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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

HyspIRI Intelligent Payload Module(IPM) and Benchmarking Algorithms for Upload

Features: Hardware: a) Xilinx Virtex-5 (GSFC Space Cube 2); b) 2 x 400MHz PPC; c) 100MHz Bus; d) 2 x 512MB SDRAM; e) Dual Gigabit Ethernet. Support Linux kernel 2.6.31 (gcc version 4.2.2). Support software running in stand alone mode for better performance. Can stream raw data up to 800 Mbps. Ready for operations. Software Application Examples: Band-stripping Algiotrhmsl:cloud, sulfur, flood, thermal, SWIL, NDVI, NDWI, SIWI, oil spills, algae blooms, etc. Corrections: geometric, radiometric, atmospheric. Core Flight System/dynamic software bus. CCSDS File Delivery Protocol. Delay Tolerant Network. CASPER /onboard planning. Fault monitoring/recovery software. S/C command and telemetry software. Data compression. Sensor Web for Autonomous Mission Operations.

Mandl, Daniel↗
NASA NTRS2010

360-Degree Visual Detection and Target Tracking on an Autonomous Surface Vehicle

This paper describes perception and planning systems of an autonomous sea surface vehicle (ASV) whose goal is to detect and track other vessels at medium to long ranges and execute responses to determine whether the vessel is adversarial. The Jet Propulsion Laboratory (JPL) has developed a tightly integrated system called CARACaS (Control Architecture for Robotic Agent Command and Sensing) that blends the sensing, planning, and behavior autonomy necessary for such missions. Two patrol scenarios are addressed here: one in which the ASV patrols a large harbor region and checks for vessels near a fixed asset on each pass and one in which the ASV circles a fixed asset and intercepts approaching vessels. This paper focuses on the ASV's central perception and situation awareness system, dubbed Surface Autonomous Visual Analysis and Tracking (SAVAnT), which receives images from an omnidirectional camera head, identifies objects of interest in these images, and probabilistically tracks the objects' presence over time, even as they may exist outside of the vehicle's sensor range. The integrated CARACaS/SAVAnT system has been implemented on U.S. Navy experimental ASVs and tested in on-water field demonstrations.

ASV (AUTONOMOUS SEA SURFACE VEHICLE)↗
DOE OSTI2023

HCPerf: Driving Performance-Directed Hierarchical Coordination for Autonomous Vehicles

The rapid development of autonomous driving poses new research challenges to the on-vehicle computing system. In particular, the execution time of autonomous driving tasks highly depends on the specific driving environment. For instance, the execution time of configurable sensor fusion increases significantly as the scene becomes complex, which leads to end-to-end deadline misses from sensing to control and may cause accidents. Thus, a framework that can effectively utilize the system resources to guarantee the end-to-end deadlines of autonomous driving tasks as well as effectively prioritize the responsiveness and throughput of the control commands is crucial for autonomous driving. In this paper, we propose HCPerf, a performance-directed hierarchical coordination framework that intelligently coordinates the autonomous driving tasks with high execution time variation and complex dependencies according to the driving performance in real-time. Specifically, HCPerf mainly consists of two coordinators. The internal coordinator intelligently schedules the tasks according to the driving performance of the vehicle in order to help them meet the end-to-end deadlines while well prioritizing the responsiveness and throughput of the control commands. At the same time, the external coordinator dynamically tunes the rates of tasks according to the schedulability in order to efficiently utilize the system resource. We conduct extensive experiments on both simulation and hardware testbeds with the representative autonomous driving application. The results show that HCPerf can effectively improve the driving performance by 7.69%-45.94% in different driving scenarios.

Ma, Jialiang↗
NASA NTRS1997

Wing Flutter Control

Through Small Business Innovation Research (SBIR) contracts from Langley Research Center, Orbital Research Inc. developed the Orbital Research Intelligent Control Algorithm (ORICA), the first practical hardware-independent adaptive predictive control structure, specifically suited for optimal control of complex, time-varying systems. ORICA technology has been applied to the problem of controlling aircraft wing flutter. Coupled with NASA expertise, the technology has the possibility of making jet travel safer, more cost effective by extending distance range, and lowering overall aircraft operating costs. Future application areas for ORICA include control of robots, power trains, systems with arrays of sensors, or regulating chemical plants or electrical power plant control.

