Results for “Multiagent systems” — Page 2

NASA NTRS2020

Dynamic Stability And Adaptive Control of Networked Evolving Formations with Weak Nonlinearities

The dynamic stability of formation geometry is vital to the design of large scale multiagent systems. In this paper, we probe into the structure of the formation system matrix using the Laplacian of a digraph to develop several fundamental theoretical results on the stability of formation geometry. Our key-results include the integration of the graph Laplacian into linear and weak-nonlinear relative dynamics, the development of several new coordinate transformations that expose the influence of the graph Laplacian matrix on the control laws of the agents, and the use of direct adaptive control as stability restoring devices. We also develop two fundamental results that provide upper bounds for stable formation evolution under nonlinear perturbations of agent dynamics. Finally, we use an illustrative example to demonstrate our theoretical findings.

Gehlot, Vinod P.↗

NASA NTRSDate not supplied

Discovery and Analysis of Rare High-Impact Failure Modes using Adversarial RL-Informed Sampling

Adaptive learning agents have tremendous potential to handle critical tasks currently performed by humans. Unfortunately, due to their complexity, it can be difficult to verify that these learning agents do not have critical failure modes. Standard verification and validation methods often do not apply directly to learning agents and Monte Carlo methods have difficulty covering even a small fraction of the state space, especially in multiagent systems or over long time horizons. To overcome this difficulty, we demonstrate an adaptive stress-testing method based on reinforcement learning of correlations that raise the probability of failure. This approach has three key properties: (1) it is able to find rare failure modes with far greater sample efficiency than Monte Carlo methods, (2) it can estimate the true probability of a failure mode despite the inherent bias in the learning method, and (3) it is capable of learning and resampling compact representations of multimodal failure spaces. These properties are important in practice as we need to find disparate failure modes while accounting for their actual relevance. This is a significant advantage over traditional adaptive stress testing methods that give abstract likelihoods of particular failure instances, but cannot estimate the probability of a broader failure mode. We test our algorithm on a simple problem from the aviation domain where an autonomous aircraft lands in gusty wind conditions. The results suggest that we can find failure modes with far fewer samples than the Monte Carlo approach and simultaneously estimate the probability of failure.

reinforcement learning↗

NASA NTRSDate not supplied

Discovery and Analysis of Rare High-Impact Failure Modes using Adversarial RL-Informed Sampling

Adaptive learning agents have tremendous potential to handle critical tasks currently performed by humans. Unfortunately, due to their complexity, it can be difficult to verify that these learning agents do not have critical failure modes. Standard verification and validation methods often do not apply directly to learning agents and Monte Carlo methods have difficulty covering even a small fraction of the state space, especially in multiagent systems or over long time horizons. To overcome this difficulty, we demonstrate an adaptive stress-testing method based on reinforcement learning of correlations that raise the probability of failure. This approach has three key properties: (1) it is able to find rare failure modes with far greater sample efficiency than Monte Carlo methods, (2) it can estimate the true probability of a failure mode despite the inherent bias in the learning method, and (3) it is capable of learning and resampling compact representations of multimodal failure spaces. These properties are important in practice as we need to find disparate failure modes while accounting for their actual relevance. This is a significant advantage over traditional adaptive stress testing methods that give abstract likelihoods of particular failure instances, but cannot estimate the probability of a broader failure mode. We test our algorithm on a simple problem from the aviation domain where an autonomous aircraft lands in gusty wind conditions. The results suggest that we can find failure modes with far fewer samples than the Monte Carlo approach and simultaneously estimate the probability of failure.

