Earth science research must bridge the gap between the atmosphere and the ocean to foster understanding of Earth's
climate and ecology. Typical ocean sensing is done with satellites or in situ buoys and research ships which are slow to
reposition. Cloud cover inhibits study of localized transient phenomena such as Harmful Algal Blooms (HAB). A fleet
of extended-deployment surface autonomous vehicles will enable in situ study of characteristics of HAB, coastal
pollutants, and related phenomena. We have developed a multiplatform telesupervision architecture that supports
adaptive reconfiguration based on environmental sensor inputs. Our system allows the autonomous repositioning of
smart sensors for HAB study by networking a fleet of NOAA OASIS (Ocean Atmosphere Sensor Integration System)
surface autonomous vehicles. In situ measurements intelligently modify the search for areas of high concentration.
Inference Grid and complementary information-theoretic techniques support sensor fusion and analysis. Telesupervision
supports sliding autonomy from high-level mission tasking, through vehicle and data monitoring, to teleoperation when
direct human interaction is appropriate. This paper reports on experimental results from multi-platform tests conducted
in the Chesapeake Bay and in Pittsburgh, Pennsylvania waters using OASIS platforms, autonomous kayaks, and multiple
simulated platforms to conduct cooperative sensing of chlorophyll-a and water quality.
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.
We are developing a multi-robot science exploration architecture and system called the Telesupervised Adaptive Ocean
Sensor Fleet (TAOSF). TAOSF uses a group of robotic boats (the OASIS platforms) to enable in-situ study of ocean
surface and sub-surface phenomena. The OASIS boats are extended-deployment autonomous ocean surface vehicles,
whose development is funded separately by the National Oceanic and Atmospheric Administration (NOAA). The
TAOSF architecture provides an integrated approach to multi-vehicle coordination and sliding human-vehicle autonomy.
It allows multiple mobile sensing assets to function in a cooperative fashion, and the operating mode of the vessels to
range from autonomous control to teleoperated control. In this manner, TAOSF increases data-gathering effectiveness
and science return while reducing demands on scientists for tasking, control, and monitoring. It combines and extends
prior related work done by the authors and their institutions. The TAOSF architecture is applicable to other areas where
multiple sensing assets are needed, including ecological forecasting, water management, carbon management, disaster
management, coastal management, homeland security, and planetary exploration. The first field application chosen for
TAOSF is the characterization of Harmful Algal Blooms (HABs). Several components of the TAOSF system have been
tested, including the OASIS boats, the communications and control interfaces between the various hardware and
software subsystems, and an airborne sensor validation system. Field tests in support of future HAB characterization
were performed under controlled conditions, using rhodamine dye as a HAB simulant that was dispersed in a pond. In
this paper, we describe the overall TAOSF architecture and its components, discuss the initial tests conducted and
outline the next steps.
The domain and technology of mobile robotic space exploration are fast moving from brief visits to benign Mars surface regions to more challenging terrain and sustained exploration. Further, the overall venue and concept of space robotic exploration are expanding-“from flatland to 3D”-from the surface, to sub-surface and aerial theatres on disparate large and small planetary bodies, including Mars, Venus, Titan, Europa, and small asteroids. These new space robotic system developments are being facilitated by concurrent, synergistic advances in software and hardware technologies for robotic mobility, particularly as regard on-board system autonomy and novel thermo-mechanical design. We outline these directions of emerging mobile science mission interest and technology enablement, including illustrative work at JPL on terrain-adaptive and multi-robot cooperative rover systems, aerobotic mobility, and subsurface ice explorers.
Robotic unmanned aerial vehicles have an enormous potential as observation and data-gathering platforms for a wide variety of applications. This paper discusses components of a perception architecture being developed for AURORA (Autonomous Unmanned Remote Monitoring Robotic Airship). The AURORA project focuses on the development of the technologies required for substantially autonomous unmanned aerial vehicles, and for robotic airships in particular. We describe our approach to spatial representation, which incorporates a Markov Random Field (MRF) model used for encoding spatial inferences obtained from sensor imagery. We present a dynamic approach to target recognition that uses a cycle of hypothesis formulation, experiment planning for hypothesis validation, experiment execution, and hypothesis evaluation to confirm or reject the classification of targets into object classes. We also discuss an approach to automatic hovering and landing using visual servoing techniques and interaction matrices, and present preliminary experimental results from our work.
Estimation of superresolution models is a problem of great interest across a broad range of applications in computer vision and robot perception. However, approaches to superresolution model estimation tend to have very high computational complexity. In this paper, we address the superresolution model estimation problem using a general modeling approach based on two-layer Bayesian or causal networks. Sensor nodes encode stochastic sensor models, while model nodes encode probabilistic inferences made about their state. The model nodes are arranged as a MRF spatial lattice. We derive optimal estimation procedures for several classes of superresolution world models, including single and multiple observation models, and analyze their computational complexity. We subsequently introduce three suboptimal estimation methods: Reinjection of Marginals (ROM), Independent Opinion Pool (IOP), and Non-Propagation of Neighbors (NPN). These methods, although suboptimal, are extremely efficient and provide high-quality superresolution estimates. We conclude by presenting results from the application of these procedures to the fusion of multiple aerial images to form highly accurate superresolution images for airborne surveying and monitoring applications.