Source record↗
NASA NTRS2008

NASA Tech Briefs, September 2008

Topics covered include: Nanotip Carpets as Antireflection Surfaces; Nano-Engineered Catalysts for Direct Methanol Fuel Cells; Capillography of Mats of Nanofibers; Directed Growth of Carbon Nanotubes Across Gaps; High-Voltage, Asymmetric-Waveform Generator; Magic-T Junction Using Microstrip/Slotline Transitions; On-Wafer Measurement of a Silicon-Based CMOS VCO at 324 GHz; Group-III Nitride Field Emitters; HEMT Amplifiers and Equipment for their On-Wafer Testing; Thermal Spray Formation of Polymer Coatings; Improved Gas Filling and Sealing of an HC-PCF; Making More-Complex Molecules Using Superthermal Atom/Molecule Collisions; Nematic Cells for Digital Light Deflection; Improved Silica Aerogel Composite Materials; Microgravity, Mesh-Crawling Legged Robots; Advanced Active-Magnetic-Bearing Thrust- Measurement System; Thermally Actuated Hydraulic Pumps; A New, Highly Improved Two-Cycle Engine; Flexible Structural-Health-Monitoring Sheets; Alignment Pins for Assembling and Disassembling Structures; Purifying Nucleic Acids from Samples of Extremely Low Biomass; Adjustable-Viewing-Angle Endoscopic Tool for Skull Base and Brain Surgery; UV-Resistant Non-Spore-Forming Bacteria From Spacecraft-Assembly Facilities; Hard-X-Ray/Soft-Gamma-Ray Imaging Sensor Assembly for Astronomy; Simplified Modeling of Oxidation of Hydrocarbons; Near-Field Spectroscopy with Nanoparticles Deposited by AFM; Light Collimator and Monitor for a Spectroradiometer; Hyperspectral Fluorescence and Reflectance Imaging Instrument; Improving the Optical Quality Factor of the WGM Resonator; Ultra-Stable Beacon Source for Laboratory Testing of Optical Tracking; Transmissive Diffractive Optical Element Solar Concentrators; Delaying Trains of Short Light Pulses in WGM Resonators; Toward Better Modeling of Supercritical Turbulent Mixing; JPEG 2000 Encoding with Perceptual Distortion Control; Intelligent Integrated Health Management for a System of Systems; Delay Banking for Managing Air Traffic; and Spline-Based Smoothing of Airfoil Curvatures.

Source record↗
DOE OSTI2022

A 75 kVA Intelligent Power Stages (IPS) Unit Design for Future Grid-interface Application

The demand for grid-interface converter with flexible dc bus is driven by the fast development of energy storage system (ESS), electric vehicle (EV) charging system, and versatile residential load. This paper first proposes a two-stage intelligent power stage (IPS) unit for future grid-interface applications. To achieve zero common-mode (CM) voltage emissions, step-up and step-down voltage gain, a 3-level four-switch buck-boost (3-L FSBB) dc-dc converter is adopted with fully zero voltage switching (ZVS). In one IPS unit, an advanced fiberoptic communication with sub-nanosecond synchronization is developed for different enhanced gate drivers. Moreover, an enhanced gate driver integrated current and voltage sensors is designed for monitoring and diagnostics. To achieve a whole low-profile design, three 12 oz heavy copper planar ac inductors are designed and implemented. Finally, a 75 kVA IPS unit was built to verify both the thermal and electrical design.

