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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Swarm Size Planning Tool for Multi-Job Type Missions

As part of swarm search and service (SSS) missions, swarms are tasked with searching an area while simultaneously servicing jobs as they are encountered. Jobs must be immediately serviced and can be one of multiple types. Each type requires that vehicle(s) break off from the swarm and travel to the job site for a specified amount of time. The number of vehicles needed and the service time for each job type are known. Once a job has been successfully serviced, vehicles return to the swarm and are available for reallocation. When planning SSS missions, human operators are tasked with determining the required number of vehicles needed to handle the expected job demand. The complex relationship between job type parameters makes this choice challenging. This work presents a prediction model used to estimate the swarm size necessary to achieve a given performance. User studies were conducted to determine the usefulness and ease of use of such a prediction model as an aid during mission planning. Results show that using the planning tool leads to 7 times less missed area and a 50 percent cost reduction.

Chandarana, Meghan↗
NASA NTRSDate not supplied

Move to Talk, Talk to Move: Tightly Integrated Communication and Control for Coordinated Swarms of Small Spacecraft

The Move to Talk, Talk to Move: Tightly Integrated Communication and Control for Coordinated Swarms of Small Spacecraft project will build on existing research on collaborative autonomy of multi-agent systems and design techniques that will enable coordinated communication and control of spacecraft. The success of many space exploration and science missions hinges on real-time monitoring of time-varying and/or geographically distributed phenomena. This monitoring can be achieved using a swarm of small spacecraft, which collect data about the environment and share information within the swarm of spacecraft. Current space exploration missions typically issue commands to control each spacecraft individually from Earth, and the data gathered by each spacecraft is also transmitted to Earth separately via X-band communication over the Deep Space Network (DSN). This approach is expensive, slow, and unreliable. Many coordinated tasks amongst a swarm of autonomous agents (or, specifically, small spacecraft) rely on communication. Existing control, estimation, and decision algorithms often assume that mostly reliable communications are available; however, this is often not the case in actual environments and thus is a barrier to operating swarms of small spacecraft.

Qi Han↗
NASA NTRS2024

Integrating Manned Aircraft and UAVs for the Prediction, Tracking, and Eradication of Desert Locust Swarms

Desert locusts (Schistocerca gregaria) present an acute threat to the agriculture of certain regions, capable of annihilating vast expanses of farmland and destabilizing food security. In this paper, an aviation-based framework concept is introduced that integrates both manned aircraft and Unmanned Aerial Vehicles (UAVs) to preemptively forecast, localize, and eliminate locust swarms efficiently. The framework begins by leveraging predictive modeling methods to refine search areas and pinpoint high-value Points of Interest (POIs) where locust swarms are most likely to materialize. From there, the proposed two-step model uses optical, infrared, and hyperspectral sensors onboard manned aircraft to conduct long-range surveying of probable swarm locations. Upon detection of a swarm, UAVs equipped with sophisticated sensors, including pheromone detectors, thermal and optical imaging systems, are deployed directly from the aircraft to carry out targeted biopesticide applications. This approach, which capitalizes on the combined strengths of both vehicles, provides the rapid response and large-scale intervention necessary to control locust outbreaks. By targeting locust swarms during their most destructive phase, this paper aims to disrupt the locust life cycle and mitigate their severe agricultural impact, offering a scalable and sustainable solution for food security in afflicted regions.

