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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

3d Kinematic Passive-steering Control with Roll Compensation for Rover Trajectory Following Over Rough Terrain

Some of the most appealing areas for future planetary surface exploration lie in rough, uneven terrains, such as craters and cold traps, which are currently inaccessible by state-of-the-art robotic systems [1]. To provide modularity for access and in-situ sampling within such extreme environments, Jet Propulsion Laboratory (JPL) and California Institute of Technology have collaborated to develop the DuAxel rover system, a modular robot composed of two Axel rappelling vehicles docked to a central module into a four-wheeled configuration, suited for driving long distances due to articulated passivesteering capabilities [2]. Inspired by Carnegie Mellon’s Zoë rover [3], a 3D kinematic control strategy, leveraging a novel, DuAxel-centric model, has been developed to enable precise trajectory following over rough, flat terrain with presence of obstacles.

McGarey, Patrick↗
NASA NTRS2003

Mars Exploration Rovers as Virtual Instruments for Determination of Terrain Roughness and Physical Properties

The two 2003 Mars Exploration Rovers (MERs), in combination with the Athena Payload, will be used as virtual instrument systems to infer terrain properties during traverses, in addition to using the rover wheels to excavate trenches, exposing subsurface materials for remote and in-situ observations. The MERs are being modeled using finite element-based rover system transfer functions that utilize the distribution of masses associated with the vehicle, together with suspension and wheel dynamics, to infer surface roughness and mechanical properties from traverse time series data containing vehicle yaw, pitch, roll, encoder counts, and motor currents. These analyses will be supplemented with imaging and other Athena Payload measurements. The approach is being validated using Sojourner data, the FIDO rover, and experiments with MER testbed vehicles. In addition to conducting traverse science and associated analyses, trenches will be excavated by the MERs to depths of approximately 10-20 cm by locking all but one of the front wheels and rotating that wheel backwards so that the excavated material is piled up on the side of the trench away from the vehicle. Soil cohesion and angle of internal friction will be determined from the trench telemetry data. Emission spectroscopy and in-situ observations will be made using the Athena payload before and after imaging. Trenching and observational protocols have been developed using Sojourner results; data from the FIDO rover, including trenches dug into sand, mud cracks, and weakly indurated bedrock; and experiments with MER testbed rovers. Particular attention will be focused on Mini-TES measurements designed to determine the abundance and state of subsurface water (e.g. hydrated, in zeolites, residual pore ice?) predicted to be present from Odyssey GRS/NS/HEND data.

R E Arvidson↗
NASA NTRS1991

Autonomous rough terrain navigation - Lessons learned

Because of light-time delays, a planetary rover located on Mars or beyond will probably need to be able to navigate autonomously, in order to do significant exploration. Recent work at JPL has explored several different autonomous navigation strategies. This work includes highly deliberative methods that require large amounts of computation and internal storage but yield very planful behavior to more reactive systems that require less resources but whose behavior is more difficult to model. This paper briefly presents these methods, the results from experiments both in simulation and in the field, and some conclusions on the value of different approaches, and their possible impact on the structure of the robot and the mission architecture.

Miller, David P.↗
NASA NTRS2023

Dynamically Rendering Rough Terrain with Minimal Memory Overhead

Rendering highly detailed terrain is a process with the potential to consume a great deal of a computer’s random access memory (RAM). In a browser-based application, this resource is limited even further, leading to the necessity to use alternative methods of rendering the large amount of data needed for high detail. This report describes one such method that places the onus of rendering on the speed of the graphics processing unit (GPU) rather than on the computer’s memory. By removing attribute buffers, which contribute greatly to memory costs, from the rendering pipeline and generating the requisite attributes on the fly using a heightmap texture instead, it is estimated that memory usage can be cut down to one-sixth that of the previous method.

Visualization↗
NASA NTRS2003

The Surface Roughness of Terrains on Mars

The RMS roughness measurements produced by Neumann et al. from Mars Orbiter Laser Altimeter (MOLA) data provide unique information about surface height variations at an effective length scale of < 75 m. Roughness at this scale is important not only for landing site safety considerations, but also for assessment of landscape evolution, which depends on emplacement mechanisms and erosional/depositional processes. Here we present an examination of the global surface roughness map with discussion of terrain types and potential formation and/or alteration mechanisms. Spatially coherent terrain types were identified based on inspection of the roughness map. These terrains were further characterized through analysis of morphology and geology using MOLA topography, MOC wide-angle, and MOC narrow-angle images as well as the geologic maps produced by Scott & Tanaka and Greeley & Guest. All of these data were used to explore potential formation and modification processes.