Cao, Yuliang↗
NASA NTRS2012

A Spacecraft Housekeeping System-on-Chip in a Radiation Hardened Structured ASIC

Housekeeping systems are essential to health monitoring of spacecraft and instruments. Typically, sensors are distributed across various sub-systems and data is collected using components such as analog-to-digital converters, analog multiplexers and amplifiers. In most cases programmable devices are used to implement the data acquisition control and storage, and the interface to higher level systems. Such discrete implementations require additional size, weight, power and interconnect complexity versus an integrated circuit solution, as well as the qualification of multiple parts. Although commercial devices are readily available, they are not suitable for space applications due the radiation tolerance and qualification requirements. The Housekeeping System-o n-A-Chip (HKSOC) is a low power, radiation hardened integrated solution suitable for spacecraft and instrument control and data collection. A prototype has been designed and includes a wide variety of functions including a 16-channel analog front-end for driving and reading sensors, analog-to-digital and digital-to-analog converters, on-chip temperature sensor, power supply current sense circuits, general purpose comparators and amplifiers, a 32-bit processor, digital I/O, pulse-width modulation (PWM) generators, timers and I2C master and slave serial interfaces. In addition, the device can operate in a bypass mode where the processor is disabled and external logic is used to control the analog and mixed signal functions. The device is suitable for stand-alone or distributed systems where multiple chips can be deployed across different sub-systems as intelligent nodes with computing and processing capabilities.

Suarez, George↗
DOE OSTI2025

Machine learning model inputs, outputs, and scripts associated with “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions”

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions” (Malhotra et al., in prep). This effort was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the contiguous United States (CONUS). New machine learning models were created every month to guide sampling locations. Data from the resulting samples were used to test and rebuild the machine learning models for the next round of sampling guidance. Associated sediment and water geochemistry and in situ sensor data can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1923689, https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1729719, and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1603775. This data package is associated with two GitHub repositories found at https://github.com/parallelworks/dynamic-learning-rivers and https://github.com/WHONDRS-Hub/ICON-ModEx_Open_Manuscript. In addition to this readme, this data package also includes two file-level metadata (FLMD) files that describes each file and two data dictionaries (DD) that describe all column/row headers and variable definitions. This data package consists of two main folders (1) dynamic-learning-rivers and (2) ICON-ModEx_Open_Manuscript which contain snapshots of the associated GitHub repositories. The input data, output data, and machine learning models used to guide sampling locations are within dynamic-learning-rivers. The folder is organized into five top-level directories: (1) “input_data” holds the training data for the ML models; (2) “ml_models” holds machine learning (ML) models trained on the data in “input_data”; (3) “examples” contains files for direct experimentation with the machine learning model, including scripts for setting up “hindcast” run; (4) “scripts” contains data preprocessing and postprocessing scripts and intermediate results specific to this data set that bookend the ML workflow; and (5) “output_data” holds the overall results of the ML model on that branch. Each trained ML model resides on its own branch in the repository; this means that inputs and outputs can be different branch-to-branch. There is also one hidden directory “.github/workflows”. This hidden directory contains information for how to run the ML workflow as an end-to-end automated GitHub Action but it is not needed for reusing the ML models archived here. Please see the top-level README.md in the GitHub repository for more details on the automation. The scripts and data used to create figures in the manuscript are within ICON-ModEx_Open_Manuscript. The folder is organized into four folders which contain the scripts, data, and pdf for each figure. Within the “fig-model-score-evolution” folder, there is a folder called “intermediate_branch_data” which contains some intermediate files pulled from dynamic-learning-rivers and reorganized to easily integrate into the workflows. NOTE: THIS FOLDER INCLUDES THE FILES AT THE POINT OF PAPER SUBMISSION. IT WILL BE UPDATED ONCE THE PAPER IS ACCEPTED WITH ANY REVISIONS AND WILL INCLUDE A DD/FLMD AT THAT POINT. We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Washington State Parks and Recreation Commission (Scientific Research Permit #210901), and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the samples labeled “SSS” were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview. WHONDRS consortium members were asked to provide any acknowledgments for the collection of samples labeled “CM” and the following is a list of acknowledgments that were submitted with their corresponding Site IDs: (MART) Research activities were conducted in part on the Wind River Experimental Forest within the Gifford Pinchot National Forest; (MP- 100379) Philadelphia is part of Lenapehoking, the ancestral homelands of the Lenape peoples; (MP-102398) Land surveyed is the ancestral homelands of the Nookhose'iinenno (Arapaho), Tsis tsis'tas (Cheyenne), and Nuuchu (Ute); (MP-100749 and MP- 100747) Georgia Coastal Ecosystem LTER, OCE-1832178; (SP-70 and SP-72) Eastern Shoshone, Shoshone-Bannock; (MP- 102944) Funded by Oregon Watershed Enhancement Board. On the traditional lands of the Confederated Tribes of the Siletz, Confederated Tribes of the Grand Rhonde, and the Clatsop-Nehalem Confederated Tribe; (MP- 100607) Holiday Creek is located on the traditional territory of the Monacan Indian Nation; (SP-45) Lafayette Blue Springs State Park; (MP-102420) NSF DEB-2016749; (MP-100019) New Hampshire Agriculture Experiment Station; (SP-35) Rayonier (land owner; https://www.rayonier.com/); (MP- 101276) US Department of Energy, Office of Science, Biological and Environmental Research, Subsurface Biogeochemical Research, Watershed Dynamics and Evolution SFA at ORNL; (MP- 103224) Watershed Dynamics and Evolution SFA at ORNL; (MP- 101584) Traditional lands of the Oceti Sakowin (Dakota, Lakota, Nakoda) and Anishinaabe Peoples.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1992

