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At least 649 records · Page 36

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NASA NTRSDate not supplied

Dynamic Spectrum Allocation in Urban Air Transportation System via Deep Reinforcement Learning

The emerging concepts of Urban Air Mobility (UAM) and Advanced Air Mobility (AAM) open a new paradigm for urban air transportation. A big challenge is that these new aerial vehicles will quickly saturate the already crowded aviation spectrum, which is an essential resource to ensure reliable communications for safe operations. In this paper, we consider an air transportation system where multiple aerial vehicles are operated to transport passengers or cargo from different sources to destinations along their pre-defined paths. During the flight, the minimum communication Quality of Service (QoS) requirement must be achieved to ensure flight safety. Our objective is to minimize the average mission completion time by jointly optimizing the velocity selection and spectrum allocation for all aerial vehicles. We formulate the optimization problem as a multi-stage Markov Decision Process (MDP) where the optimization variables are coupled together. A multi-agent Deep Reinforcement Learning (DRL) based solution is proposed where Value Decomposition Networks (VDN) algorithm is utilized to take discrete actions. Additionally, we propose a heuristic greedy algorithm as a baseline solution. Simulation results show that our learning based solution outperforms the heuristic greedy algorithm and another Orthogonal Multiple Access (OMA) solution in minimizing the mission completion time.

Ruixuan Han↗

DOE OSTI2022

Methods of recovering lignin and other products from biomass

Methods of recovering lignin products and other products from biomass. The method comprises adding a chemical agent to a stored biomass comprising lignin to form a chemically-treated biomass. The chemical agent comprises an acid, a base, an inorganic salt, or a combination of the inorganic salt and one of the acid or the base. The chemically-treated biomass is stored under anaerobic conditions. Lignin products, such as high molecular weight lignin, medium molecular weight lignin, or low molecular weight lignin, are recovered from the chemically-treated biomass. Additional methods of recovering lignin products and other products from biomass are disclosed.

09 BIOMASS FUELS↗

NASA NTRS2021

Collision Free Dynamic Stability of Formations Using Projection Based Estimators

Transient instability is a phenomenon where inter- agent collisions can occur in dynamically stable formations of autonomous agents. Therefore, the necessity to manage and mitigate transient instabilities is essential for successfully deploying formation structures. This paper develops a novel control architecture that augments the baseline formation maintenance controller to mitigate transient instabilities. At its heart, the proposed architecture consists of a projection operator based estimator disguised as a reference model that generates collision-free trajectories for the agents to follow. This paper’s main theoretical result shows that the proposed control architecture can simultaneously mitigate transient instabilities and guarantee asymptotic convergence of the formation dynamics. Also, an illustrative example demonstrates the theoretical developments presented in this paper.

Bayard, David S.↗

NASA NTRSDate not supplied

Impact of Traffic-Following on Order of Autonomous Airspace Operations

We investigate the dynamic emergence of traffic order in a distributed multi-agent system, aiming to minimize inefficiencies that stem from unnecessary structural impositions. We introduce a methodology for developing a dynamically updating traffic pattern map of the airspace by leveraging information about the consistency and frequency of flow directions used by current as well as preceding traffic. Informed by this map, an agent can discern the degree to which it is advantageous to follow traffic by trading off utilities such as time and order. We show that for the traffic levels studied, for low degrees of traffic-following behavior, there is minimal penalty in terms of aircraft travel times while improving the overall orderliness of the airspace. On the other hand, heightened traffic-following behavior may result in increased aircraft travel times, while marginally reducing the overall entropy of the airspace. Ultimately, the methods and metrics presented in this paper can be used to optimally and dynamically adjust an agent’s traffic-following behavior based on these trade-offs.

Airspace Operations↗

NASA NTRSDate not supplied

Impact of Traffic-Following on Order of Autonomous Airspace Operations

In this paper, we investigate the dynamic emergence of traffic order in a distributed multi-agent system, aiming to minimize inefficiencies that stem from unnecessary structural impositions. We introduce a methodology for developing a dynamically updating traffic pattern map of the airspace by leveraging information about the consistency and frequency of flow directions used by current as well as preceding traffic. Informed by this map, an agent can discern the degree to which it is advantageous to follow traffic by trading off utilities such as time and order. We show that for the traffic levels studied, for low degrees of traffic-following behavior, there is minimal penalty in terms of aircraft travel times while improving the overall orderliness of the airspace. On the other hand, heightened traffic-following behavior may result in increased aircraft travel times, while marginally reducing the overall entropy of the airspace. Ultimately, the methods and metrics presented in this paper can be used to optimally and dynamically adjust an agent’s traffic-following behavior based on these trade-offs.

