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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Projected Demand and Potential Impacts to the National Airspace System of Autonomous, Electric, On-Demand Small Aircraft

Electric propulsion and autonomy are technology frontiers that offer tremendous potential to achieve low operating costs for small-aircraft. Such technologies enable simple and safe to operate vehicles that could dramatically improve regional transportation accessibility and speed through point-to-point operations. This analysis develops an understanding of the potential traffic volume and National Airspace System (NAS) capacity for small on-demand aircraft operations. Future demand projections use the Transportation Systems Analysis Model (TSAM), a tool suite developed by NASA and the Transportation Laboratory of Virginia Polytechnic Institute. Demand projections from TSAM contain the mode of travel, number of trips and geographic distribution of trips. For this study, the mode of travel can be commercial aircraft, automobile and on-demand aircraft. NASA's Airspace Concept Evaluation System (ACES) is used to assess NAS impact. This simulation takes a schedule that includes all flights: commercial passenger and cargo; conventional General Aviation and on-demand small aircraft, and operates them in the simulated NAS. The results of this analysis projects very large trip numbers for an on-demand air transportation system competitive with automobiles in cost per passenger mile. The significance is this type of air transportation can enhance mobility for communities that currently lack access to commercial air transportation. Another significant finding is that the large numbers of operations can have an impact on the current NAS infrastructure used by commercial airlines and cargo operators, even if on-demand traffic does not use the 28 airports in the Continental U.S. designated as large hubs by the FAA. Some smaller airports will experience greater demand than their current capacity allows and will require upgrading. In addition, in future years as demand grows and vehicle performance improves other non-conventional facilities such as short runways incorporated into shopping mall or transportation hub parking areas could provide additional capacity and convenience.

Smith, Jeremy C.↗
NASA NTRS1997

Multi-Window Controllers for Autonomous Space Systems

Multi-window controllers select between elementary linear controllers using nonlinear windows based on the amplitude and frequency content of the feedback error. The controllers are relatively simple to implement and perform much better than linear controllers. The commanders for such controllers only order the destination point and are freed from generating the command time-profiles. The robotic missions rely heavily on the tasks of acquisition and tracking. For autonomous and optimal control of the spacecraft, the control bandwidth must be larger while the feedback can (and, therefore, must) be reduced.. Combining linear compensators via multi-window nonlinear summer guarantees minimum phase character of the combined transfer function. It is shown that the solution may require using several parallel branches and windows. Several examples of multi-window nonlinear controller applications are presented.

Lurie, B, J.↗
NASA NTRS1987

Implementing system simulation of C3 systems using autonomous objects

The basis of all conflict recognition in simulation is a common frame of reference. Synchronous discrete-event simulation relies on the fixed points in time as the basic frame of reference. Asynchronous discrete-event simulation relies on fixed-points in the model space as the basic frame of reference. Neither approach provides sufficient support for autonomous objects. The use of a spatial template as a frame of reference is proposed to address these insufficiencies. The concept of a spatial template is defined and an implementation approach offered. Discussed are the uses of this approach to analyze the integration of sensor data associated with Command, Control, and Communication systems.

Rogers, Ralph V.↗
NASA NTRSDate not supplied

Enhancing Neural Network Explainability with Variational Autoencoders

Machine intelligence has been used to tackle increasingly complex problems and deep learning solutions are at the forefront of tackling these problems. In general, these architectures have a great number of parameters that are methodically updated in training. The vast number and complexity of deep neural networks makes it very difficult to decipher the inner workings of the neurons and layers that make up the network. This paper posits that trustworthiness and trust in autonomous systems are increased through eXplainable Artificial Intelligence (XAI) and presents a method that enhances the explainability and understanding of a neural network decision. We leverage variational autoencoders to produce human interpretable features from complex data sets. We show that the explainable features can then be used for machine learning applications. Explainability inspires trust in autonomous systems that use deep learning, which is necessary for safety critical systems.

Loc Tran↗
NASA NTRS2017

An Integrated System for Autonomous Search and Track with a Small Unmanned Aerial Vehicle

This article presents a framework for searching, detecting, and tracking an object of interest with a small unmanned aerial vehicle (sUAV). The vehicle is given an area to search for an object of interest. Once the object is detected, the sUAV follows the target while maintaining a fixed distance and centered on its image plane. This paper describes an architecture for integrating autonomy capabilities for search and track for sUAVs. The system has been implemented in the Robot Operating System (ROS) framework using the Parrot ARDrone platform. In this paper we present our technical approach, the system architecture and demonstrate the principles of the approach in a simulation environment and indoor flight tests.