Validation↗

NASA NTRSDate not supplied

Discovery and Analysis of Rare High-Impact Failure Modes using Adversarial RL-Informed Sampling

Adaptive learning agents have tremendous potential to handle critical tasks currently performed by humans. Unfortunately, due to their complexity, it can be difficult to verify that these learning agents do not have critical failure modes. Standard verification and validation methods often do not apply directly to learning agents and Monte Carlo methods have difficulty covering even a small fraction of the state space, especially in multiagent systems or over long time horizons. To overcome this difficulty, we demonstrate an adaptive stress-testing method based on reinforcement learning of correlations that raise the probability of failure. This approach has three key properties: (1) it is able to find rare failure modes with far greater sample efficiency than Monte Carlo methods, (2) it can estimate the true probability of a failure mode despite the inherent bias in the learning method, and (3) it is capable of learning and resampling compact representations of multimodal failure spaces. These properties are important in practice as we need to find disparate failure modes while accounting for their actual relevance. This is a significant advantage over traditional adaptive stress testing methods that give abstract likelihoods of particular failure instances, but cannot estimate the probability of a broader failure mode. We test our algorithm on a simple problem from the aviation domain where an autonomous aircraft lands in gusty wind conditions. The results suggest that we can find failure modes with far fewer samples than the Monte Carlo approach and simultaneously estimate the probability of failure.

Validation↗

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 NTRS2005

Control Algorithms and Simulated Environment Developed and Tested for Multiagent Robotics for Autonomous Inspection of Propulsion Systems

The NASA Glenn Research Center and academic partners are developing advanced multiagent robotic control algorithms that will enable the autonomous inspection and repair of future propulsion systems. In this application, on-wing engine inspections will be performed autonomously by large groups of cooperative miniature robots that will traverse the surfaces of engine components to search for damage. The eventual goal is to replace manual engine inspections that require expensive and time-consuming full engine teardowns and allow the early detection of problems that would otherwise result in catastrophic component failures. As a preliminary step toward the long-term realization of a practical working system, researchers are developing the technology to implement a proof-of-concept testbed demonstration. In a multiagent system, the individual agents are generally programmed with relatively simple controllers that define a limited set of behaviors. However, these behaviors are designed in such a way that, through the localized interaction among individual agents and between the agents and the environment, they result in self-organized, emergent group behavior that can solve a given complex problem, such as cooperative inspection. One advantage to the multiagent approach is that it allows for robustness and fault tolerance through redundancy in task handling. In addition, the relatively simple agent controllers demand minimal computational capability, which in turn allows for greater miniaturization of the robotic agents.

Wong, Edmond↗

NASA NTRS2017

Machine Learning for Slow but Steady Interplanetary Construction

For prolonged manned missions to destinations such as the moon and Mars, there is a need for significant infrastructure construction ahead of time, such as habitats and landing pads. Unfortunately we have little experience in remote construction and using conventional methods is likely to be expensive, cumbersome and unreliable. Fortunately these challenges may be overcome by taking advantage of the long lead time for such missions and using teams of small and slow construction robots. We propose using teams of simple autonomous robots for this purpose that would perform continuous construction over a period of many years or even decades. While individual robot reliability will be low over such long time frames, system reliability will be maintained by using machine learning over simulations to achieve coordination and reconfigurations in the event of lost robots.

Tensegrity robots↗

NASA NTRS2014

Evolutionary Agent-Based Simulation of the Introduction of New Technologies in Air Traffic Management

Accurate simulation of the effects of integrating new technologies into a complex system is critical to the modernization of our antiquated air traffic system, where there exist many layers of interacting procedures, controls, and automation all designed to cooperate with human operators. Additions of even simple new technologies may result in unexpected emergent behavior due to complex human/ machine interactions. One approach is to create high-fidelity human models coming from the field of human factors that can simulate a rich set of behaviors. However, such models are difficult to produce, especially to show unexpected emergent behavior coming from many human operators interacting simultaneously within a complex system. Instead of engineering complex human models, we directly model the emergent behavior by evolving goal directed agents, representing human users. Using evolution we can predict how the agent representing the human user reacts given his/her goals. In this paradigm, each autonomous agent in a system pursues individual goals, and the behavior of the system emerges from the interactions, foreseen or unforeseen, between the agents/actors. We show that this method reflects the integration of new technologies in a historical case, and apply the same methodology for a possible future technology.