Airspace Operations↗

NASA NTRS1984

The influence of temperature on the lubricating effectiveness of MoS2 dispersed in mineral oils

The effects of oil viscosity, base oil temperature, and surface-active agents naturally present in mineral oils on the lubricating effectiveness of MoS2 dispersions under boundary lubrication conditions are investigated. Friction and wear data are obtained from tests conducted under a wide range of oil viscosities and operating temperatures. The dispersion temperature at which the friction dropped below that obtained with the base oils, depended upon the base oil viscosity and the concentration of surface-active agents present in the oil. White oils showed reductions in friction before mineral oils of like viscosity, and lower viscosity oils showed reductions in friction before heavier viscosity oils. The results show that for a given base oil, wear increases as temperature increases, while the wear obtained from a MoS2 dispersion made from the base oil remains approximately constant as temperature is increased.

Rolek, R. J.↗

DOE OSTI2022

PowerNet: Multi-agent Deep Reinforcement Learning for Scalable Powergrid Control

This paper develops an efficient multi-agent deep reinforcement learning algorithm for cooperative controls in powergrids. Specifically, we consider the decentralized inverter-based secondary voltage control problem in distributed generators (DGs), which is first formulated as a cooperative multi-agent reinforcement learning (MARL) problem. We then propose a novel on-policy MARL algorithm, PowerNet, in which each agent (DG) learns a control policy based on (sub-)global reward but local states and encoded communication messages from its neighbors. Motivated by the fact that a local control from one agent has limited impact on agents distant from it, we exploit a novel spatial discount factor to reduce the effect from remote agents, to expedite the training process and improve scalability. Furthermore, a differentiable, learning-based communication protocol is employed to foster the collaborations among neighboring agents. In addition, to mitigate the effects of system uncertainty and random noise introduced during on-policy learning, we utilize an action smoothing factor to stabilize the policy execution. To facilitate training and evaluation, we develop PGSim, an efficient, high-fidelity powergrid simulation platform. Here, experimental results in two microgrid setups show that the developed PowerNet outperforms the conventional model-based control method, as well as several state-of-the-art MARL algorithms. The decentralized learning scheme and high sample efficiency also make it viable to large-scale power grids.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Method for generating and dispensing a powdered release agent

A method for generating and dispensing a powdered release agent during an additive manufacturing build is disclosed. A solid body of release agent material is ground insitu by a grinder and dispensed on a surface of the part to prevent adhesion of an adjacent layer of a base material. With the addition of the powdered release agent, a support structure is easily separated from the base material when the part is complete, saving time and preventing the part from sustaining unintentional damage. Since no powdered release agent is actually loaded or stored in the apparatus, the potential for spillage, waste, inconsistent dispensing, inadvertent dispensing, and clumping due to humidity is eliminated.

42 ENGINEERING↗

NASA NTRS2003

Advantages of Brahms for Specifying and Implementing a Multiagent Human-Robotic Exploration System

We have developed a model-based, distributed architecture that integrates diverse components in a system designed for lunar and planetary surface operations: an astronaut's space suit, cameras, all-terrain vehicles, robotic assistant, crew in a local habitat, and mission support team. Software processes ('agents') implemented in the Brahms language, run on multiple, mobile platforms. These mobile agents interpret and transform available data to help people and robotic systems coordinate their actions to make operations more safe and efficient. The Brahms-based mobile agent architecture (MAA) uses a novel combination of agent types so the software agents may understand and facilitate communications between people and between system components. A state-of-the-art spoken dialogue interface is integrated with Brahms models, supporting a speech-driven field observation record and rover command system. An important aspect of the methodology involves first simulating the entire system in Brahms, then configuring the agents into a runtime system Thus, Brahms provides a language, engine, and system builder's toolkit for specifying and implementing multiagent systems.

Clancey, William J.↗

NASA NTRS2004

Apoptosis and Self-Destruct: A Contribution to Autonomic Agents?

Autonomic Computing (AC), a self-managing systems initiative based on the biological metaphor of the autonomic nervous system, is increasingly gaining momentum as the way forward in designing reliable systems. Agent technologies have been identified as a key enabler for engineering autonomicity in systems, both in terms of retrofitting autonomicity into legacy systems and designing new systems. The AC initiative provides an opportunity to consider other biological systems and principles in seeking new design strategies. This paper reports on one such investigation; utilizing the apoptosis metaphor of biological systems to provide a dynamic health indicator signal between autonomic agents.