Unmanned Aerial Vehicle↗
DOE OSTI2024

Object Detection and Recognition with PointPillars in LiDAR Point Clouds – Comparisions

In the field of autonomous systems, neural networks have been leveraged for object detection and recognition in 2-dimensional images captured by cameras. Other types of sensors are available for sensing surroundings, including LiDAR sensors, and corresponding networks have been developed to perform detection and recognition in the point clouds generated by these sensors. The approaches are similar, both perform convolutions, but have distinct characteristics and challenges. In designing and configuring autonomous systems, a variety of LiDAR sensors are available, along with configurable deep neural networks to leverage their data. This work presents a review of the PointPillars network, an evolution of the seminal PointNet, comparing accuracy and training time relative to different LiDAR sensors, network and training parameters, CPU and GPU hardware, and the criticality of the use of reflective intensity as a feature. The value of using reflectivity as a predictive feature is explored and quantified to determine if it makes a significant difference in accuracy of the PointPillars network. Two separate LiDAR sensors are utilized, a 16-plane and a 32-plane, and corresponding accuracies and training times with the PointPillars network are evaluated.

LiDAR, machine learning, neural network, object re↗
NASA NTRS2020

Designing and Training for Appropriate Trust in Increasingly Autonomous Advanced Air Mobility Operations: A Mental Model Approach: Version 1

To enable effective human-autonomy teaming (HAT) in Advanced Air Mobility (AAM) operations, the current paper presents a theoretical framework to design and train for appropriate trust in automation. The novel contribution of this work resides in connecting the construct of trust to mental models and showing how this method could be used to enable emerging HAT concepts such as Adaptive Trust Calibration. To contextualize this framework, in section 2 we discuss simplified vehicle operations (SVO) and remote vehicle operations (RVO), which are leading operational concepts within AAM. In section 3 we describe our perspective on automation and increasingly autonomous systems and present a brief discussion on human-automation interaction and human-autonomy teaming. In section 4 we provide a detailed discussion on the construct of trust in automation. In section 5 we present a framework that associates mental models with trust through principles of transparent design. Finally, in section 6 we present three descriptive models for designing and training for appropriate trust in increasingly autonomous systems.

Human-Autonomy Teaming↗
DOE OSTI2026

Algorithm-guided experimentation for autonomous AI systems in self-driving laboratories

This presentation summarizes our work in the PrOMMiS project on benchmarking of data-driven optimization algorithms and their applications in self-driving laboratories. This work supports the broader project goal of accelerating the identification of promising separation methods and operating conditions for critical minerals separation processes. We present a systematic benchmarking study of 42 data-driven optimization algorithms on a broad collection of 502 test problems. The results identify BAM, GLCCLUSTER, and MULTIMIN as the most effective optimization solvers, with BAM showing the highest overall performance and solving more than 80% of the benchmark problems. The study also shows that no single solver consistently outperforms the others across all problem types, indicating that our future laboratory applications may benefit from using a small set of strong solvers rather than relying on a single method. The presentation also illustrates an in-silico chemical reactor case study showing that data-driven optimization methods can guide autonomous experimentation in a self-driving laboratory and identify optimal operating conditions within a small number of experiments. Overall, the results provide a basis for selecting efficient optimization methods and demonstrate the practical use of data-driven optimization in self-driving laboratory workflows.

36 MATERIALS SCIENCE↗
NASA NTRS2012

Optimal Force Control of Vibro-Impact Systems for Autonomous Drilling Applications

The need to maintain optimal energy efficiency is critical during the drilling operations performed on future and current planetary rover missions (see figure). Specifically, this innovation seeks to solve the following problem. Given a spring-loaded percussive drill driven by a voice-coil motor, one needs to determine the optimal input voltage waveform (periodic function) and the optimal hammering period that minimizes the dissipated energy, while ensuring that the hammer-to-rock impacts are made with sufficient (user-defined) impact velocity (or impact energy). To solve this problem, it was first observed that when voice-coil-actuated percussive drills are driven at high power, it is of paramount importance to ensure that the electrical current of the device remains in phase with the velocity of the hammer. Otherwise, negative work is performed and the drill experiences a loss of performance (i.e., reduced impact energy) and an increase in Joule heating (i.e., reduction in energy efficiency). This observation has motivated many drilling products to incorporate the standard bang-bang control approach for driving their percussive drills. However, the bang-bang control approach is significantly less efficient than the optimal energy-efficient control approach solved herein. To obtain this solution, the standard tools of classical optimal control theory were applied. It is worth noting that these tools inherently require the solution of a two-point boundary value problem (TPBVP), i.e., a system of differential equations where half the equations have unknown boundary conditions. Typically, the TPBVP is impossible to solve analytically for high-dimensional dynamic systems. However, for the case of the spring-loaded vibro-impactor, this approach yields the exact optimal control solution as the sum of four analytic functions whose coefficients are determined using a simple, easy-to-implement algorithm. Once the optimal control waveform is determined, it can be used optimally in the context of both open-loop and closed-loop control modes (using standard realtime control hardware).