Simulation↗

DOE OSTI2021

Multiagent-Based Dynamic Voltage Support of Power Converters During Fault Ride-Through

Reference point of applicability (RPA) is a predefined point in a distribution network that performance requirements shall apply. As RPA is not directly controlled by a dedicated distributed energy resource (DER), it requires an aggregate response of multiple DERs over a complex meshed network. Here, this article presents a multiagent-based distributed dynamic voltage support (DDVS) method for coordinated aggregate response of multiple DER units to support RPA voltage profile during and after fault ride-through. DDVS implements a multiagent-based leader target tracking scheme with velocity matching and flock centering features to avoid uncoordinated current injections of cooperative DERs. The DDVS is an event-based approach based on RPA requirements. Two-dimensional voltage support objective is handled: 1) coordinated positive sequence current injection for voltage magnitude boosting and 2) coordinated negative sequence current injection for unbalance voltage mitigation. The results demonstrate that the proposed DDVS algorithm addresses emerging coordinated DVS requirement of DERs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2021

Inertial Transfer Concept for Autonomous In-Space Assembly

Rocket payload and fairing size have placed strict mass and volume limitations on single launch in-space structures. These limitations are what motivates In-Space Assembly (ISA) and where Inertial Transfer has potential to improve or augment ISA capabilities by utilizing a multiagent autonomous system to transport untethered payloads. The Inertial Transfer concept is presented including autonomy capabilities, potential risks, and three system configurations. These configurations are based on the autonomous system’s available sensor coverage that tracks the payload’s state. Participating agents have well-defined roles, expectations, and assumptions in their physical organization and coordination of actions depending the mode of operation. The sensor information available to participating agents is considered as it affects the uncertainty in the estimated state and the actions required to capture the payload.

Multi-Agent↗

NASA NTRS2001

Multiagent Modeling and Simulation in Human-Robot Mission Operations Work System Design

This paper describes a collaborative multiagent modeling and simulation approach for designing work systems. The Brahms environment is used to model mission operations for a semi-autonomous robot mission to the Moon at the work practice level. It shows the impact of human-decision making on the activities and energy consumption of a robot. A collaborative work systems design methodology is described that allows informal models, created with users and stakeholders, to be used as input to the development of formal computational models.

Sierhuis, Maarten↗

NASA NTRSDate not supplied

APIS: Honeybee Foraging Task Assignment for Use in Uncertain and Unreliable Environments

Multiagent Cyber-Physical-Human (CPH) systems in realistic environments operate under uncertain conditions. Communication among agents, aimed at reducing the uncertainty, is itself subject to uncertainty. We propose to manage uncertainties in autonomous, long-duration operations of multiagent systems via a modified Honeybee Foraging (HBF) behavioral scheme. The resulting system, Autonomous Persistent Intelligent Swarm (APIS),incorporates two new behaviors to ameliorate informational uncertainty. When “scouting”, agents are tasked based on informational quality and reliability rather than solely on priorities. When “dancing”, agents are tasked to rendezvous with other dancing agents to exchange information at close range, where successful communication is guaranteed. When coupled with uncertainty-aware modeling across agents, these behaviors improve situational awareness and resilience of the system, enabling it to function under more uncertain conditions arising during long-duration missions.

Autonomous systems↗

NASA NTRSDate not supplied

APIS: Honeybee Foraging Task Assignment for Use in Uncertain and Unreliable Environments

Multiagent Cyber-Physical-Human (CPH) systems in realistic environments operate under uncertain conditions. Communication among agents, aimed at reducing the uncertainty, is itself subject to uncertainty. We propose to manage uncertainties in autonomous, long-duration operations of multiagent systems via a modified Honeybee Foraging (HBF) behavioral scheme. The resulting system, Autonomous Persistent Intelligent Swarm (APIS),incorporates two new behaviors to ameliorate informational uncertainty. When “scouting”, agents are tasked based on informational quality and reliability rather than solely on priorities. When “dancing”, agents are tasked to rendezvous with other dancing agents to exchange information at close range, where successful communication is guaranteed. When coupled with uncertainty-aware modeling across agents, these behaviors improve situational awareness and resilience of the system, enabling it to function under more uncertain conditions arising during long-duration missions.

