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Space science missions based on a swarm implementation strategy offers benefits for future science missions. Achieving viable swarm missions depends on key enabling technologies. This presentation discusses the enabling technologies and how they can be developed and tested.
This paper presents an algorithm for efficient search and rescue using a multi-agent system of vehicles. The algorithm uses an artificial potential field combined with a time-varying reward function for visiting various points within the search area. The reward function is used as a weight for the attractiveness of these points in the potential field. The reward value increases while the point is not being observed, and decreases while the point is observed. Collision avoidance terms are used to repel vehicles from each other, which has the additional effect of reducing duplication of searching efforts. Gradient descent of the potential field results in persistent surveillance of the search area. The algorithm generates position commands in real-time based on communication with the other vehicles. This framework allows vehicles to react in a dynamic environment, which is a significant advantage to simply following a-priori defined trajectories. The algorithm is applied to a swarm of ground robots, and experimental data is presented showing that the swarm effectively searches the entire area and self-allocates search regions to individual vehicles.
Preliminary experiments show that heliocentric planetesimals passing through the Earth environment possess significant angular momentum. However it also appears that these same planetesimals impacting a circularized circumterrestrial planetesimal swarm would likely remove angular momentum (though possibly increasing mean kinetic energy), presumably promoting both swarm infall upon the Earth and escape to heliocentric space. Only a distribution of highly eccentric satellite orbits with mean tangential velocities of a few tens of percent of local circular velocity would be immune against angular momentum loss to passing heliocentric planetesimals.
The Farmington ordinary L5 chondrite with its uniquely short cosmic-ray exposure age of less than 25,000 years may have been a member of a large meteoroid swarm which was detected by the Apollo seismic network when it encountered the moon in June 1975. The association implies that the parent body of the Farmington meteorite was in an earth-crossing orbit at the time the swarm was formed. This supports the idea that at least some meteorites are derived from the observable population of earth-crossing asteroids.
Magellan radar data of Venus reveal 163 large radial lineament systems composed of graben, fissure, and fracture elements. On the basis of their structure, plan view geometry, and volcanic associations, at least 72% are interpreted to have formed primarily through subsurface dike swarm emplacement, the remainder through uplift or a combination of these two mechanisms. The population of swarms is used to determine regional and global stress orientation. The stress configuration recorded from 330-210 deg E (Aphrodite Terra) is best explained by isostatic compensation of existing long wavelength topography or coupling between mantle flow and the lithosphere. The rest are correlated with concentrations of rifting and volcanism in the Beta-Atla-Themis region. The global stress field on Venus is different than that of Earth, where plate boundary forces dominate.
ANTS (Autonomous Nano-Technology Swarm), a NASA advanced mission concept, calls for a large (1000 member) swarm of pico-class (1 kg) totally autonomous spacecraft to prospect the asteroid belt. Additional information is contained in the original extended abstract.
The purpose of this paper is to demonstrate the application of particle swarm optimization to a realistic multidisciplinary optimization test problem. The paper's new contributions to multidisciplinary optimization is the application of a new algorithm for dealing with the unique challenges associated with multidisciplinary optimization problems, and recommendations as to the utility of the algorithm in future multidisciplinary optimization applications. The selected example is a bi-level optimization problem that demonstrates severe numerical noise and has a combination of continuous and truly discrete design variables. The use of traditional gradient-based optimization algorithms is thus not practical. The numerical results presented indicate that the particle swarm optimization algorithm is able to reliably find the optimum design for the problem presented here. The algorithm is capable of dealing with the unique challenges posed by multidisciplinary optimization as well as the numerical noise and truly discrete variables present in the current example problem.
We discuss two software engineering aspects in the development of complex swarm-based systems. NASA researchers have been investigating various possible concept missions that would greatly advance future space exploration capabilities. The concept mission that we have focused on exploits the principles of autonomic computing as well as being based on the use of intelligent swarms, whereby a (potentially large) number of similar spacecraft collaborate to achieve mission goals. The intent is that such systems not only can be sent to explore remote and harsh environments but also are endowed with greater degrees of protection and longevity to achieve mission goals.
Kennedy Space Center has teamed up with the Biological Computation Lab at the University of New Mexico to create a swarm of small, low-cost, autonomous robots, called Swarmies, to be used as a ground-based research platform for in-situ resource utilization missions. The behavior of the robot swarm mimics the central-place foraging strategy of ants to find and collect resources in an unknown environment and return those resources to a central site.
