This paper describes the development of a novel vision-based modeling and grasping system for three-dimensional (3D) objects whose shape and location are unknown a priori. Our approach integrates online computer vision-based 3D object modeling with online 3D grasp planning and execution. A single wrist-mounted video camera is moved around the stationary object to obtain images from multiple viewpoints. Object silhouettes are extracted from these images and used to form a 3D solid model of the object. To refine the model, the object's top surface is modeled by scanning with a wrist-mounted line laser while recording images. The laser line in each image is used to form a 3D surface model that is combined with the silhouette result. The grasp planning algorithm is designed for the parallel-jaw grippers that are commonly used in industry. The algorithm analyses the solid model, generates a robust force closure grasp, and outputs the required gripper position and orientation for grasping the object. The robot then automatically picks up the object. Experiments are performed with two real-world 3D objects, a metal bracket and a hex nut. The shape, position and orientation of the objects are not known by the system a priori. The time required to compute an object model and plan a grasp was less than 4 s for each object. The experimental results demonstrate that the automated grasping system can obtain suitable models and generate successful grasps, even when the objects are not lying parallel to the supporting table.

This study explores a national methodology for assessing the risk that peak wind gusts pose to Australian communities. This risk has been estimated for four cities across Australia, specifically; Perth, Cairns, Brisbane and the Gold Coast. Initially the return period regional wind speeds for three second peak gusts (estimation of the wind speeds that would be equaled or exceeded within a given time period) for the four study regions were obtained from the Australian wind loadings standard [1]. In addition, updated wind hazards for two cities were considered to explore the sensitivity of the assessed risk to refinements of the hazard definition. The local effects on the return period regional wind speeds used were subsequently determined by assessing the local effect of terrain at the structure height of interest, the shielding effect of up-wind buildings and the effect of topography. These effects were numerically estimated using remote sensing techniques, digital elevation data, and formulae presented in the wind loadings standard. The return period wind speeds were determined by combining the local wind multipliers for eight cardinal directions with the return period regional wind speeds (from [1] or other source) on a 25 metre grid across each study region. A structural vulnerability model estimated the economic loss for a given site wind speed. Damage levels across each region were assessed for various return periods (50, 100, 200, 500, 1000 and 2000 years) and the associated economic losses determined. Each city data set was regressed to obtain a corresponding Probable Maximum Loss (PML) curve. Finally, the regression curves were used to determine annualized losses for each study area.

We describe two things. First, we present a uniform framework for object oriented specification and verification of hardware. For this purpose the object oriented language “e” is introduced along with a powerful run-time environment that enables the designer to perform the verification task. Second, we present an object oriented synthesis that enhances “e” and its dedicated run-time environment into a framework for specification, verification, and synthesis. The usability of our approach is demonstrated by real-world examples.

The employment of computer generated forces (CGFs) within distributed virtual environments (DVEs) dramatically increases the number of entities in a simulated training environment. However, current CGF limitations produce behaviours that can be defeated using methods ineffective against humans. Our research focuses on developing aircraft CGFs. It is necessary to deal with uncertainty, ambiguity, and approximation. The Fuzzy Wingman (FW) relies on fuzzy logic to provide these abilities. In this manner, the FW presents a reasonable approach to effectively populating the simulated training environment with low cost CGFs while maintaining the realism of training with human controlled entities.

A phase transition in a connectionist model refers to a qualitative change in the model's behavior as parameters determining the spread of activation (gain, decay rate, etc.) pass through certain critical values. As connectionist methods have been increasingly adopted to model various problems in neuroscience, artificial intelligence, and cognitive science, there has been an increased need to understand and predict these phase transitions to assure meaningful model behavior. This paper extends previous results on phase transitions to encompass a class of connectionist models having rapidly varying connection strengths (“fast weights”). Phase transitions are predicted theoretically and then verified through a series of computer simulations. These results broaden the range of connectionist models for which phase transitions are identified and lay the foundation for future studies comparing models with rapidly varying and slowly varying connection strengths.