Characterization of the electromechanical properties of EAP material
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paint NDE automation robotics↗
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Transition of EAP material from novelty to practical applications - are we there yet?
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paint NDE automation robotics↗
The JPL Serpentine Robot: A 12 DOF System for Inspection
The Serpentine Robot is a prototype hyper-redundant (snake-like) manipulator system developed at the Jet Propulsion Laboratory. It is designed to navigate and perform tasks in obstructed and constrained environments in which conventional 6 DOF manipulators cannot function. Described are the robot mechanical design, a joint assembly low level inverse kinematic algorithm, control development, and applications.
robotics Serpentine robot automation kinematics↗
Multifunction automated crawling system
The present invention is an automated crawling robot system including a platform, a first leg assembly, a second leg assembly, first and second rails attached to the platform, and an onboard electronic computer controller. The first leg assembly has an intermittent coupling device and the second leg assembly has an intermittent coupling device for intermittently coupling the respective first and second leg assemblies to a particular object. The first and second leg assemblies are slidably coupled to the rail assembly and are slidably driven by motors to thereby allow linear movement. In addition, the first leg assembly is rotary driven by a rotary motor to thereby provide rotary motion relative to the platform. To effectuate motion, the intermittent coupling devices of the first and second leg assemblies alternately couple the respective first and second leg assemblies to an object. This motion is done while simultaneously moving one of the leg assemblies linearly in the desired direction and preparing the next step. This arrangement allows the crawler of the present invention to traverse an object in a range of motion covering 360 degrees.
Bar-Cohen, Yoseph↗
Facilitating Analysis of Multiple Partial Data Streams
Robotic Operations Automation: Mechanisms, Imaging, Navigation report Generation (ROAMING) is a set of computer programs that facilitates and accelerates both tactical and strategic analysis of time-sampled data especially the disparate and often incomplete streams of Mars Explorer Rover (MER) telemetry data described in the immediately preceding article. As used here, tactical refers to the activities over a relatively short time (one Martian day in the original MER application) and strategic refers to a longer time (the entire multi-year MER missions in the original application). Prior to installation, ROAMING must be configured with the types of data of interest, and parsers must be modified to understand the format of the input data (many example parsers are provided, including for general CSV files). Thereafter, new data from multiple disparate sources are automatically resampled into a single common annotated spreadsheet stored in a readable space-separated format, and these data can be processed or plotted at any time scale. Such processing or plotting makes it possible to study not only the details of a particular activity spanning only a few seconds, but also longer-term trends. ROAMING makes it possible to generate mission-wide plots of multiple engineering quantities [e.g., vehicle tilt as in Figure 1(a), motor current, numbers of images] that, heretofore could be found only in thousands of separate files. ROAMING also supports automatic annotation of both images and graphs. In the MER application, labels given to terrain features by rover scientists and engineers are automatically plotted in all received images based on their associated camera models (see Figure 2), times measured in seconds are mapped to Mars local time, and command names or arbitrary time-labeled events can be used to label engineering plots, as in Figure 1(b).
Maimone, Mark W.↗
Lunabotics Mining Competition: Inspiration Through Accomplishment
NASA's Lunabotics Mining Competition is designed to promote the development of interest in space activities and STEM (Science, Technology, Engineering, and Mathematics) fields. The competition uses excavation, a necessary first step towards extracting resources from the regolith and building bases on the moon. The unique physical properties of lunar regolith and the reduced 1/6th gravity, vacuum environment make excavation a difficult technical challenge. Advances in lunar regolith mining have the potential to significantly contribute to our nation's space vision and NASA space exploration operations. The competition is conducted annually by NASA at the Kennedy Space Center Visitor Complex. The teams that can use telerobotic or autonomous operation to excavate a lunar regolith geotechnical simulant, herein after referred to as Black Point-1 (or BP-1) and score the most points (calculated as an average of two separate 10-minute timed competition attempts) will eam points towards the Joe Kosmo Award for Excellence and the scores will reflect ranking in the on-site mining category of the competition. The minimum excavation requirement is 10.0 kg during each competition attempt and the robotic excavator, referred to as the "Lunabot", must meet all specifications. This paper will review the achievements of the Lunabotics Mining Competition in 2010 and 2011, and present the new rules for 2012. By providing a framework for robotic design and fabrication, which culminates in a live competition event, university students have been able to produce sophisticated lunabots which are tele-operated. Multi-disciplinary teams are encouraged and the extreme sense of accomplishment provides a unique source of inspiration to the participating students, which has been shown to translate into increased interest in STEM careers. Our industrial sponsors (Caterpillar, Newmont Mining, Harris, Honeybee Robotics) have all stated that there is a strong need for skills in the workforce related to robotics and automated machines. In 2010, 22 United States (US) universities competed, and in May 2011 the competition was opened to international participation, with 46 Universities attending. There were 12 international teams and 34 US teams. This combined total directly inspired an estimated 544 university students. More students and the public were engaged via internet broadcasting and social networking media. This is expected to be of value for actual future space missions, as knowledge is gained from testing many innovative prototypes in simulated lunar regolith. More information is available at www.nasa.gov/lunabotics/.
Mueller, Robert P.↗
Evaluation of Human and AutomationRobotics Integration Needs for Future Human Exploration Missions
NASA employs Design Reference Missions (DRMs) to define potential architectures for future human exploration missions to deep space, the Moon, and Mars. While DRMs to these destinations share some components, each mission has different needs. This paper focuses on the human and automation/robotic integration needs for these future missions, evaluating them with respect to NASA research gaps in the area of space human factors engineering. The outcomes of our assessment is a human and automation/robotic (HAR) task list for each of the four DRMs that we reviewed (i.e., Deep Space Sortie, Lunar Visit/Habitation, Deep Space Habitation, and Planetary), a list of common critical HAR factors that drive HAR design.
design reference missions↗
Future Exploration Missions' Tasks Associated with the Risk of Inadequate Design of Human and Automation/Robotic Integration
NASA's Human Research Program (HRP) funds research efforts aimed at mitigating various human health and performance risks, including the Risk of Inadequate Design of Human and Automation/Robotic Integration (HARI). As such, within HRP, the Human Factors and Behavioral Performance (HFBP) Element tasked an evaluation of future HARI needs in order to scope and focus the HARI risk research plan. The objective was to provide a systematic understanding of the critical factors associated with effective HARI that will be necessary to achieve the future mission goals for near- and deep-space exploration. Future mission goals are specified by NASA Design Reference Missions (DRMs) that are pertinent to the HRP. The outcome of this evaluation is a set of NASA-relevant HARI tasks, factors, and interactions required for exploration-class missions.
space operations↗
Autonomy and Robotics Workshop in Support of Space Crop Production
This presentation will be an introduction and overview of space crop production needs, goals, and challenges in the areas of robotics and automation for the workshop Aug. 6-7, 2019 at Kennedy Space Center. This presentation will be used to start the workshop and set the direction.
Fritsche, Ralph F.↗
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.↗
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.↗