Spacecraft orbits about the Sun-Earth librarian point L1 have been of interest since the 1950s. An L1 halo orbit was first achieved with the International Sun-Earth Explorer-3 (ISEE-3) mission, and similar orbits around Sun-Earth L1 were achieved in the Solar and Heliospheric Observatory (SOHO), Advanced Composition Explorer (ACE), Genesis, and Deep Space Climate Observatory (DSCOVR) missions. With recent advancements in CubeSat technology, we envision that it will soon be feasible to deploy CubeSats at L1. As opposed to these prior missions where one large satellite orbited alone, a swarm of CubeSats at L1 would enable novel science data return, providing a topology for intersatellite measurements of heliophysics phenomena both spatially and temporally, at varying spatial scales.The purpose of this iPoster is to present a flight dynamics strategy for a swarm of numerous CubeSats orbiting Sun-Earth L1. The presented method is a coupled, two-part solution. First, we present a deployment strategy for theCubeSats that is optimized to produce prescribed, time-varying intersatellite baselines for the purposes of collectingmagnetometer data as well as radiometric measurements from cross-links. Second, we employ a loose controlstrategy that was successfully applied to SOHO and ACE for minimized stationkeeping propellant expenditure. Weemphasize that the presented solution is practical within the current state-of-the-art and heritage CubeSat technology,citing capabilities of CubeSat designs that will launch on the upcoming Exploration Mission 1 (EM-1) to lunar orbitsand beyond. Within this iPoster, we present animations of the simulated deployment strategy and resulting spacecrafttrajectories. Mission design parameters such as total v required for long-term station keeping andminimum/maximum/mean spacecraft separation distances are also presented.
It is desirable to develop a high Equivalent Isotropically Radiated Power (EIRP), autonomous, distributed, reconfigurable, on-demand Ka/X-band transmit-antenna array using small satellites, for deepspace communication (Mars and beyond). Our work shows that a distributed, free-flying swarm array composed of N CubeSats can not only be phased to provide a coherent beam in Ka/X-band, with performance (mass, power, data rate) comparable to the state-of-the-art Mars Reconnaissance Orbiter (MRO), but with N large, higher performance and data rates can be achieved, assuming a proper intra-swarm metrology system is in place.
This paper describes new autonomy technology that enabled a team of unmanned surface vehicles (USVs) to execute cooperative behaviors in the USV Swarm II harbor patrol demonstration and provides a description of autonomy performance in the event. The new developments extend the NASA Jet Propulsion Laboratory’s CARACaS (Control Architecture for Robotic Agent Command and Sensing) autonomy architecture, which pro- vides foundational software infrastructure, core executive functions, and several default robotic technology mod- ules. In Swarm II, CARACaS demonstrated higher levels of autonomy and more complex cooperation than previous on-water exercises, using full-sized vehicles and real-world sensing and communication. The core au- tonomous behaviors to support the harbor patrol scenario included Patrol, Track, Inspect, and Trail, providing the capability of finding all vessels entering the patrol area, keeping track of them, inspecting them to infer intent, and trailing suspect vessels. Significantly, CARACaS assumed responsibility for not only executing tasks safely and efficiently but also recognizing what tasks needed to be accomplished, given the current state of the world. Since the heterogeneous USV teams shared world model that evolved, such as due to (dis)appearance of vessels in the area or a change in health or availability of a USV, CARACaS replanned to generate and reallocate the new task list. Thus, human intervention was never required in the loop to task USVs during mission execution, though a supervisory role was supported in the autonomy system for mission monitoring and exception handling. Finally, CARACaS also ensured the USVs avoided hazards and obeyed the applicable rules of the road, using its local motion planning modules.
In this paper, we investigate the feasibility of calibrating an orbiting phased array composed of CubeSat class spacecraft together with a larger reference (or “chief”) spacecraft, comprising an overall Swarm Array. Considered as an analog to the Deep Space Network (DSN) Uplink Arraying problem [1], a spaceborne Swarm Array that can potentially deliver comparable or even greater operational performance than large monolithic spacecraft (such as increased data rates for telecommunications, or greater baselines for improved spatial resolution), thus increasing future mission capability but with significantly enhanced flexibility, evolvability and robustness.
NASA’s future vision for interplanetary networking includes a lunar network, Cube Satellite (CubeSat) constellations, and deep space robotic missions, comprising what could be viewed as a network of networks. Delay-tolerant networking (DTN) architecture and protocols provide a standard network layer among these varying scenarios and mitigate many challenges of the space environment, such as long delays, unplanned service interruptions, and asymmetric links. The Cognitive Space Gateway (CSG) is a routing method in a DTN architecture that uses spiking neural networks as the learning element to optimize outing decisions in a complex environment. This work aims to further develop cognitive networking technologies in several critical areas, including DTN, the CSG algorithm, SmallSat swarm topologies, and cloud services. The CSG algorithm is tested in a realistic scenario in which the emulated network topology is based on a SmallSat swarm. The emulation environment will be built upon a commercial cloud service, such as Amazon Web Services (AWS) Elastic Compute Cloud. This work investigates the ability of such a platform to enable a flexible, lower maintenance approach to creating a multihop network outside of a physical laboratory. The cloud platform will provide a secure environment allowing for collaboration among government and academic entities.
