Solar Sailing Adaptive Control Using Integral Concurrent Learning for Solar Flux Estimation

In the interest of exploiting natural forces for propellant-less spacecraft missions, this thesis proposes an adaptive control strategy to account for unknown parameters in the dynamic modeling of a reflectivity-controlled solar sail spacecraft. A Lyapunov-based control law along with integral concurrent learning is suggested to accomplish and prove global exponential tracking of the estimated parameters and states of interest, without satisfying the common persistence of excitation condition, which in most nonlinear systems cannot be guaranteed a priori. This involves estimating the solar flux or irradiance from the Sun to account for uncertainty and variation over time in this value. To illustrate potential applications, two missions are considered: (1) a geostationary debris removal case and (2) an Earth-Mars interplanetary transfer orbit following a logarithmic spiral reference trajectory. The proposed formulation demonstrates the benefit of estimating the solar flux using integral concurrent learning. Results are compared to trajectories with no estimation to illustrate the need to account for solar flux fluctuations

Wolf spider burrows from a modern saline sandflat in central Argentina: Morphology, taphonomy and clues for recognition of fossil examples

Pavocosa sp. (Lycosidae) burrows found in an open sparsely vegetated area on the edge of the Gran Salitral saline lake, in central Argentina, are described. Burrows were studied by capturing the occupant and casting them with dental plaster. The hosting sediments and vegetation were also characterized. Inhabited Pavocosa sp. burrows display distinctive features as open, cylindrical, nearly vertical, silk lined shafts about 120 mm long, subcircular entrances, a gradual downward widening, and a particularly distinctive surface ornamentation in the form of sets of two linear parallel marks at a high angle to the burrow axis. Instead, casts of vacated Pavocosa sp. burrows showed some disturbances caused either by the reoccupation by another organism or by predation of the dweller. Two morphologies are related to reoccupation of burrows: those with a structure in form of an ``umbrella'' and another with smaller excavations at the bottom of the burrow. Predation by small mammals produces funnel-shaped burrows. Both active and abandoned Pavocosa sp. burrow casts are compared with existing ichnogenera and inorganic sedimentary structures, highlighting its distinction. It is argued that key features like the presence of a neck, a downward widening and the described surface texture will allow recognition of wolf spider burrows in the fossil record. However, the putative spider burrows described in the literature either lack the necessary preservational quality or do not show ornamentation similar to the modern wolf spider burrows. Fossil wolf spiders are recorded since the Paleogene (possibly Late Cretaceous), therefore Cenozoic continental rocks can contain wolf spider burrows awaiting recognition. In addition, the particular distribution of Pavocosa sp. in saline lakes may imply that this type of burrow is linked to saline environments.Fil: Mendoza Belmontes, Fatima del Rosario. Ministerio de Ciencia, Tecnología e Innovación Productiva. Agencia Nacional de Promoción Científica y Tecnológica. Fondo para la Investigación Científica y Tecnológica; Argentina. Universidad Nacional de La Pampa; ArgentinaFil: Melchor, Ricardo Nestor. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias de la Tierra y Ambientales de La Pampa. Universidad Nacional de La Pampa. Facultad de Ciencias Exactas y Naturales. Instituto de Ciencias de la Tierra y Ambientales de La Pampa; ArgentinaFil: Piacentini, Luis Norberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales “Bernardino Rivadavia”; Argentin

Decision-Theoretic Planning with Person Trajectory Prediction for Social Navigation

Robots navigating in a social way should reason about people intentions when acting. For instance, in applications like robot guidance or meeting with a person, the robot has to consider the goals of the people. Intentions are inherently nonobservable, and thus we propose Partially Observable Markov Decision Processes (POMDPs) as a decision-making tool for these applications. One of the issues with POMDPs is that the prediction models are usually handcrafted. In this paper, we use machine learning techniques to build prediction models from observations. A novel technique is employed to discover points of interest (goals) in the environment, and a variant of Growing Hidden Markov Models (GHMMs) is used to learn the transition probabilities of the POMDP. The approach is applied to an autonomous telepresence robot