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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Intelligent robots for planetary exploration and construction

Robots capable of practical applications in planetary exploration and construction will require realtime sensory-interactive goal-directed control systems. A reference model architecture based on the NIST Real-time Control System (RCS) for real-time intelligent control systems is suggested. RCS partitions the control problem into four basic elements: behavior generation (or task decomposition), world modeling, sensory processing, and value judgment. It clusters these elements into computational nodes that have responsibility for specific subsystems, and arranges these nodes in hierarchical layers such that each layer has characteristic functionality and timing. Planetary exploration robots should have mobility systems that can safely maneuver over rough surfaces at high speeds. Walking machines and wheeled vehicles with dynamic suspensions are candidates. The technology of sensing and sensory processing has progressed to the point where real-time autonomous path planning and obstacle avoidance behavior is feasible. Map-based navigation systems will support long-range mobility goals and plans. Planetary construction robots must have high strength-to-weight ratios for lifting and positioning tools and materials in six degrees-of-freedom over large working volumes. A new generation of cable-suspended Stewart platform devices and inflatable structures are suggested for lifting and positioning materials and structures, as well as for excavation, grading, and manipulating a variety of tools and construction machinery.

Albus, James S.↗
NASA NTRS2020

Autonomous Navigation over Europa Analogue Terrain for an Actively Articulated Wheel-on-Limb Rover

The ocean world Europa is a prime target forexploration given its potential habitability [1]. We proposea mobile robotic system that is capable of autonomouslytraversing hundreds of meters to visit multiple sites of intereston a Europan analogue surface. Due to the topology of Europanterrain being largely unknown, it is desired that this mobilitysystem traverse a large variety of terrain types. The mobilitysystem should also be capable of crossing unstructured terrainin an autonomous manner given the communications limitationsbetween Earth and Europa.A wheel-on-limb robotic rover is presented that may activelyconform to terrain features up to 1.5 wheel diameters tall whiledriving. The robot uses a sampling-based motion planner togenerate paths that leverage its unique locomotive capabilities.The planner assesses terrain hazards and wheel workspacelimits as obstacles. It may also select a mobility mode basedon predicted energy usage and the need for limb articulationon the terrain being traversed. This autonomous mobility wasevaluated on the chaotic salt-evaporite terrain found in DeathValley, CA, an analogue to the Europan surface. Over the courseof 38 trials, the rover autonomously traversed 435m of extremeterrain while maintaining a rate of 0.64 traverse ending failuresfor every 10m driven.

Meirion-Griffith, Gareth↗
NASA NTRS1994

ARK: Autonomous mobile robot in an industrial environment

This paper describes research on the ARK (Autonomous Mobile Robot in a Known Environment) project. The technical objective of the project is to build a robot that can navigate in a complex industrial environment using maps with permanent structures. The environment is not altered in any way by adding easily identifiable beacons and the robot relies on naturally occurring objects to use as visual landmarks for navigation. The robot is equipped with various sensors that can detect unmapped obstacles, landmarks and objects. In this paper we describe the robot's industrial environment, it's architecture, a novel combined range and vision sensor and our recent results in controlling the robot in the real-time detection of objects using their color and in the processing of the robot's range and vision sensor data for navigation.

