3D Printing a Space Vehicle NASA’S HUMAN-SUPPORTING ROVER HAS FDM PARTS
STRATASYS - THE 3D PRINTING SOLUTIONS COMPANY
CASE STUDY
An agile white vehicle roams the Arizona desert, maneuvering the unforgiving terrain as the wind and sun beat down and temperatures swing from one extreme to another. NASA astronauts and engineers are test-driving a rover over rocks and sand, up and down hills in an environment that simulates the brutal conditions of Mars.
This is Desert RATS (Research and Technology Studies), and the rover — about the size of a Hummer and boasting a pressurized cabin to support humans in space — is being put to the test. It could ultimately serve one of NASA’s loftiest goals: human exploration of Mars. In the nearer future, similar vehicles might help humans investigate near-earth asteroids.
The rover is integral to NASA’s mission to extend human reach farther into space. Its cabin can accommodate a pair of astronauts for days as they study extraterrestrial surfaces. Its twelve rugged wheels on six axles grapple over irregular, unsure terrain. And its forward-jutting cockpit can tilt down to place its observation bubble low to the ground.
3D Printed Rover Parts
To design such a tenacious and specialized vehicle, NASA engineers drew on ingenuity and advanced technology. For example, about 70 of the parts that make up the rover were built digitally, directly from computer designs, in the heated chamber of a production-grade Stratasys 3D Printer. The process, called Fused Deposition Modeling (FDM) Technology or additive manufacturing, creates complex shapes durable enough for Martian terrain.
When you’re building a handful of highly customized vehicles and subjecting them to otherworldly punishment, stock parts and traditional manufacturing methods aren’t enough. 3D-printed parts on NASA’s rover include flame-retardant vents and housings, camera mounts, large pod doors, a large part that functions as a front bumper, and many custom fixtures. FDM offers the design flexibility and quick turnaround to build tailored housings for complex electronic assemblies. For example, one ear-shaped exterior housing is deep and contorted, and would be impossible — or at least prohibitively expensive — to machine.
For its 3D-printed parts, NASA uses ABS, PCABS and polycarbonate materials. FDM, patented by Stratasys, is the only 3D-printing method that supports production-grade thermoplastics, which are lightweight but durable enough for rugged end-use parts.
Failure is Not an Option “You always want it to be as light as possible, but you also want it to be strong enough that it’s got your safety factors, that nobody’s going to get hurt,” NASA test engineer Chris Chapman says. NASA’s mantra regarding human space travel is: Failure is not an option. The journey to space subjects a vehicle to intense stresses, starting with the launch from Earth. “You’re going at several thousand miles per hour just to escape the Earth’s atmosphere. So you’ve got to be able to handle all these vibrations just to get out into space, and the vehicle can’t be damaged,” Chapman says.
NASA engineers also 3D print prototypes to test form, fit and function of parts they’ll eventually build in other materials. This ensures machined parts are based on the best possible design by solving challenges before committing to expensive tooling. “Everyone’s got a budget to deal with, and we’re no different,” says Chapman.
Every day, NASA engineers and their devices bridge the gap between practical concerns such as budget and manufacturability, and the human drive to discover the secrets of unfamiliar worlds — in the workshop, in the desert, and eventually on another planet.
Watch a video of the rover’s story online at Stratasys.com/Rover
3-D uncertainty-based topographic change detection with structure-from-motion photogrammetry: precision maps for ground control and directly georeferenced surveys.
White Rose Research Online URL for this paper: http://eprints.whiterose.ac.uk/112711/
James, M. R.a, Robson, S.b and Smith, M. W.c
Lancaster Environment Centre, Lancaster University, Lancaster,
(corresponding author: m.james@lancs.ac.uk, )
Short title: 3-D uncertainty-based change detection for SfM surveys
Abstract
Structure-from-motion (SfM) photogrammetry is revolutionising the collection of detailed topographic data, but insight into geomorphological processes is currently restricted by our limited understanding of SfM survey uncertainties. Here, we present an approach that, for the first time, specifically accounts for the spatially variable precision inherent to photo-based surveys, and enables confidence-bounded quantification of 3-D topographic change. The method uses novel 3-D precision maps that describe the 3-D photogrammetric and georeferencing uncertainty, and determines change through an adapted state-of-the-art fully 3-D point-cloud comparison (M3C2; Lague, et al., 2013), which is particularly valuable for complex topography. We introduce this method by: (1) using simulated UAV surveys, processed in photogrammetric software, to illustrate the spatial variability of precision and the relative influences of photogrammetric (e.g. image network geometry, tie point quality) and georeferencing (e.g. control measurement) considerations; we then present a new Monte Carlo procedure for deriving this information using standard SfM software and integrate it into confidence-bounded change detection; before demonstrating geomorphological application in which we use benchmark TLS data for validation and then estimate sediment budgets through differencing annual SfM surveys of an eroding badland. We show how 3-D precision maps enable more probable erosion patterns to be identified than existing analyses, and how a similar overall survey precision could have been achieved with direct survey georeferencing for camera position data with precision half as good as the GCPs’. Where precision is limited by weak georeferencing (e.g. camera positions with multi-metre precision, such as from a consumer UAV), then overall survey precision can scale as n-½ of the control precision (n = number of images). Our method also provides variance-covariance information for all parameters. Thus, we now open the door for SfM practitioners to use the comprehensive analyses that have underpinned rigorous photogrammetric approaches over the last half-century.
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