locust monitoring systems↗
NASA NTRSDate not supplied

Creating an Interface to view Multi-Spacecraft Swarm Telemetry

Distributed Spacecraft Systems are a type of multi-spacecraft mission architecture that can not only provide improved resolution, coverage, and availability of existing missions, but also enable missions that would be previously infeasible using traditional approaches. Distributed Spacecraft Autonomy (DSA) is a project developed by the National Aeronautics and Space Administration that enables distributed spacecraft systems. In previous science swarm missions, the spacecraft involved have not been able to communicate with each other without utilizing a ground station. Now that the spacecraft can perform inter-satellite communication, the spacecraft can be treated as a collective. Swarm autonomy is critical for a growing number of satellites which means novel ways of displaying swarm data needs to be implemented. Such systems introduce unique challenges to traditional approaches for command and control of these spacecraft, due to the large number of spacecraft and the complexity of the interactions between them. The ground data system for DSA addresses these challenges through the creation of a custom user interface that allows a single operator to orchestrate a multi-spacecraft swarm in a scalable way. This plenary describes the details of the autonomy demonstration being performed, the requirements of those using the interface to analyze the spacecraft telemetry to assess demonstration success, and the approach taken by the ground systems team to create an interface that satisfies these requirements. This approach involves the creation of several distinct components that correspond to the level of detail presented to the user. These components are based on conventional user roles in human-robot interaction, including supervisor, operator, and mechanic, extended to accommodate the additional overhead of coordinating actions between agents. One main feature of the interface is the listenability matrix component which will represent inter-satellite communications in a heat mapped matrix. The above work described will enable users to command and interact with the spacecraft as a collective.

human-swarm interaction↗
NASA NTRS2018

Propulsion Trade Studies for Spacecraft Swarm Mission Design

Spacecraft swarms constitute a challenge from an orbital mechanics standpoint. Traditional mission design involves the application of methodical processes where predefined maneuvers for an individual spacecraft are planned in advance. This approach does not scale to spacecraft swarms consisting of many satellites orbiting in close proximity; non-deterministic maneuvers cannot be preplanned due to the large number of units and the uncertainties associated with their differential deployment and orbital motion. For autonomous small sat swarms in LEO, we investigate two approaches for controlling the relative motion of a swarm. The first method involves modified miniature phasing maneuvers, where maneuvers are prescribed that cancel the differential delta V of each CubeSat's deployment vector. The second method relies on artificial potential functions (APFs) to contain the spacecraft within a volumetric boundary and avoid collisions. Performance results and required delta V budgets are summarized, indicating that each method has advantages and drawbacks for particular applications. The mini phasing maneuvers are more predictable and sustainable. The APF approach provides a more responsive and distributed performance, but at considerable propellant cost. After considering current state of the art CubeSat propulsion systems, we conclude that the first approach is feasible, but the modified APF method of requires too much control authority to be enabled by current propulsion systems.

CubeSat↗
NASA NTRS2018

A Comprehensive Model of Earth's Magnetic Field Determined from 4 Years of Swarm Satellite Observations

The European Space Agency's three-satellite constellation Swarm, launched in November 2013, has provided unprecedented monitoring of Earth's magnetic field via a unique set of gradiometric and multi-satellite measurements from low Earth orbit. In order to exploit these measurements, an advanced "Comprehensive Inversion" (CI) algorithm has been developed to optimally separate the various major magnetic field sources in the near-Earth regime. The CI algorithm is used to determine Swarm Level-2 (L2) magnetic field data products that include the core, lithospheric, ionospheric, magnetospheric, and associated induced sources. In addition, it has become apparent that the CI is capable of extracting the magnetic signal associated with the oceanic principal lunar semi-diurnal tidal constituent M(sub 2) to such an extent that it has been added to the L2 data product line. This paper presents the parent model of the Swarm L2 CI products derived with measurements from the first four years of the Swarm mission and from ground observatories, denoted as "CIY4", including the new product describing the magnetic signal of the M(sub 2) oceanic tide.