Nickerson, S. B.↗
NASA NTRS2013

NASA Tech Briefs, April 2013

Topics covered include: Fully Integrated, Miniature, High-Frequency Flow Probe Utilizing MEMS Leadless SOI Technology; Nanoscale Surface Plasmonics Sensor With Nanofluidic Control; Advanced Dispersed Fringe Sensing Algorithm for Coarse Phasing Segmented Mirror Telescopes; Neural Network Back-Propagation Algorithm for Sensing Hypergols; Bulk Moisture and Salinity Sensor; Change-Based Satellite Monitoring Using Broad Coverage and Targetable Sensing; Circularly Polarized Microwave Antenna Element with Very Low Off-Axis Cross-Polarization; Ultra-Low Heat-Leak, High-Temperature Superconducting Current Leads for Space Applications; Flash Cracking Reactor for Waste Plastic Processing; An Automated Safe-to-Mate (ASTM) Tester; Wireless Chalcogenide Nanoionic-Based Radio-Frequency Switch; Compute Element and Interface Box for the Hazard Detection System; DOT Transmit Module; Composite Aerogel Multifoil Protective Shielding; Li-Ion Electrolytes with Improved Safety and Tolerance to High-Voltage Systems; Polymer-Reinforced, Non-Brittle, Lightweight Cryogenic Insulation; Controlled, Site-Specific Functionalization of Carbon Nanotubes with Diazonium Salts; Regenerable Sorbent for CO2 Removal; Sprayable Aerogel Bead Compositions With High Shear Flow Resistance and High Thermal Insulation Value; Lexan Linear Shaped Charge Holder with Magnets and Backing Plate; Robotic Ankle for Omnidirectional Rock Anchors; Wind, Wave, and Tidal Energy Without Power Conditioning; An Active Heater Control Concept to Meet IXO Type Mirror Module Thermal-Structural Distortion Requirement; Waterless Clothes-Cleaning Machine; Integrated Electrical Wire Insulation Repair System; LVGEMS Time-of-Flight Mass Spectrometry on Satellites; Surface Inspection Tool for Optical Detection of Surface Defects; Per-Pixel, Dual-Counter Scheme for Optical Communications; Certification-Based Process Analysis; Surface Navigation Using Optimized Waypoints and Particle Swarm Optimization; Smart-Divert Powered Descent Guidance to Avoid the Backshell Landing Dispersion Ellipse; Estimating Foreign-Object-Debris Density from Photogrammetry Data; Adaptive Sampling of Spatiotemporal Phenomena with Optimization Criteria; Building a 2.5D Digital Elevation Model From 2D Imagery; Eyes on the Earth 3D; Target Trailing With Safe Navigation for Maritime Autonomous Surface Vehicles; Adams-Based Rover Terramechanics and Mobility Simulator - ARTEMIS; ISTP CDF Skeleton Editor; Uplink Summary Generator (ULSGEN) Version 1.0; Robotics On-Board Trainer (ROBoT); Software Engineering Tools for Scientific Models; Automatic Data Filter Customization Using a Genetic Algorithm; Tracker Toolkit; Towards Efficient Scientific Data Management Using Cloud Storage; On a Formal Tool for Reasoning About Flight Software Cost Analysis; A Nanostructured Composites Thermal Switch Controls Internal and External Short Circuit in Lithium Ion Batteries; Spacecraft Crew Cabin Condensation Control; and Functional Near-Infrared Spectroscopy Signals Measure Neuronal Activity in the Cortex.

Source record↗
NASA NTRS2019

Astrobee Robot Software: Enabling Mobile Autonomy on the ISS

Astrobee is a new free-flyer robot designed to operate inside the International Space Station (ISS). Astrobee capabilities include markerless navigation, autonomous docking for recharge, perching on handrails to minimize power and modular payloads. Astrobee will operate without crew support, controlled by teleoperation, plan execution, or on-board third parties software. This slides presents the Astrobee Robot Software, a NASA Open-Source project, powering the Astrobee robot.The Astrobee Robot Software relies on a distributed architecture based on the Robot Operating System (ROS). We present the software approach, infrastructure required, and main software components.

Fluckiger, Lorenzo↗
NASA NTRS1999

Insect-Based Vision for Autonomous Vehicles: A Feasibility Study

The aims of the project were to use a high-speed digital video camera to pursue two questions: i) To explore the influence of temporal imaging constraints on the performance of vision systems for autonomous mobile robots; To study the fine structure of insect flight trajectories with in order to better understand the characteristics of flight control, orientation and navigation.

Srinivasan, Mandyam V.↗
NASA NTRS1999

Insect-Based Vision for Autonomous Vehicles: A Feasibility Study

The aims of the project were to use a high-speed digital video camera to pursue two questions: (1) To explore the influence of temporal imaging constraints on the performance of vision systems for autonomous mobile robots; (2) To study the fine structure of insect flight trajectories in order to better understand the characteristics of flight control, orientation and navigation.

Srinivasan, Mandyam V.↗
NASA NTRS2004

Two-Armed, Mobile, Sensate Research Robot

The Anthropomorphic Robotic Testbed (ART) is an experimental prototype of a partly anthropomorphic, humanoid-size, mobile robot. The basic ART design concept provides for a combination of two-armed coordination, tactility, stereoscopic vision, mobility with navigation and avoidance of obstacles, and natural-language communication, so that the ART could emulate humans in many activities. The ART could be developed into a variety of highly capable robotic assistants for general or specific applications. There is especially great potential for the development of ART-based robots as substitutes for live-in health-care aides for home-bound persons who are aged, infirm, or physically handicapped; these robots could greatly reduce the cost of home health care and extend the term of independent living. The ART is a fully autonomous and untethered system. It includes a mobile base on which is mounted an extensible torso topped by a head, shoulders, and two arms. All subsystems of the ART are powered by a rechargeable, removable battery pack. The mobile base is a differentially- driven, nonholonomic vehicle capable of a speed >1 m/s and can handle a payload >100 kg. The base can be controlled manually, in forward/backward and/or simultaneous rotational motion, by use of a joystick. Alternatively, the motion of the base can be controlled autonomously by an onboard navigational computer. By retraction or extension of the torso, the head height of the ART can be adjusted from 5 ft (1.5 m) to 6 1/2 ft (2 m), so that the arms can reach either the floor or high shelves, or some ceilings. The arms are symmetrical. Each arm (including the wrist) has a total of six rotary axes like those of the human shoulder, elbow, and wrist joints. The arms are actuated by electric motors in combination with brakes and gas-spring assists on the shoulder and elbow joints. The arms are operated under closed-loop digital control. A receptacle for an end effector is mounted on the tip of the wrist and contains a force-and-torque sensor that provides feedback for force (compliance) control of the arm. The end effector could be a tool or a robot hand, depending on the application.