Autonomous systems↗

DOE OSTI2022

Internship Report - Andrew Beathard

This summer, I had the opportunity to work as an intern under Ryan Goldhahn at Lawrence Livermore National Laboratory. Alongside another intern, I was tasked with implementing perception and autonomy algorithms on unmanned aerial systems (UAS) for multiagent cooperative missions and testing their effectiveness at the OS-150 UAS test facility at LLNL. We aimed to engineer a collaborative reaction to a single agent’s identification of an object of interest and test various multiagent algorithms.

42 ENGINEERING↗

DOE OSTI2021

A Novel Multiagent Resource Sharing Algorithm for Control of Advanced Energy Systems

This paper implements a novel resource sharing control strategy on a fuel cell–gas turbine hybrid power system at the National Energy Technology Laboratory’s Hybrid Performance Facility (Hyper). In a fuel cell–gas turbine hybrid power system, the simultaneous interaction of the gas turbine and the fuel cell creates a tightly coupled environment characterized by conflicting dynamics. In this paper, a model-free control approach is applied to solve the tightly coupled control problem posed by this challenging environment. Specifically, this control problem is presented as a resource sharing problem that can be solved using a resource sharing algorithm that is defined based on the distribution construction concept. This algorithm creates computational agents and solves the problem through the distribution and redistribution of shared resources defined as blocks. Furthermore, two agents were created; the first agent (agent 1) controls the gas turbine speed by adjusting the electric load, and the second agent (agent 2) controls the cathode mass flow through the fuel cell using the cold-air bypass valve. A parametric study was performed over the course of 15 experimental tests for both agents 1 and 2 through an evaluation of the responses based on setpoint changes. The algorithm was shown to have behavior comparable to a previously implemented multi-input multioutput state-space controller, which was designed through a model-based control approach. The resource sharing algorithm was able to find stable performance during run-time operations without any prior system knowledge identification on the power plant and without creating models used in traditional control strategies.

25 ENERGY STORAGE↗

NASA NTRS2000

Applying Multiagent Simulation to Planetary Surface Operations

This paper describes a multiagent modeling and simulation approach for designing cooperative systems. Issues addressed include the use of multiagent modeling and simulation for the design of human and robotic operations, as a theory for human/robot cooperation on planetary surface missions. We describe a design process for cooperative systems centered around the Brahms modeling and simulation environment being developed at NASA Ames.

Sierhuis, Maarten↗

NASA NTRS2014

UAS Conflict-Avoidance Using Multiagent RL with Abstract Strategy Type Communication

The use of unmanned aerial systems (UAS) in the national airspace is of growing interest to the research community. Safety and scalability of control algorithms are key to the successful integration of autonomous system into a human-populated airspace. In order to ensure safety while still maintaining efficient paths of travel, these algorithms must also accommodate heterogeneity of path strategies of its neighbors. We show that, using multiagent RL, we can improve the speed with which conflicts are resolved in cases with up to 80 aircraft within a section of the airspace. In addition, we show that the introduction of abstract agent strategy types to partition the state space is helpful in resolving conflicts, particularly in high congestion.

Unmanned Autonomous Systems↗

NASA NTRS2014

Announced Strategy Types in Multiagent RL for Conflict-Avoidance in the National Airspace

The use of unmanned aerial systems (UAS) in the national airspace is of growing interest to the research community. Safety and scalability of control algorithms are key to the successful integration of autonomous system into a human-populated airspace. In order to ensure safety while still maintaining efficient paths of travel, these algorithms must also accommodate heterogeneity of path strategies of its neighbors. We show that, using multiagent RL, we can improve the speed with which conflicts are resolved in cases with up to 80 aircraft within a section of the airspace. In addition, we show that the introduction of abstract agent strategy types to partition the state space is helpful in resolving conflicts, particularly in high congestion.

National Airspace↗