An Introduction to Intelligent Processing Programs Developed by the Air Force Manufacturing Technology Directorate

The Air Force has numerous on-going manufacturing and integration development programs (machine tools, composites, metals, assembly, and electronics) which are instrumental in improving productivity in the aerospace industry, but more importantly, have identified strategies and technologies required for the integration of advanced processing equipment. An introduction to four current Air Force Manufacturing Technology Directorate (ManTech) manufacturing areas is provided. Research is being carried out in the following areas: (1) machining initiatives for aerospace subcontractors which provide for advanced technology and innovative manufacturing strategies to increase the capabilities of small shops; (2) innovative approaches to advance machine tool products and manufacturing processes; (3) innovative approaches to advance sensors for process control in machine tools; and (4) efforts currently underway to develop, with the support of industry, the Next Generation Workstation/Machine Controller (Low-End Controller Task).

Sampson, Paul G.↗
NASA NTRSDate not supplied

The Wizard Staff: A Mobile Tool Supporting Human Exploration on The Lunar Surface

The Wizard Staff is a rechargeable, battery powered mobile tool with multiple uses for lunar surface exploration. It supports human activity as a surface survey tool, provides external illumination to enhance safety, delivers networking, communications, and navigation features to remote sites, and assists crew rescue with an emergency beacon and rigid splint structure. As a survey tool, it assists crewed geologic field site exploration with external lights, cameras, and LiDAR to illuminate, map, and record the sample collection process. As a relay station, it provides a surface intranet communications and navigation node for extended exploration far from the lander or habitation module. A separately developed Search-and-Rescue (SAR) handheld device can broadcast a signal to the Gandalf Staff containing crew position and biometric data. The staff then formats the message and sends it orbiting satellite(s) for emergency response via the LunaNET on a dedicated frequency similar to terrestrial applications using an Emergency Position-Indicating Radiobeacon (EPIRB). The staff is designed with an upper and lower segment. Components in the upper segment are the LiDAR, external lighting arrays, cameras, communications equipment, intelligence devices, and user interface for crew operations. The lower segment is a set of FeLiPO4 battery cells packed into the 2” diameter carbon fiber tube. When the battery pack is discharged, a spring connector (designed for the dusty lunar environment) easily disconnects the two segments for replacement with another fully charged battery pack. The lower segment can be used for Incapacitated Crew Rescue (ICR) as a rigid splint. As a stand-alone science platform, the Wizard Staff provides infrastructure for instruments and sensors to record the lunar environment over long periods of time. After a crew installs the components on the lunar surface, the platform delivers data to the lunar lander or base station for analysis. The stand-alone mode requires an auxiliary power source (e.g. solar array) and energy storage system (e.g. battery), so it could also be used as an electrical recharging station. A working prototype of Wizard Staff has been developed with collaboration between NASA (JSC, MSFC, and GSFC), Texas Space Technology Applications and Research (T STAR), and Texas A&M University (TAMU) using $300k of innovation funding in FY’21 and FY’22. The functional staff weights 36# on Earth (6# on the Moon), stands 2.1m tall and delivers external lighting with an array of 40 LED bulbs generating 1460 Lux at 2m range (near field), or 2 Lux at 50m (far field).

