The search for artificial and natural objects in both cis-lunar and trans-lunar space has grown increasingly important. To accurately detect and track small objects, stray light mitigation is a necessity. Observations conducted in 2022 from a ground-based telescope intended to track such objects have been hampered by excess lunar stray light. In this paper, we present work done to resolve this problem by applying black pigments to the optical tube and thus suppressing its surface scattering. A non-sequential ray tracing model was created to analyze the telescope’s final focal plane irradiance. This model was used to identify critical and illuminated surfaces to determine the stray light paths that have affected observations. We conducted experimental tests to measure the Bidirectional Reflectance Distribution Function (BRDF) of various practical, readily available, and robust black coatings, including paints such as Black 3.0 and Musou. After application on the actual telescope tube, the new surface coating reduced the photon count on the detector from a variable-angle off-axis point source by 76% over all angles measured.
Current analysis of data streamed back to Earth by the Cassini spacecraft features Titan as one of the most exciting
places in the solar system. NASA centers and universities around the US, as well as the European Space Agency, are
studying the possibility of sending, as part of the next mission to this giant moon of Saturn, a hot-air balloon
(Montgolfier-type) for further and more in-depth exploration. The basic idea would be to design a reliable, semi-autonomous,
and yet cheap Montgolfier capable of using continuous flow of waste heat from a power source to lift the
balloon and sustain its altitude in the Titan environment.
In this paper we study the problem of locally navigating a hot-air balloon in the nitrogen-based Titan atmosphere. The
basic idea is to define a strategy (i.e. design of a suitable guidance system) that allows autonomous and semi-autonomous
navigation of the balloon using the available (and partial) knowledge of the wind structure blowing on the
saturnian satellite surface. Starting from first principles we determined the appropriate thermal and dynamical models
describing (a) the vertical dynamics of the balloon and (b) the dynamics of the balloon moving on a vertical plane (2-D
motion). Next, various non-linear fuzzy-based control strategies have been evaluated, analyzed and implemented in
MATLAB to numerically simulate the capability of the system to simultaneously maintain altitude, as well as a
scientifically desirable trajectory. We also looked at the ability of the balloon to perform station keeping. The results of
the simulation are encouraging and show the effectiveness of such a system to cheaply and effectively perform semi-autonomous
exploration of Titan.
Crucial questions for possible utilization of Near Earth Asteroids include how to break asteroid materials down to
particle sizes that can be processed. This remained difficult to answer because of the limited number and resolutions of
images previous obtained through asteroid missions. Recently, the Hayabusa spacecraft obtained unprecedentedly high-resolution
images of a ~300m-sized asteroid, Itokawa, which gives unique opportunity to discuss the nature of surface
materials on a small asteroid. Hayabusa reveals that the asteroid is covered by fine- and coarse-grained materials,
including granules, pebbles, cobbles, and boulders up to tens of meters. Gravels on this small asteroid appear to be
loosely deposited along the gravitational equipotential surfaces. The existence of smooth areas as well as boulder-rich
rough areas indicate that gravels should have experienced migrations and segregations. Thus, the issue regarding the
breaking of asteroid materials appears to have been resolved naturally, at least for this asteroid, which has important
implications for future robotic missions dedicated to resource exploration and utilization.
Remote sensing studies are often based on simplified approaches describing the photon transport in absorbing and
scattering media. The main purpose of the present paper is to show the potentiality of modeling directly the transport
phenomena by mean of linear Boltzmann equation. Some details about the solution method of the integro-differential
equation are reported with a collection of results of relevance in planetary study domain. An inverse approach based on
artificial neural network is also proposed to retrieve the optical properties of planetary surfaces and its performances are
tested in various cases.