In this paper, we investigate the feasibility of calibrating an orbiting phaased array composed of CubeSat class spacecraft together with a larger reference (or “chief”) spacecraft, comprising an overall Swarm Array. Considered as an analog to the Deep Space Network (DSN) Uplink Arraying problem [1], a spaceborne Swarm Array that can potentially deliver comparable or even greater operational performance than large monolithic spacecraft (such as increased datarates for telecommunications, or greater baselines for improved spatial resolution), thus increasing future mission capability but with significantly enhanced flexibility, evolvability and robustness.
Alfvén mode Pc1 waves undergo mode conversion to the fast mode due to induced Hall current in the ionosphere. The fast mode Pc1 waves are trapped and propagate across the magnetic field through the ionospheric waveguide. This process is called Pc1 wave ducting (PWD). Ducting is expected to be in any direction, but most of the existing literature investigated only PWDs toward the equator. In this paper, we report the rare observations of PWD propagating from sub-auroral latitudes and pervading the polar cap using Swarm satellites, ground magnetometers, and Defense Meteorological Satellite Program (DMSP) satellites. We first identify the injection region of Pc1 wave where localized broadband transverse waves, isolated aurora, and energetic proton precipitations are concurrently observed. Then, we compare ducting characteristics in the ionosphere between the two hemispheres. For the three events investigated here, PWDs in the Southern Hemisphere (SH) pervaded the polar cap while Pc1 waves in the Northern Hemisphere (NH) did not. This hemispheric asymmetry is attributed to the plasma density in the SH sufficient to form the Pc1 waveguide. However, a sharp plasma density gradient on the propagation path still interrupts the ducting even in higher plasma density (> 10 5 cm -3 ) regions. The observation of two intersecting Swarm satellites indicates the PWD is not only elongated meridionally, but also can have a significant zonal extent beyond that of the injection region.
Following the rapid growth of distributed energy resources (e.g. renewables, battery), localized peer-to-peer energy transactions are receiving more attention for multiple benefits, such as, reducing power loss, stabilizing the main power grid, etc. To promote distributed renewables locally, the local trading price is usually set to be within the external energy purchasing and selling price range. Consequently, building prosumers are motivated to trade energy through a local transaction center. This local energy transaction is modeled in bilevel optimization game. A selfish upper level agent is assumed with the privilege to set the internal energy transaction price with an objective of maximizing its arbitrage profit. Meanwhile, the building prosumers at the lower level will response to this transaction price and make decisions on electricity transaction amount. Therefore, this non-cooperative leader-follower trading game is seeking for equilibrium solutions on the energy transaction amount and prices. In addition, a uniform local transaction price structure (purchase price equals selling price) is considered here. Aiming at reducing the computational burden from classical Karush-Kuhn-Tucker (KKT) transformation and protecting the private information of each stakeholder (e.g., building), swarm intelligence based solution approach is employed for upper level agent to generate trading price and coordinate the transactive operations. On one hand, to decrease the chance of premature convergence in global-best topology, Rubiks Cube topology is proposed in this study based on further improvement of a two-dimensional square lattice model (i.e., one local-best topology-Von Neumann topology). Rotating operation of the cube is introduced to dynamically changing the neighborhood and enhancing information flow at the later searching state. Several groups of experiments are designed to evaluate the performance of proposed Rubiks Cube topology based particle swarm algorithm. The results have validated the effectiveness of proposed topology and operators comparing with global-best version PSO and Von Neumann topology based PSO and its scalability on larger scale applications.
Neutron transport simulations are ubiquitous in nuclear engineering because they allow one to model experimental systems and render a model platform for easy perturbation of experimental designs. In addition, simulations allow one to gain experimental insight without actually having to go through the trouble of building a physical experiment. Neutron transport simulations can be stochastic or deterministic based. Stochastic neutron transport simulations are typically simulated using the Monte Carlo method and yield very accurate solutions but are computationally expensive, while deterministic methods are typically faster but can be less accurate. Here we focus on optimizing the accuracy of deterministic neutron transport simulations for radiotherapy simulations. Deterministic neutron transport requires discretization of angle, energy, and space to appropriately analyze the system one is trying to model. Discretization of energy is challenging because of the highly variable neutron flux at certain neutron energies. Improper discretization of energy in the transport model can lead to erroneous results and therefore inaccurate interpretations of the solution. In this study, we evaluate Particle Swarm Optimization (PSO) as a mechanism for selecting optimal group structures for radiotherapy shielding. We tested the particle swarm optimization algorithm on radiotherapy shielding problems using Los Alamos National Laboratory's (LANL) main deterministic transport code PARTISN. Results show that the optimized energy group structures generated from the optimization algorithm outperformed LANL's standard energy group structures, and therefore demonstrate utility in using PSO to expedite computation times due to the increased accuracy obtained with a smaller but optimized group structure. (authors)