Gandalf Staff↗
NASA NTRSDate not supplied

Autonomous Amphibious Rescue System

The US Marines are in the process of retiring 900+ Assault Amphibious Vehicles (AAVs) and replacing them with new Amphibious Combat Vehicles ACVs. There is the potential to convert “swords into ploughshares” in this transition, by upgrading and repurposing the retiring AAVs into a national civilian asset. Add Advanced Sensors & Communications to Enhance Safety and Search Capabilities. Add Remote Operations Capabilities. Add Autonomous Operations Capabilities. Launch & Recovery Platform: Equip with Remote/Autonomous: 1. Deployable airborne systems 2. Deployable ground systems 3. Deployable marine systems to support Search and Rescue Missions in Extreme Conditions.

Remote Capability↗
DOE OSTI2024

Data‐driven variational method for discrepancy modeling: Dynamics with small‐strain nonlinear elasticity and viscoelasticity

Abstract The effective inclusion of a priori knowledge when embedding known data in physics‐based models of dynamical systems can ensure that the reconstructed model respects physical principles, while simultaneously improving the accuracy of the solution in the previously unseen regions of state space. This paper presents a physics‐constrained data‐driven discrepancy modeling method that variationally embeds known data in the modeling framework. The hierarchical structure of the method yields fine scale variational equations that facilitate the derivation of residuals which are comprised of the first‐principles theory and sensor‐based data from the dynamical system. The embedding of the sensor data via residual terms leads to discrepancy‐informed closure models that yield a method which is driven not only by boundary and initial conditions, but also by measurements that are taken at only a few observation points in the target system. Specifically, the data‐embedding term serves as residual‐based least‐squares loss function, thus retaining variational consistency. Another important relation arises from the interpretation of the stabilization tensor as a kernel function, thereby incorporating a priori knowledge of the problem and adding computational intelligence to the modeling framework. Numerical test cases show that when known data is taken into account, the data driven variational (DDV) method can correctly predict the system response in the presence of several types of discrepancies. Specifically, the damped solution and correct energy time histories are recovered by including known data in the undamped situation. Morlet wavelet analyses reveal that the surrogate problem with embedded data recovers the fundamental frequency band of the target system. The enhanced stability and accuracy of the DDV method is manifested via reconstructed displacement and velocity fields that yield time histories of strain and kinetic energies which match the target systems. The proposed DDV method also serves as a procedure for restoring eigenvalues and eigenvectors of a deficient dynamical system when known data is taken into account, as shown in the numerical test cases presented here.

Masud, Arif↗
NASA NTRS1987

Monovision techniques for telerobots

The primary task of the vision sensor in a telerobotic system is to provide information about the position of the system's effector relative to objects of interest in its environment. The subtasks required to perform the primary task include image segmentation, object recognition, and object location and orientation in some coordinate system. The accomplishment of the vision task requires the appropriate processing tools and the system methodology to effectively apply the tools to the subtasks. The functional structure of the telerobotic vision system used in the Langley Research Center's Intelligent Systems Research Laboratory is discussed as well as two monovision techniques for accomplishing the vision subtasks.