Aldrich, Jack B.↗
NASA NTRS1991

Autonomous power expert system advanced development

The autonomous power expert (APEX) system is being developed at Lewis Research Center to function as a fault diagnosis advisor for a space power distribution test bed. APEX is a rule-based system capable of detecting faults and isolating the probable causes. APEX also has a justification facility to provide natural language explanations about conclusions reached during fault isolation. To help maintain the health of the power distribution system, additional capabilities were added to APEX. These capabilities allow detection and isolation of incipient faults and enable the expert system to recommend actions/procedure to correct the suspected fault conditions. New capabilities for incipient fault detection consist of storage and analysis of historical data and new user interface displays. After the cause of a fault is determined, appropriate recommended actions are selected by rule-based inferencing which provides corrective/extended test procedures. Color graphics displays and improved mouse-selectable menus were also added to provide a friendlier user interface. A discussion of APEX in general and a more detailed description of the incipient detection, recommended actions, and user interface developments during the last year are presented.

Quinn, Todd M.↗
NASA NTRS1981

Autonomous navigation system

An inertial navigation system utilizing a servo-controlled two degree of freedom pendulum to obtain specific force components in the locally level coordinate system is described. The pendulum includes a leveling gyroscope and an azimuth gyroscope supported on a two gimbal system. The specific force components in the locally level coordinate system are converted to components in the geographical coordinate system by means of a single Euler transformation. The standard navigation equations are solved to determine longitudinal and lateral velocities. Finally, vehicle position is determined by a further integration.

Merhav, S. J.↗
NASA NTRS1986

The evolution of automation and robotics in manned spaceflight

The evolution of automation on all manned spacecraft including the Space Shuttle is reviewed, and a concept for increasing automation and robotics from the current Shuttle Remote Manipulator System (RMS) to an autonomous system is presented. The requirements for robotic elements are identified for various functions on the Space Station, including extravehicular functions and functions within laboratory and habitation modules which expand man's capacity in space and allow selected teleoperation from the ground. The initial Space Station will employ a telerobot and necessary knowledge based systems as an advisory to the crew on monitoring, fault diagnosis, and short term planning and scheduling.

Moser, T. L.↗
NASA NTRS1988

Technology forecast and applications for autonomous, intelligent systems

Significant research products which have emerged from the core program of NASA's Office of Aeronautics and Space Technology (OAST) are discussed. The Space Station Thermal Control System, the Space Shuttle Integrated Communications Officer Station, the Launch Processing System, the Expert Scheduling System for Pioneer Venus Spacecraft, a Bayesian classification system, and a spaceborne multiprocessor system are included. The technology trends which led to these results are discussed and future developments in technology are forecasted.

Lum, Henry, Jr.↗
NASA NTRS1988

The organization of an autonomous learning system

The organization of systems that learn from experience is examined, human beings and animals being prime examples of such systems. How is their information processing organized. They build an internal model of the world and base their actions on the model. The model is dynamic and predictive, and it includes the systems' own actions and their effects. In modeling such systems, a large pattern of features represents a moment of the system's experience. Some of the features are provided by the system's senses, some control the system's motors, and the rest have no immediate external significance. A sequence of such patterns then represents the system's experience over time. By storing such sequences appropriately in memory, the system builds a world model based on experience. In addition to the essential function of memory, fundamental roles are played by a sensory system that makes raw information about the world suitable for memory storage and by a motor system that affects the world. The relation of sensory and motor systems to the memory is discussed, together with how favorable actions can be learned and unfavorable actions can be avoided. Results in classical learning theory are explained in terms of the model, more advanced forms of learning are discussed, and the relevance of the model to the frame problem of robotics is examined.

Kanerva, Pentti↗
NASA NTRS1988

A CLIPS prototype for autonomous power system control

The model of the system assumes a constant power source and loads (experiments) whose power demands exceed the supply. Experiments are described by their name, power consumption, time for a complete run, present status and the state of the load. The power consumption of each load is set at a constant level but can be dynamically modified by the operator. The status specifies if the experiment is running, paused, completed or failed. The state compensates for the lack of actual feedback sensor data, by signifying the stability of the load. Experiments are scheduled to keep as many running as possible with the current system limitations. A graphics oriented user interface is embedded into the rule-based system to enable an operator to easily experiment with the system.

Vezina, James M.↗