Engelberger, J. F.↗
NASA NTRS1997

Neural Network Based Sensory Fusion for Landmark Detection

NASA is planning to send numerous unmanned planetary missions to explore the space. This requires autonomous robotic vehicles which can navigate in an unstructured, unknown, and uncertain environment. Landmark based navigation is a new area of research which differs from the traditional goal-oriented navigation, where a mobile robot starts from an initial point and reaches a destination in accordance with a pre-planned path. The landmark based navigation has the advantage of allowing the robot to find its way without communication with the mission control station and without exact knowledge of its coordinates. Current algorithms based on landmark navigation however pose several constraints. First, they require large memories to store the images. Second, the task of comparing the images using traditional methods is computationally intensive and consequently real-time implementation is difficult. The method proposed here consists of three stages, First stage utilizes a heuristic-based algorithm to identify significant objects. The second stage utilizes a neural network (NN) to efficiently classify images of the identified objects. The third stage combines distance information with the classification results of neural networks for efficient and intelligent navigation.

Kumbla, Kishan -K.↗
NASA NTRS2001

Fuzzy rule-based reasoning for rover safety and survivability

Operatiional safety and health monitoring are critical matters for autonomous field mobile robots such as planetary rovers operating on challenging terrain. This paper describes relevant rover safety and health issues and presents an approach to maintaining vehicle safety in a navigational context.

planetary↗
NASA NTRS1989

A tesselated probabilistic representation for spatial robot perception and navigation

The ability to recover robust spatial descriptions from sensory information and to efficiently utilize these descriptions in appropriate planning and problem-solving activities are crucial requirements for the development of more powerful robotic systems. Traditional approaches to sensor interpretation, with their emphasis on geometric models, are of limited use for autonomous mobile robots operating in and exploring unknown and unstructured environments. Here, researchers present a new approach to robot perception that addresses such scenarios using a probabilistic tesselated representation of spatial information called the Occupancy Grid. The Occupancy Grid is a multi-dimensional random field that maintains stochastic estimates of the occupancy state of each cell in the grid. The cell estimates are obtained by interpreting incoming range readings using probabilistic models that capture the uncertainty in the spatial information provided by the sensor. A Bayesian estimation procedure allows the incremental updating of the map using readings taken from several sensors over multiple points of view. An overview of the Occupancy Grid framework is given, and its application to a number of problems in mobile robot mapping and navigation are illustrated. It is argued that a number of robotic problem-solving activities can be performed directly on the Occupancy Grid representation. Some parallels are drawn between operations on Occupancy Grids and related image processing operations.

Elfes, Alberto↗
NASA NTRS2004

Mobile Autonomous Humanoid Assistant

A mobile autonomous humanoid robot is assisting human co-workers at the Johnson Space Center with tool handling tasks. This robot combines the upper body of the National Aeronautics and Space Administration (NASA)/Defense Advanced Research Projects Agency (DARPA) Robonaut system with a Segway(TradeMark) Robotic Mobility Platform yielding a dexterous, maneuverable humanoid perfect for aiding human co-workers in a range of environments. This system uses stereo vision to locate human team mates and tools and a navigation system that uses laser range and vision data to follow humans while avoiding obstacles. Tactile sensors provide information to grasping algorithms for efficient tool exchanges. The autonomous architecture utilizes these pre-programmed skills to form human assistant behaviors. The initial behavior demonstrates a robust capability to assist a human by acquiring a tool from a remotely located individual and then following the human in a cluttered environment with the tool for future use.