Swarm Satellites↗
DOE OSTI2021

Episodic Earthquake Swarms in the Mineral Mountains, Utah Driven by the Roosevelt Hydrothermal System

Over 1,000 earthquakes (-2.0 < M < 2.0), identified using a matched-filter method, occurred in the Mineral Mountains, Utah between 2016 and 2019. The enhanced catalog is complete down to M -0.9 and contains roughly 15 times more events than originally cataloged. Earthquake relocation of ~800 earthquakes shows that activity is concentrated in a <2 km long E-W striking narrow zone, ~4 km east of the Roosevelt hydrothermal system. Two fault orientations, both N-S and E-W parallel to the Opal Mound and Mag Lee faults, respectively, are observed after computing composite focal mechanisms of highly similar earthquakes. Looking solely at the temporal distribution of the seismicity, we identify 15 periods of swarm-like activity, with two major clusters occurring in December 2016, recorded by three stations, and in October 2019 recorded by eight stations. The October 2019 swarm, the best recorded sequence in the area, provides evidence for the underlying triggering mechanism. We show that a complex mechanism of fluid diffusion and aseismic slip is responsible for the swarm evolution with migration velocities reaching 10 km/day. We hypothesize that these episodic swarms in the Mineral Mountains are primarily driven by migrating fluids that originate within the Roosevelt hydrothermal system.

58 GEOSCIENCES↗
NASA NTRS2005

Autonomous and Autonomic Swarms

A watershed in systems engineering is represented by the advent of swarm-based systems that accomplish missions through cooperative action by a (large) group of autonomous individuals each having simple capabilities and no global knowledge of the group s objective. Such systems, with individuals capable of surviving in hostile environments, pose unprecedented challenges to system developers. Design and testing and verification at much higher levels will be required, together with the corresponding tools, to bring such systems to fruition. Concepts for possible future NASA space exploration missions include autonomous, autonomic swarms. Engineering swarm-based missions begins with understanding autonomy and autonomicity and how to design, test, and verify systems that have those properties and, simultaneously, the capability to accomplish prescribed mission goals. Formal methods-based technologies, both projected and in development, are described in terms of their potential utility to swarm-based system developers.

Hinchey, Michael G.↗
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 NTRS2015

The Swarm Initial Field Model for the 2014 Geomagnetic Field

Data from the first year of ESA's Swarm constellation mission are used to derive the Swarm Initial Field Model (SIFM), a new model of the Earth's magnetic field and its time variation. In addition to the conventional magnetic field observations provided by each of the three Swarm satellites, explicit advantage is taken of the constellation aspect by including east-west magnetic intensity gradient information from the lower satellite pair. Along-track differences in magnetic intensity provide further information concerning the north-south gradient. The SIFM static field shows excellent agreement (up to at least degree 60) with recent field models derived from CHAMP data, providing an initial validation of the quality of the Swarm magnetic measurements. Use of gradient data improves the determination of both the static field and its secular variation, with the mean misfit for east-west intensity differences between the lower satellite pair being only 0.12 nT.

Olsen, Nils↗
NASA NTRS2018

Determination of Total Magnetic Anomalies and Their Vertical Gradients of Swarm-A Satellite over Central Europe and Pannonian Basin

Our paper discusses the determination of total magnetic field anomalies derived from the Swarm–A satellite data; one of two low orbiting satellites of the three Swarm formations. This procedure requires several modifications. The first step is the conversion of the measured CDF data to the ASCII format. This step is followed by the selection of the data with K(sub p) index ≤ 1(sub +). The anomalies are determined by the removal of the IGRF from the resulting satellite data. There are two Swarm–A data sets: descending (6000) orbits and ascending (5688) orbits. For our study the descending orbits were used. The next step of calculations is to determine the difference of the two-dimensional linear field fitted to the Swarm–A anomalies and the anomalies given. These anomalies are filtered by Gaussian low-pass filter. The last step of the corrections is the removal of the direct component, zero spatial frequency, from the descending anomalies. The anomalies and their vertical gradients are qualitatively interpreted over Central Europe and the Pannonian Basin.