Sterritt, Roy↗

NASA NTRS2017

Distributed Fast Motion Planning for Spacecraft Swarms in Cluttered Environments using Spherical Expansions and Sequence of Convex Optimization Problems

This paper presents a novel guidance algorithm for spacecraft swarms in an environment cluttered with many obstacles like a debris field or the asteroid belt. The objective of this algorithm is to reconfigure the swarm to a desired formation in a distributed manner while minimizing fuel and avoiding collisions among themselves and with the obstacles. The agents first use a spherical-expansion-based sampling algorithm to cooperatively explore the workspace and find paths to the desired terminal positions. Using a distributed assignment algorithm, the agents converge on an optimal assignment of the target locations in the desired formation. Then each agent generates a locally optimal trajectory from its current location to its terminal position by solving a sequence of convex optimization problems. As the agent moves along this trajectory, it receives the position of other agents and updates its trajectory to avoid collisions with other agents and the obstacles. Thus the swarm achieves the desired formation in a distributed manner while avoiding collisions. Moreover, this algorithm is computationally efficient, therefore it can be implemented onboard resource-constrained spacecraft. Simulations results show that the proposed distributed algorithm can be used by a spacecraft swarm to reconfigure a desired formation around an asteroid in a collision-free manner.

Bandyopadhyay, Saptarshi↗

DOE OSTI2025

Topology-Aware Reinforcement Learning for Voltage Control: Centralized and Decentralized Strategies

Volt-VAR control (VVC) methods based on deep reinforcement learning (DRL) can effectively control distribution grid voltage and minimize power loss by implementing corrective and preventive control measures on the reactive power output of inverter-based distributed energy resources (DERs). However, model-free DRL-based VVC approaches usually cannot capture the important topological feature of the power system since they use a fully-connected network (FCN) to deliver the action. Therefore, this paper proposes a graph convolutional network (GCN)-based DRL approach that can employ the topological information of the network to take better control action for regulating the voltage. Our implementation allows for both centralized and decentralized configurations, utilizing a single agent and multiple agents respectively. Although the centralized GCN-based DRL approach has its advantages of minimizing voltage fluctuation and power loss, it is not suitable for large scale power systems due to its challenges in terms of scalability, computation speed and potential single points of failure. Therefore, these problems can be resolved using the decentralized GCN-based DRL approach. Moreover, to ensure the safe operation of the model, our proposed approach incorporates an exponential barrier function while formulating the reward function for each agent. To validate performance of the proposed approaches, the proposed model is tested on modified IEEE test systems and the performances are measured in terms on voltage fluctuation reduction, minimization of power loss and computational speed. Finally, the results show that the proposed topology-aware approach outperforms the FCN-based DRL approach in terms of reducing voltage fluctuation and minimizing power loss of the network. Moreover, it is shown that the decentralized GCN-based DRL has faster computational speed than other approaches.

42 ENGINEERING↗

NASA NTRS2012

A Markov Chain Approach to Probabilistic Swarm Guidance

This paper introduces a probabilistic guidance approach for the coordination of swarms of autonomous agents. The main idea is to drive the swarm to a prescribed density distribution in a prescribed region of the configuration space. In its simplest form, the probabilistic approach is completely decentralized and does not require communication or collabo- ration between agents. Agents make statistically independent probabilistic decisions based solely on their own state, that ultimately guides the swarm to the desired density distribution in the configuration space. In addition to being completely decentralized, the probabilistic guidance approach has a novel autonomous self-repair property: Once the desired swarm density distribution is attained, the agents automatically repair any damage to the distribution without collaborating and without any knowledge about the damage.

Markov chain↗

NASA NTRS1993

PILOT: An intelligent distributed operations support system

The Real-Time Data System (RTDS) project is exploring the application of advanced technologies to the real-time flight operations environment of the Mission Control Centers at NASA's Johnson Space Center. The system, based on a network of engineering workstations, provides services such as delivery of real time telemetry data to flight control applications. To automate the operation of this complex distributed environment, a facility called PILOT (Process Integrity Level and Operation Tracker) is being developed. PILOT comprises a set of distributed agents cooperating with a rule-based expert system; together they monitor process operation and data flows throughout the RTDS network. The goal of PILOT is to provide unattended management and automated operation under user control.