K S Deal↗
NASA NTRS2003

EVA Robotic Assistant Project: Platform Attitude Prediction

The Robotic Systems Technology Branch is currently working on the development of an EVA Robotic Assistant under the sponsorship of the Surface Systems Thrust of the NASA Cross Enterprise Technology Development Program (CETDP). This will be a mobile robot that can follow a field geologist during planetary surface exploration, carry his tools and the samples that he collects, and provide video coverage of his activity. Prior experiments have shown that for such a robot to be useful it must be able to follow the geologist at walking speed over any terrain of interest. Geologically interesting terrain tends to be rough rather than smooth. The commercial mobile robot that was recently purchased as an initial testbed for the EVA Robotic Assistant Project, an ATRV Jr., is capable of faster than walking speed outside but it has no suspension. Its wheels with inflated rubber tires are attached to axles that are connected directly to the robot body. Any angular motion of the robot produced by driving over rough terrain will directly affect the pointing of the on-board stereo cameras. The resulting image motion is expected to make tracking of the geologist more difficult. This will either require the tracker to search a larger part of the image to find the target from frame to frame or to search mechanically in pan and tilt whenever the image motion is large enough to put the target outside the image in the next frame. This project consists of the design and implementation of a Kalman filter that combines the output of the angular rate sensors and linear accelerometers on the robot to estimate the motion of the robot base. The motion of the stereo camera pair mounted on the robot that results from this motion as the robot drives over rough terrain is then straightforward to compute. The estimates may then be used, for example, to command the robot s on-board pan-tilt unit to compensate for the camera motion induced by the base movement. This has been accomplished in two ways: first, a standalone head stabilizer has been implemented and second, the estimates have been used to influence the search algorithm of the stereo tracking algorithm. Studies of the image motion of a tracked object indicate that the image motion of objects is suppressed while the robot crossing rough terrain. This work expands the range of speed and surface roughness over which the robot should be able to track and follow a field geologist and accept arm gesture commands from the geologist.

Nickels, Kevin M.↗
NASA NTRS2011

Single-Frame Terrain Mapping Software for Robotic Vehicles

This software is a component in an unmanned ground vehicle (UGV) perception system that builds compact, single-frame terrain maps for distribution to other systems, such as a world model or an operator control unit, over a local area network (LAN). Each cell in the map encodes an elevation value, terrain classification, object classification, terrain traversability, terrain roughness, and a confidence value into four bytes of memory. The input to this software component is a range image (from a lidar or stereo vision system), and optionally a terrain classification image and an object classification image, both registered to the range image. The single-frame terrain map generates estimates of the support surface elevation, ground cover elevation, and minimum canopy elevation; generates terrain traversability cost; detects low overhangs and high-density obstacles; and can perform geometry-based terrain classification (ground, ground cover, unknown). A new origin is automatically selected for each single-frame terrain map in global coordinates such that it coincides with the corner of a world map cell. That way, single-frame terrain maps correctly line up with the world map, facilitating the merging of map data into the world map. Instead of using 32 bits to store the floating-point elevation for a map cell, the vehicle elevation is assigned to the map origin elevation and reports the change in elevation (from the origin elevation) in terms of the number of discrete steps. The single-frame terrain map elevation resolution is 2 cm. At that resolution, terrain elevation from 20.5 to 20.5 m (with respect to the vehicle's elevation) is encoded into 11 bits. For each four-byte map cell, bits are assigned to encode elevation, terrain roughness, terrain classification, object classification, terrain traversability cost, and a confidence value. The vehicle s current position and orientation, the map origin, and the map cell resolution are all included in a header for each map. The map is compressed into a vector prior to delivery to another system.

Rankin, Arturo L.↗
NASA NTRS2003

Cooperative Exploration of Rough Martian Terrains with the "Scorpion" Legged Robot as an Adjunct to a Rover.