Kis, K.↗
NASA NTRS2019

Marsbee - Swarm of Flapping Wing Flyers for Enhanced Mars Exploration: NASA Innovative Advanced Concepts (NIAC) - Phase I: Final Report

Mars exploration has received significant interest from academia, industry, government, and the general public. Despite continued interest, flying on Mars remains challenging, mainly due to the ultra-thin Martian atmospheric density. Although the gravitational acceleration on Mars is 38 percent of Earth's 9.8 meters per second squared, the Martian atmospheric density is only 1.3 percent of the air density on Earth. The aerodynamic forces are proportional to the ambient fluid density. Therefore, flying near the surface of Mars has been considered nearly impossible. The proposed mission architecture (Fig. 1) consists of a Mars rover (already existing) that serves as a mobile base for Marsbees - a deployable swarm of small bio-inspired flapping wing vehicles. In one ConOps scenario, each Marsbee would carry an integrated stereographic video camera and the swarm could construct a 3D topographic map of the local surface for rover path planning. These flying scouts would provide a "third-dimension" to the rover capabilities. In other scenarios, each part of the swarm of Marsbees could carry pressure and temperature sensors for atmospheric sampling, or small spectral analyzers for identification of mineral outcroppings. In each scenario, the rover acts as a recharging and deployment/return station and data and communication hub. Human exploration of Mars is one of the major objectives of NASA and commercial entities such as SpaceX and Boeing. The identified innovations unique to the bio-inspired flapping Marsbee provide viable multi-mode flying mobility for Martian atmospheric and terrain exploration. A swarm of Marsbees provides an enhanced reconfigurable Mars exploration system that is resilient to individual component failures. These Marsbees can carry sensors and wireless communication devices in combination with a Mars rover and helicopters. These enhanced sensing and information gathering abilities can contribute to the following NASA Mars mission objectives: i) "Determine the habitability of an environment", ii) "Obtain surface weather measurements to validate global atmospheric models", and iii) "Prepare for human exploration on Mars." Various commercial entities, e.g. SpaceX and Boeing, are investing in technologies to transport humans to Mars.

Kang, Chang-kwon↗
NASA NTRS2017

Distributed Spatiotemporal Motion Planning for Spacecraft Swarms in Cluttered Environments

This paper focuses on trajectory planning for spacecraft swarms in cluttered environments, like debris fields or the asteroid belt. Our objective is to reconfigure the spacecraft swarm to a desired formation in a distributed manner while minimizing fuel and avoiding collisions among themselves and with obstacles. In our prior work we proposed a novel distributed guidance algorithm for spacecraft swarms in static environments. In this paper, we present the Multi-Agent Moving-Obstacles Spherical Expansion and Sequential Convex Programming (MAMO SE-SCP) algorithm that extends our prior work to include spatiotemporal constraints such as time-varying, moving obstacles and desired time-varying terminal positions. In the MAMO SE-SCP algorithm, each agent uses a spherical-expansion-based sampling algorithm to cooperatively explore the time-varying environment, a distributed assignment algorithm to agree on the terminal position for each agent, and a sequential-convex-programming-based optimization step to compute the locally-optimal trajectories from the current location to the assigned time-varying terminal position while avoiding collision with other agents and moving obstacles. Simulation results demonstrate that the proposed distributed algorithm can be used by a spacecraft swarm to achieve a time-varying, desired formation around an object of interest in a dynamic environment with many moving and tumbling obstacles.

Bandyopadhyay, Saptarshi↗
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↗
NASA NTRS2017

Distributed Spatiotemporal Motion Planning for Spacecraft Swarms in Cluttered Environments

This paper focuses on trajectory planning for spacecraft swarms in cluttered environments, like debris fields or the asteroid belt. Our objective is to reconfigure the spacecraft swarm to a desired formation in a distributed manner while minimizing fuel and avoiding collisions among themselves and with the obstacles. In our prior work we proposed a novel distributed guidance algorithm for spacecraft swarms in static environments.1 In this paper, we present the Multi-Agent Moving-Obstacles Spherical Expansion and Sequential Convex Programming (MAMO SE–SCP) algorithm that extends our prior work to include spatiotemporal constraints such as time-varying, moving obstacles and desired time-varying terminal positions. In the MAMO SE–SCP algorithm, each agent uses a spherical-expansion-based sampling algorithm to cooperatively explore the time-varying environment, a distributed assignment algorithm to agree on the terminal position for each agent, and a sequential-convex-programming-based optimization step to compute the locally-optimal trajectories from the current location to the assigned time-varying terminal position while avoiding collision with other agent and the moving obstacles. Simulations results demonstrate that the proposed distributed algorithm can be used by a spacecraft swarm to achieve a time-varying, desired formation around an object of interest in a dynamic environment with many moving and tumbling obstacles.