Goode, P. W.↗
NASA NTRS2012

U.S. Government Open Internet Access to Sub-meter Satellite Data

The National Geospatial-Intelligence Agency (NGA) has contracted United States commercial remote sensing companies GeoEye and Digital Globe to provide very high resolution commercial quality satellite imagery to federal/state government agencies and those projects/people who support government interests. Under NextView contract terms, those engaged in official government programs/projects can gain online access to NGA's vast global archive. Additionally, data from vendor's archives of IKONOS-2 (IK-2), OrbView-3 (OB-3), GeoEye-1 (GE-1), QuickBird-1 (QB-1), WorldView-1 (WV-1), and WorldView-2 (WV-2), sensors can also be requested under these agreements. We report here the current extent of this archive, how to gain access, and the applications of these data by Earth science investigators to improve discoverability and community use of these data. Satellite commercial quality imagery (CQI) at very high resolution (< 1 m) (here after referred to as CQI) over the past decade has become an important data source to U.S. federal, state, and local governments for many different purposes. The rapid growth of free global CQI data has been slow to disseminate to NASA Earth Science community and programs such as the Land-Cover Land-Use Change (LCLUC) program which sees potential benefit from unprecedented access. This article evolved from a workshop held on February 23rd, 2012 between representatives from NGA, NASA, and NASA LCLUC Scientists discussion on how to extend this resource to a broader license approved community. Many investigators are unaware of NGA's archive availability or find it difficult to access CQI data from NGA. Results of studies, both quality and breadth, could be improved with CQI data by combining them with other moderate to coarse resolution passive optical Earth observation remote sensing satellites, or with RADAR or LiDAR instruments to better understand Earth system dynamics at the scale of human activities. We provide the evolution of this effort, a guide for qualified user access, and describe current to potential use of these data in earth science.

Neigh, Christopher S. R>↗
NASA NTRS1992

Logic programming and metadata specifications

Artificial intelligence (AI) ideas and techniques are critical to the development of intelligent information systems that will be used to collect, manipulate, and retrieve the vast amounts of space data produced by 'Missions to Planet Earth.' Natural language processing, inference, and expert systems are at the core of this space application of AI. This paper presents logic programming as an AI tool that can support inference (the ability to draw conclusions from a set of complicated and interrelated facts). It reports on the use of logic programming in the study of metadata specifications for a small problem domain of airborne sensors, and the dataset characteristics and pointers that are needed for data access.

Lopez, Antonio M., Jr.↗
NASA NTRS1987

Monovision techniques for telerobots

The primary task of the vision sensor in a telerobotic system is to provide information about the position of the system's effector relative to objects of interest in its environment. The subtasks required to perform the primary task include image segmentation, object recognition, and object location and orientation in some coordinate system. The accomplishment of the vision task requires the appropriate processing tools and the system methodology to effectively apply the tools to the subtasks. This paper describes the functional structure of the telerobotic vision system used in the Langley Research Center's (LaRC) Intelligent Systems Research Laboratory (ISRL) and discusses two monovision techniques for accomplishing the vision subtasks.

Goode, Plesent W.↗
NASA NTRS2004

Human-Robot Planetary Exploration Teams

The EVA Robotic Assistant (ERA) project at NASA Johnson Space Center studies human-robot interaction and robotic assistance for future human planetary exploration. Over the past four years, the ERA project has been performing field tests with one or more four-wheeled robotic platforms and one or more space-suited humans. These tests have provided experience in how robots can assist humans, how robots and humans can communicate in remote environments, and what combination of humans and robots works best for different scenarios. The most efficient way to understand what tasks human explorers will actually perform, and how robots can best assist them, is to have human explorers and scientists go and explore in an outdoor, planetary-relevant environment, with robots to demonstrate what they are capable of, and roboticists to observe the results. It can be difficult to have a human expert itemize all the needed tasks required for exploration while sitting in a lab: humans do not always remember all the details, and experts in one arena may not even recognize that the lower level tasks they take for granted may be essential for a roboticist to know about. Field tests thus create conditions that more accurately reveal missing components and invalid assumptions, as well as allow tests and comparisons of new approaches and demonstrations of working systems. We have performed field tests in our local rock yard, in several locations in the Arizona desert, and in the Utah desert. We have tested multiple exploration scenarios, such as geological traverses, cable or solar panel deployments, and science instrument deployments. The configuration of our robot can be changed, based on what equipment is needed for a given scenario, and the sensor mast can even be placed on one of two robot bases, each with different motion capabilities. The software architecture of our robot is also designed to be as modular as possible, to allow for hardware and configuration changes. Two focus areas of our research are safety and crew time efficiency. For safety, our work involves enabling humans to reliably communicate with a robot while moving in the same workspace, and enabling robots to monitor and advise humans of potential problems. Voice, gesture, remote computer control, and enhanced robot intelligence are methods we are studying. For crew time efficiency, we are investigating the effects of assigning different roles to humans and robots in collaborative exploration scenarios.

Tyree, Kimberly↗