Distance estimation and collision prediction for on-line robotic motion planning

An efficient method for computing the minimum distance and predicting collisions between moving objects is presented. This problem has been incorporated in the framework of an in-line motion planning algorithm to satisfy collision avoidance between a robot and moving objects modeled as convex polyhedra. In the beginning the deterministic problem, where the information about the objects is assumed to be certain is examined. If instead of the Euclidean norm, L(sub 1) or L(sub infinity) norms are used to represent distance, the problem becomes a linear programming problem. The stochastic problem is formulated, where the uncertainty is induced by sensing and the unknown dynamics of the moving obstacles. Two problems are considered: (1) filtering of the minimum distance between the robot and the moving object, at the present time; and (2) prediction of the minimum distance in the future, in order to predict possible collisions with the moving obstacles and estimate the collision time

An optimal control strategy for collision avoidance of mobile robots in non-stationary environments

An optimal control formulation of the problem of collision avoidance of mobile robots in environments containing moving obstacles is presented. Collision avoidance is guaranteed if the minimum distance between the robot and the objects is nonzero. A nominal trajectory is assumed to be known from off-line planning. The main idea is to change the velocity along the nominal trajectory so that collisions are avoided. Furthermore, time consistency with the nominal plan is desirable. A numerical solution of the optimization problem is obtained. Simulation results verify the value of the proposed strategy

Unified model for network dynamics exhibiting nonextensive statistics

We introduce a dynamical network model which unifies a number of network families which are individually known to exhibit $q$-exponential degree distributions. The present model dynamics incorporates static (non-growing) self-organizing networks, preferentially growing networks, and (preferentially) rewiring networks. Further, it exhibits a natural random graph limit. The proposed model generalizes network dynamics to rewiring and growth modes which depend on internal topology as well as on a metric imposed by the space they are embedded in. In all of the networks emerging from the presented model we find q-exponential degree distributions over a large parameter space. We comment on the parameter dependence of the corresponding entropic index q for the degree distributions, and on the behavior of the clustering coefficients and neighboring connectivity distributions.Comment: 11 pages 8 fig

On-Line Identification of Autonomous Underwater Vehicles through Global Derivative-Free Optimization

We describe the design and implementation of an on-line identification scheme for Autonomous Underwater Vehicles (AUVs). The proposed method estimates the dynamic parameters of the vehicle based on a global derivative-free optimization algorithm. It is not sensitive to initial conditions, unlike other on-line identification schemes, and does not depend on the differentiability of the model with respect to the parameters. The identification scheme consists of three distinct modules: a) System Excitation, b) Metric Calculator and c) Optimization Algorithm. The System Excitation module sends excitation inputs to the vehicle. The Optimization Algorithm module calculates a candidate parameter vector, which is fed to the Metric Calculator module. The Metric Calculator module evaluates the candidate parameter vector, using a metric based on the residual of the actual and the predicted commands. The predicted commands are calculated utilizing the candidate parameter vector and the vehicle state vector, which is available via a complete navigation module. Then, the metric is directly fed back to the Optimization Algorithm module, and it is used to correct the estimated parameter vector. The procedure continues iteratively until the convergence properties are met. The proposed method is generic, demonstrates quick convergence and does not require a linear formulation of the model with respect to the parameter vector. The applicability and performance of the proposed algorithm is experimentally verified using the AUV Girona 500. © 2013 IEEE

Flowstats: an ontology based network management tool

One of the problems that hinders large scale network management tasks is the number of possible heterogeneous data sources that provide network information and how to focus on a desired network segment without requiring a deep knowledge of the network structure. This work investigates how to intelligently and efficiently refine and manage a vast amount of network monitoring data sources, by using artificial intelligent reasoning through an intuitive user interface. We aim to minimise the user interaction and required user knowledge when searching for the desired network monitoring information by refining the presented information based on user choices. The concept of Ontology is utilised to create a knowledge base of multiple different aspects of our testbed: Internal Management structure, Physical Location of data sources, and network switch meta-data

Supporting new product commercialization through managerial social ties and market knowledge development in an emerging economy

University of Tasmania, Australi

Psychotic symptoms with and without a primary psychotic disorder in children requiring inpatient mental health admission

Psychotic symptoms are relatively common in children and adolescents attending mental health services. On most occasions, their presence is not associated with a primary psychotic disorder, and their clinical significance remains understudied. No studies to date have evaluated the prevalence and clinical correlates of psychotic symptoms in children requiring inpatient mental health treatment. All children aged 6 to 12 years admitted to an inpatient children's unit over a 9-year period were included in this naturalistic study. Diagnosis at discharge, length of admission, functional impairment, and medication use were recorded. Children with psychotic symptoms without a childhood-onset schizophrenia spectrum disorder (COSS) were compared with children with COSS and children without psychotic symptoms using Chi-square and linear regressions. A total of 211 children were admitted during this period with 62.4% experiencing psychotic symptoms. The most common diagnosis in the sample was autism spectrum disorder (53.1%). Psychotic symptoms were not more prevalent in any diagnosis except for COSS (100%) and intellectual disability (81.8%). Psychotic symptoms were associated with longer admissions and antipsychotic medication use. The mean length of admission of children with psychotic symptoms without COSS seems to lie in between that of children without psychotic symptoms and that of children with COSS. We concluded that psychotic symptoms in children admitted to the hospital may be a marker of severity. Screening for such symptoms may have implications for treatment and could potentially contribute to identifying more effective targeted interventions and reducing overall morbidity