Autonomous behaviors for an unmanned mini-aerial vehicle

This thesis aims at addressing the development of autonomous behaviors, for search and exploration with a mini-UAV (Unmanned Aerial Vehicle), or also called MAV (Mini Aerial Vehicle) prototype, in order to gather information in rescue scenarios. The platform used in this work is a four rotor helicopter, known as quad-rotor from the German company Ascending Technologies GmbH, which is later assembled with a on-board processing unit (i.e. a tiny light weight computer) and a on-board sensor suite (i.e. 2D-LIDAR and Ultrasonic Sonar). This work can be divided into two phases. In the first phase an Indoor Position Tracking system was settled in order to obtain the Cartesian coordinates (i.e. X, Y, Z) and orientation (i.e.heading) which provides the relative position and orientation of the platform. The second phase was the design and implementation of medium/high level controllers on each command input in order to autonomously control the aircraft position, which is the first step towards an autonomous hovering flight, and any autonomous behavior (e.g. Landing, Object avoidance, Follow the wall). The main work is carried out in the Laboratory ”Intelligent Systems for Emergencies and Civil Defense”, in collaboration with ”Dipartimento di Informatica e Sistemistica” of Sapienza Univ. of Rome and ”Istituto Superiore Antincendi” of the Italian Firemen Department.

Playing Newtonian Games with Modellus

This article is a short introduction on how to use Modellus (a computer package that is freely available on the Internet and used in the IOP Advancing Physics course) to build physics games using Newton’s laws, expressed as differential equations. Solving systems of differential equations is beyond most secondary-school or first-year college students. However, with Modellus, the solution is simply the output of the usual physical reasoning: define the force law, compute its magnitude and components, use it to obtain the acceleration components, then the velocity components and, finally, use the velocity components to find the coordinates.

A go-to-market strategy for vertebral metrics

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Fsp27/CIDEC is a CREB target gene induced during fasting and regulated by fatty acid oxidation rate

Dissertação para obtenção do Grau de Mestre em Biotecnologia

From the portolan chart of the mediterranean to the latitude chart of the atlantic

Thesis submitted to the Instituto Superior de Estatística e Gestão de Informação da Universidade Nova de Lisboa in partial fulfillment of the requirements for the Degree of Doctor of Philosophy in Information Management – Geographic Information Systems

Travel simulation inside an Immersive Video Environment (IVE)

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Range of motion measurements based on depth camera for clinical rehabilitation

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Extension and patterning of the vertebrate body

Dissertation presented to obrain the Ph.D degree in Biology. Developmental Biology

Constraint reasoning with local search for continuous optimization

Optimization is a very important field for getting the best possible value for the optimization function. Continuous optimization is optimization over real intervals. There are many global and local search techniques. Global search techniques try to get the global optima of the optimization problem. However, local search techniques are used more since they try to find a local minimal solution within an area of the search space. In Continuous Constraint Satisfaction Problems (CCSP)s, constraints are viewed as relations between variables, and the computations are supported by interval analysis. The continuous constraint programming framework provides branch-and-prune algorithms for covering sets of solutions for the constraints with sets of interval boxes which are the Cartesian product of intervals. These algorithms begin with an initial crude cover of the feasible space (the Cartesian product of the initial variable domains) which is recursively refined by interleaving pruning and branching steps until a stopping criterion is satisfied. In this work, we try to find a convenient way to use the advantages in CCSP branchand- prune with local search of global optimization applied locally over each pruned branch of the CCSP. We apply local search techniques of continuous optimization over the pruned boxes outputted by the CCSP techniques. We mainly use steepest descent technique with different characteristics such as penalty calculation and step length. We implement two main different local search algorithms. We use “Procure”, which is a constraint reasoning and global optimization framework, to implement our techniques, then we produce and introduce our results over a set of benchmarks.