Rasmussen, Arthur N.↗

DOE OSTI2023

Improved chemical synthesis, identification and evaluation of prospective centrally active oxime antidotes for the treatment of nerve agent exposure

In this study, an improved method for the synthesis of hydroxyiminoacetamide-based oximes, compounds rhat hold great promise as centrally-active nerve agent antidotes, is described. The method involves the coupling of hydroxyiminoacetic acid to various amines in the presence of HOBT or HATU and diisopropylcarbodiimide. The optimized protocol was found to work effectively for the coupling with amines to the activated form of the hydroxyiminoacetic acid with yields ranging between 84 and 90% for primary amines, 65–75% for α-substituted and cyclic amines, while producing decent 41–48% yields for the sterically hindered α,α-substituted amines, that can not be obtained using the previously established method.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Shotgun Immunoproteomic Approach for the Discovery of Linear B-Cell Epitopes in Biothreat Agents Francisella tularensis and Burkholderia pseudomallei

Peptide-based subunit vaccines are coming to the forefront of current vaccine approaches, with safety and cost-effective production among their top advantages. Peptide vaccine formulations consist of multiple synthetic linear epitopes that together trigger desired immune responses that can result in robust immune memory. The advantages of linear compared to conformational epitopes are their simple structure, ease of synthesis, and ability to stimulate immune responses by means that do not require complex 3D conformation. Prediction of linear epitopes through use of computational tools is fast and cost-effective, but typically of low accuracy, necessitating extensive experimentation to verify results. On the other hand, identification of linear epitopes through experimental screening has been an inefficient process that requires thorough characterization of previously identified full-length protein antigens, or laborious techniques involving genetic manipulation of organisms. In this study, we apply a newly developed generalizable screening method that enables efficient identification of B-cell epitopes in the proteomes of pathogenic bacteria. As a test case, we used this method to identify epitopes in the proteome of Francisella tularensis (Ft), a Select Agent with a well-characterized immunoproteome. Our screen identified many peptides that map to known antigens, including verified and predicted outer membrane proteins and extracellular proteins, validating the utility of this approach. We then used the method to identify seroreactive peptides in the less characterized immunoproteome of Select Agent Burkholderia pseudomallei (Bp). This screen revealed known Bp antigens as well as proteins that have not been previously identified as antigens. Although B-cell epitope prediction tools Bepipred 2.0 and iBCE-EL classified many of our seroreactive peptides as epitopes, they did not score them significantly higher than the non-reactive tryptic peptides in our study, nor did they assign higher scores to seroreactive peptides from known Ft or Bp antigens, highlighting the need for experimental data instead of relying on computational epitope predictions alone. The present workflow is easily adaptable to detecting peptide targets relevant to the immune systems of other mammalian species, including humans (depending upon the availability of convalescent sera from patients), and could aid in accelerating the discovery of B-cell epitopes and development of vaccines to counter emerging biological threats.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1991

A framework for building real-time expert systems

The Space Station Freedom is an example of complex systems that require both traditional and artificial intelligence (AI) real-time methodologies. It was mandated that Ada should be used for all new software development projects. The station also requires distributed processing. Catastrophic failures on the station can cause the transmission system to malfunction for a long period of time, during which ground-based expert systems cannot provide any assistance to the crisis situation on the station. This is even more critical for other NASA projects that would have longer transmission delays (e.g., the lunar base, Mars missions, etc.). To address these issues, a distributed agent architecture (DAA) is proposed that can support a variety of paradigms based on both traditional real-time computing and AI. The proposed testbed for DAA is an autonomous power expert (APEX) which is a real-time monitoring and diagnosis expert system for the electrical power distribution system of the space station.

Lee, S. Daniel↗

NASA NTRS2014

Field Programmable Gate Array Apparatus, Method, and Computer Program

An apparatus is provided that includes a plurality of modules, a plurality of memory banks, and a multiplexor. Each module includes at least one agent that interfaces between a module and a memory bank. Each memory bank includes an arbiter that interfaces between the at least one agent of each module and the memory bank. The multiplexor is configured to assign data paths between the at least one agent of each module and a corresponding arbiter of each memory bank based on the assigned data path. The at least one agent of each module is configured to read data from the corresponding arbiter of the memory bank or write modified data to the corresponding arbiter of the memory bank.

Morfopoulos, Arin C.↗