NASA needs autonomous robotic exploration of difficult (rough and/or steep) scientifically interesting Martian terrains. Concepts involving distributed autonomy for cooperative robotic exploration are key to enabling new scientific objectives in robotic missions. We propose to utilize a legged robot as an adjunct scout to a rover for access to difficult - scientifically interesting - terrains (rocky areas, slopes, cliffs). Our final mission scenario involves the Ames rover platform "K9" and Scorpion acting together to explore a steep cliff, with the Scorpion robot rappelling down using the K9 as an anchor as well as mission planner and executive. Cooperation concepts, including wheeled rappelling robots have been proposed before. Now we propose to test the combined advantages of a wheeled vehicle with a legged scout as well as the advantages of merging of high level planning and execution with biologically inspired, behavior based robotics. We propose to use the 8-legged, multifunctional autonomous robot platform Scorpion that is currently capable of: Walking on different terrains (rocks, sand, grass, ...). Perceiving its environment and modifying its behavioral pattern accordingly. These capabilities would be extended to enable the Scorpion to: communicate and cooperate with a partner robot; climb over rocks, rubble piles, and objects with structural features. This will be done in the context of exploration of rough terrains in the neighborhood of the rover, but inaccessible to it, culminating in the added capability of rappelling down a steep cliff for both vertical and horizontal terrain observation.

Colombano, Silvano P.↗
NASA NTRS1984

Frequency and site selection criteria for MST radars, part 5.1A

The majority of mesosphere-stratosphere-troposphere (MST) and ST radars are located in or near mountainous terrain. When measuring horizontal velocities, the terrain is a small factor, but when measuring vertical velocities, the meteorological noise induced by rough terrain can severely limit the usefulness of the observations. When the variance of the vertical velocity is too large, it is not possible to suitably filter the data to detect the small synoptic-scale signal with reasonable statistical confidence. The variance of vertical velocity at all tropospheric levels is directly related to the low level wind speed during flow over rough terrain. It is suggested that the synoptic-scale vertical velocity can be measured by ST radars where the terrain is smooth. The large-scale vertical velocity cannot always be reliably determined from MST radar data when the underlying terrain is rough. The vertical velocity is potentially on of future radar site selections, taking into account the desired meteorological applications of the data and engineering design factors. If the synoptic-scale vertical velocity is a desired variable, the radar should not be located near mountains.

Nastrom, G. D.↗
NASA NTRS1971

Lunar terrain mapping and relative-roughness analysis

Terrain maps of the equatorial zone were prepared at scales of 1:2,000,000 and 1:1,000,000 to classify lunar terrain with respect to roughness and to provide a basis for selecting sites for Surveyor and Apollo landings, as well as for Ranger and Lunar Orbiter photographs. Lunar terrain was described by qualitative and quantitative methods and divided into four fundamental classes: maria, terrae, craters, and linear features. Some 35 subdivisions were defined and mapped throughout the equatorial zone, and, in addition, most of the map units were illustrated by photographs. The terrain types were analyzed quantitatively to characterize and order their relative roughness characteristics. For some morphologically homogeneous mare areas, relative roughness can be extrapolated to the large scales from measurements at small scales.

Rowan, L. C.↗
NASA NTRS1989

Measurement of vertical velocity using clear-air Doppler radars

A new clear air Doppler radar was constructed, called the Flatland radar, in very flat terrain near Champaign-Urbana, Illinois. The radar wavelength is 6.02 m. The radar has been measuring vertical velocity every 153 s with a range resolution of 750 m almost continuously since March 2, 1987. The variance of vertical velocity at Flatland is usually quite small, comparable to the variance at radars located near rough terrain during periods of small background wind. The absence of orographic effects over very flat terrain suggests that clear air Doppler radars can be used to study vertical velocities due to other processes, including synoptic scale motions and propagating gravity waves. For example, near rough terrain the shape of frequency spectra changes drastically as the background wind increases. But at Flatland the shape at periods shorter than a few hours changes only slowly, consistent with the changes predicted by Doppler shifting of gravity wave spectra. Thus it appears that the short period fluctuations of vertical velocity at Flatland are alsmost entirely due to the propagating gravity waves.

Vanzandt, T. E.↗
NASA NTRS2009

Adaptive Behavior for Mobile Robots

The term "System for Mobility and Access to Rough Terrain" (SMART) denotes a theoretical framework, a control architecture, and an algorithm that implements the framework and architecture, for enabling a land-mobile robot to adapt to changing conditions. SMART is intended to enable the robot to recognize adverse terrain conditions beyond its optimal operational envelope, and, in response, to intelligently reconfigure itself (e.g., adjust suspension heights or baseline distances between suspension points) or adapt its driving techniques (e.g., engage in a crabbing motion as a switchback technique for ascending steep terrain). Conceived for original application aboard Mars rovers and similar autonomous or semi-autonomous mobile robots used in exploration of remote planets, SMART could also be applied to autonomous terrestrial vehicles to be used for search, rescue, and/or exploration on rough terrain.

Huntsberger, Terrance↗