Diftler, M. A.↗
NASA NTRS1996

Planetary Aerobots: A Program for Robotic Balloon Exploration

The exploration of the solar system has proceeded in several phases beginning with flyby missions, proceeding to orbiters, then to probes and landers and finally mobile vehicles that operate on the surface and in its atmosphere. For the most accessible planetary bodies, Venus and Mars, we are now entering the phase of mobile exploration of the surface and atmosphere.

robotically-controlled↗
NASA NTRS2005

Single-Command Approach and Instrument Placement by a Robot on a Target

AUTOAPPROACH is a computer program that enables a mobile robot to approach a target autonomously, starting from a distance of as much as 10 m, in response to a single command. AUTOAPPROACH is used in conjunction with (1) software that analyzes images acquired by stereoscopic cameras aboard the robot and (2) navigation and path-planning software that utilizes odometer readings along with the output of the image-analysis software. Intended originally for application to an instrumented, wheeled robot (rover) in scientific exploration of Mars, AUTOAPPROACH could be adapted to terrestrial applications, notably including the robotic removal of land mines and other unexploded ordnance. A human operator generates the approach command by selecting the target in images acquired by the robot cameras. The approach path consists of multiple legs. Feature points are derived from images that contain the target and are thereafter tracked to correct odometric errors and iteratively refine estimates of the position and orientation of the robot relative to the target on successive legs. The approach is terminated when the robot attains the position and orientation required for placing a scientific instrument at the target. The workspace of the robot arm is then autonomously checked for self/terrain collisions prior to the deployment of the scientific instrument onto the target.

Huntsberger, Terrance↗
NASA NTRS2020

A ROS-based Simulator for Testing the Enhanced Autonomous Navigation of the Mars 2020 Rover

In order to achieve the ambitious objectives of the Mars 2020 (M2020) mission, in particular the ability to autonomously traverse more challenging terrains more efficiently, new surface mobility software was developed for Enhanced Navigation (ENav). That decision was made early in the project, before most of the new surface flight software (FSW) existed, which created a need for a separate framework where the new navigation algorithms could be quickly prototyped and tested, before more realistic FSW-based testbeds became available. The JPL robotics team chose the Robot Operating System [1] (ROS) as the environment in which to test the new ENav algorithms. This made it possible to write the algorithms in the C language required by the FSW, so they could be directly ported over to the flight module later on, while leveraging all the C++ libraries and tools provided by ROS for simulation and testing. The ENav algorithms were developed as a separate C library, and stubs were used to replace any FSW-specific code, such as Event Reporting (EVRs) and data products (DPs). A ROS simulator was developed to generate a rich set of varied 3D terrains representative of the candidate Mars landing sites and simulate the physics of the rover motion, the point cloud perceived by the rover’s stereo vision system, and the new thinking-while-driving (TWD) navigation logic which directs the rover to drive autonomously to user-specified waypoints. To simulate the rover motion and perception, a ROS node was developed that uses a software library called HyperDrive Sim (HDSim), which is a wrapper for the Rover Sequencing and Visualization Program [2] (RSVP). That library provides roverterrain settling, realistic slip modelling, and camera rendering capability based on the rover’s NavCam machine vision models. To simulate the navigation logic, a ROS node was created that initializes and runs the ENav algorithms in a way that mimics the FSW execution, while also providing the capability to load and replay data products, including re-running the recorded inputs through the ENav algorithms for testing. An engineering Graphical User Interface (GUI) was also developed to visualize various elements, such as the rover pose during the drive, the simulated and perceived terrain, the selected local and global paths to the goal, the evaluated candidate paths and the reasons why they were rejected, the keep-in and keep-out zones (KIOZs), etc. Finally, an advanced Monte Carlo (MC) framework that can run many simulations in parallel on the Cloud and automatically generate reports that capture the key ENav performance metrics was developed to evaluate the system in a statisticallymeaningful way. This paper provides an overview of the ROSbased simulator used for testing the M2020 ENav algorithms.

Toupet, Olivier↗
NASA NTRS2010

Evaluating the Performance of Unmanned Ground Vehicle Water Detection

Water detection is a critical perception requirement for unmanned ground vehicle (UGV) autonomous navigation over cross-country terrain. During the Robotics Collaborative Technology Alliances (RCTA) program, the Jet Propulsion Laboratory (JPL) developed a set of water detection algorithms that are used to detect, localize, and avoid water bodies large enough to be a hazard to a UGV. The JPL water detection software performs the detection and localization stages using a forward-looking stereo pair of color cameras. The 3D coordinates of water body surface points are then output to a UGV's autonomous mobility system, which is responsible for planning and executing safe paths. There are three primary methods for evaluating the performance of the water detection software. Evaluations can be performed in image space on the intermediate detection product, in map space on the final localized product, or during autonomous navigation to characterize the avoidance of a variety of water bodies. This paper describes a methodology for performing the first two types of water detection performance evaluations.

stereo vision↗