Future planetary exploration of the outer satellites of the Solar System will require higher levels of onboard
automation, including autonomous determination of sites where the probability of significant scientific
findings is highest. Generally, the level of needed automation is heavily influenced by the distance between
Earth and the robotic explorer(s) (e.g. spacecraft(s), rover(s), and balloon(s)). Therefore, planning missions
to the outer satellites mandates the analysis, design and integration within the mission architecture of semi- and/or completely autonomous intelligence systems. Such systems should (1) include software packages
that enable fully automated and comprehensive identification, characterization, and quantification of
feature information within an operational region with subsequent target prioritization and selection for
close-up reexamination; and (2) integrate existing information with acquired, "in transit" spatial and
temporal sensor data to automatically perform intelligent planetary reconnaissance, which includes
identification of sites with the highest potential to yield significant geological and astrobiological
information. In this paper we review and compare some of the available Artificial Intelligence (AI)
schemes and their adaptation to the problem of designing expert systems for onboard-based, autonomous
science to be performed in the course of outer satellites exploration. More specifically, the fuzzy-logic
framework proposed is analyzed in some details to show the effectiveness of such a scheme when applied
to the problem of designing expert systems capable of identifying and further exploring regions on Titan
and/or Enceladus that have the highest potential to yield evidence for past or present life. Based on
available information (e.g., Cassini data), the current knowledge and understanding of Titan and Enceladus
environments is evaluated to define a path for the design of a fuzzy-based system capable of reasoning over
collected data and capable of providing the inference required to autonomously optimize future outer
satellites explorations.
If the goal of planetary exploration is to build a permanent and expanding, self-sustaining extraterrestrial civilization,
then clever and myriad uses must be made of planetary resources. Resources must be identified and evaluated
according to their practicality. A new economy should be devised based on resource occurrence, ore accessibility,
options for ore transport, material beneficiation, and manufacturing; end uses and demand; and full economic
cost/benefit assessment. Locating and evaluating these resources should be done with coordinated robotic assets
arrayed in orbit and on the surface. Sensor arrays and tandem on-ground means of physical manipulation of rocks
should incorporate highly capable onboard data processing, feature detection, and quantification of material
properties; intelligent decision making; a flexible capacity to re-order priorities and act on those priorities in carrying
out exploration programs; and human-robot interaction. As resource exploration moves into exploitation, sensors
working in tandem with robust physical manipulation will place increased emphasis on automation in effective and
safe robotic quarrying, tunneling, boring, and ore beneficiation. Any new global planetary economy will have to
weigh the efficiency of resource identification and utilization with full-spectrum cost/benefit assessment for human
health and safety, the environment, future habitability and sustainability, and human priorities in the development and
growth of civilization. It makes no sense to rove from one planet to another in a wave of resource use and depletion,
like interplanetary locusts. Robotic systems will open new worlds to human use, but they will also place a premium
on human ability to control exponentially growing consumption.
The Viking mission was the only mission to date that conducted life detection experiments. It revealed ambiguous and
still controversial results. New findings and hypotheses urge a re-evaluation of the Viking results and a re-evaluation of
the evidence for the possible presence of life on Mars in general. Recent findings of abundant water ice on Mars, the
presence of liquid contemporary water on the Martian surface, and the detection of methane in the Martian atmosphere
further support this possibility. Current missions to be launched focus on habitability considerations (e.g., NASA Phoenix,
NASA Mars Science Laboratory), but shy away from directly testing for life on Mars, with the potential exception of the
ESA ExoMars mission. If these currently planned missions collect positive evidence toward habitability and the possible
existence of extraterrestrial (microbial) life on Mars, it would be timely to propose a new mission to Mars with a strong life
detection component. We propose such a mission called BOLD: Biological Oxidant and Life Detection Mission. The
BOLD mission objective would be to quantify the amount of hydrogen peroxide existing in the Martian soil and to test
for processes typically associated with life. Six landing packages are projected to land on Mars that include a limited
power supply, a set of oxidant and life detection experiments, and a transmitter, which is able to transmit information via
an existing Mars orbiter back to Earth.
Over the past few years, NASA has had a great interest in exploring the feasibility of using Unmanned Aerial Vehicles (UAVs), equipped with multi-spectral imaging systems, as long-duration platform for crop monitoring. To address the problem of predicting the ripeness level of the Kauai coffee plantation field using UAV aerial images, we proposed a neural network algorithm based on a nested Leaf-Canopy radiative transport Model (LCM2). A model-based, multi-layer neural network using backpropagation has been designed and trained to learn the functional relationship between the airborne reflectance and the percentage of ripe, over-ripe and under-ripe cherries present in the field. LCM2 was used to generate samples of the desired map. Post-processing analysis and tests on synthetic coffee field data showed that the network has accurately learn the map. A new Domain Projection Technique (DPT) was developed to deal with situations where the measured reflectance fell outside the training set. DPT projected the reflectance into the domain forcing the network to provide a physical solution. Tests were conducted to estimate the error bound. The synergistic combination of neural network algorithms and DPT lays at the core of a more complex algorithm designed to process UAV images. The application of the algorithm to real airborne images shows predictions consistent with post-harvesting data and highlights the potential of the overall methodology.
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