoring and managing navigation, scheduling, equipment deployment, telemetry, health tracking, and scientific data collection. Data are stored automatically in a shared database in the habitat/vehicle and mirrored to a site accessible by a remote science team. The program has been developed iteratively in authentic work contexts, including six years of ethnographic observation of field geology. Analog field experiments in Utah enabled empirically discovering requirements and testing alternative technologies and protocols. We report on the 2004 system configuration, experiments, and results, in which an EVA robotic assistant (ERA) followed geologists approximately 150 m through a winding, narrow canyon. On voice command, the ERA took photographs and panoramas and was directed to serve as a relay on the wireless network.
Clancey, William J.↗
Automation of vehicle processing at Space Station Freedom
A methodology for applying the Kennedy Space Center vehicle processing experience to similar operations at Space Station Freedom is described. First, the required on-orbit processing tasks are identified. These tasks are then evaluated for automation suitability, and robotic manipulator and artificial intelligence technologies are investigated to automate selected physical and cognitive tasks. Effects on processing times, extra-vehicular activity savings, and required resources for incorporating these automation enhancements are identified. Results of the following case studies are included: Phobos Gateway Vehicle On-Orbit Assembly and Launch, Lunar Evolution Vehicle On-Orbit Refurbishment, and Mars Mission Vehicle On-Orbit Assembly and Launch.
Vargo, Rick↗
Machine learning in motion control
The existing methodologies for robot programming originate primarily from robotic applications to manufacturing, where uncertainties of the robots and their task environment may be minimized by repeated off-line modeling and identification. In space application of robots, however, a higher degree of automation is required for robot programming because of the desire of minimizing the human intervention. We discuss a new paradigm of robotic programming which is based on the concept of machine learning. The goal is to let robots practice tasks by themselves and the operational data are used to automatically improve their motion performance. The underlying mathematical problem is to solve the problem of dynamical inverse by iterative methods. One of the key questions is how to ensure the convergence of the iterative process. There have been a few small steps taken into this important approach to robot programming. We give a representative result on the convergence problem.
Su, Renjeng↗
Robotic and Human-Tended Collaborative Drilling Automation for Subsurface Exploration
Future in-situ lunar/martian resource utilization and characterization, as well as the scientific search for life on Mars, will require access to the subsurface and hence drilling. Drilling on Earth is hard - an art form more than an engineering discipline. Human operators listen and feel drill string vibrations coming from kilometers underground. Abundant mass and energy make it possible for terrestrial drilling to employ brute-force approaches to failure recovery and system performance issues. Space drilling will require intelligent and autonomous systems for robotic exploration and to support human exploration. Eventual in-situ resource utilization will require deep drilling with probable human-tended operation of large-bore drills, but initial lunar subsurface exploration and near-term ISRU will be accomplished with lightweight, rover-deployable or standalone drills capable of penetrating a few tens of meters in depth. These lightweight exploration drills have a direct counterpart in terrestrial prospecting and ore-body location, and will be designed to operate either human-tended or automated. NASA and industry now are acquiring experience in developing and building low-mass automated planetary prototype drills to design and build a pre-flight lunar prototype targeted for 2011-12 flight opportunities. A successful system will include development of drilling hardware, and automated control software to operate it safely and effectively. This includes control of the drilling hardware, state estimation of both the hardware and the lithography being drilled and state of the hole, and potentially planning and scheduling software suitable for uncertain situations such as drilling. Given that Humans on the Moon or Mars are unlikely to be able to spend protracted EVA periods at a drill site, both human-tended and robotic access to planetary subsurfaces will require some degree of standalone, autonomous drilling capability. Human-robotic coordination will be important, either between a robotic drill and humans on Earth, or a human-tended drill and its visiting crew. The Mars Analog Rio Tinto Experiment (MARTE) is a current project that studies and simulates the remote science operations between an automated drill in Spain and a distant, distributed human science team. The Drilling Automation for Mars Exploration (DAME) project, by contrast: is developing and testing standalone automation at a lunar/martian impact crater analog site in Arctic Canada. The drill hardware in both projects is a hardened, evolved version of the Advanced Deep Drill (ADD) developed by Honeybee Robotics for the Mars Subsurface Program. The current ADD is capable of 20m, and the DAME project is developing diagnostic and executive software for hands-off surface operations of the evolved version of this drill. The current drill automation architecture being developed by NASA and tested in 2004-06 at analog sites in the Arctic and Spain will add downhole diagnosis of different strata, bit wear detection, and dynamic replanning capabilities when unexpected failures or drilling conditions are discovered in conjunction with simulated mission operations and remote science planning. The most important determinant of future 1unar and martian drilling automation and staffing requirements will be the actual performance of automated prototype drilling hardware systems in field trials in simulated mission operations. It is difficult to accurately predict the level of automation and human interaction that will be needed for a lunar-deployed drill without first having extensive experience with the robotic control of prototype drill systems under realistic analog field conditions. Drill-specific failure modes and software design flaws will become most apparent at this stage. DAME will develop and test drill automation software and hardware under stressful operating conditions during several planned field campaigns. Initial results from summer 2004 tests show seven identifi distinct failure modes of the drill: cuttings-removal issues with low-power drilling into permafrost, and successful steps at executive control and initial automation.
Glass, Brian↗