Hadaegh, Fred Y.↗
NASA NTRS2020

Detection and Mitigation of Transient Instabilities in Multi-Agent Systems and Swarms

We first introduce the novel concept of transient instabilities in multi-agent systems and swarms, i.e., a small disturbance leads to increasing-amplitude oscillations throughout the swarm, which results in a large number of inter-agent collisions. This instability is dominant in the transient phase of the system and it does not appear in the steady-state behavior of the system, as each agent uses Lyapunov-stable feedback control laws. We present a rigorous definition of transient instability in swarms, and discuss its key properties. We also present a sufficient condition to check if a swarm will be transient stable. We study the behaviour of different control laws under this condition. We also show how transient instability phenomenon could impact the dynamics of deployable structures. Next, we present 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. We present numerical simulation results to demonstrate the effectiveness of our proposed approaches.

Quadrelli, Marco B.↗
NASA NTRS2020

CM6: A Comprehensive Geomagnetic Field Model Derived From Both CHAMP and Swarm Satellite Observations

From the launch of the Oersted satellite in 1999, through the CHAMP mission from 2000 to 2010, and now with the Swarm constellation mission starting in 2013, satellite magnetometry has provided excellent monitoring of the near-Earth magnetic field regime. The advanced Comprehensive Inversion scheme has been applied to data before Swarm and to the Swarm data itself, but now for the first time to all the satellite data in this new era, culminating in the CM6 model. The highlights of this model include not only a continuous core magnetic field description over the entire time period 1999 to 2019.5 in good agreement with the CHAOS model series, but the addition of two new oceanic tidal magnetic sources: the larger lunar elliptic semi-diurnal constituent N2 and the lunar diurnal constituent O1. CM6 is also the parent model of the NASA/GSFC candidates for the DGRF2015 and IGRF2020 in response to the IGRF-13 call. This paper provides a full report on the development of CM6.

Geomagnetism↗
NASA NTRSDate not supplied

HelioSwarm: The Swarm is the Observatory

HelioSwarm will transform our understanding of space plasma turbulence by being the first-of-its-kind simultaneous, multiscale observatory comprising multiple spacecraft. HelioSwarm was competitively selected under the Heliophysics Explorers Program 2019 Medium-Class Explorer (MIDEX) Announcement of Opportunity. The central powered-ESPA hub spacecraft is co-orbited by eight SmallSat Node spacecraft, together moving through a High Earth Orbit to obtain data in various solar wind regimes. The mission architecture is that of hub-and-spoke, with the larger hub serving as a communications relay between the nodes and DSN. Mission operations, management, and technical oversight are provided by NASA Ames Research Center; the spacecraft are provided by Northrop Grumman and BCT. The instrument suite includes foreign-contributed instruments and U.S. ones, all under the oversight of University of New Hampshire (which is also the Principal Investigator’s home institution and Science Operations Center). The mission timeline from launch through conclusion of the one-year science mission is provided along with a summarized concept of operations, with particular emphasis on placing the nodes in their proper relative orbit loops to form the geometry needed for science collection at apogee. A brief discussion of how a combination of legacy tools and custom-created swarm analysis tools are used to design the swarm and sort and visualize the collected science data and telemetry in context is provided. Finally, an exploration of the pathfinding nature of HelioSwarm and some implications for future large scientific swarms is offered.

Heliophysics↗