Recurrent neural robot controllers: feedback mechanisms for identifying environmental motion dynamics

In this paper a series of recurrent controllers for mobile robots have been developed. The system combines the iterative learning capability of neural controllers and the optimisation ability of particle swarms. In particular, three controllers have been developed: an Exo-sensing, an Ego-sensing and a Composite controller which is the hybrid of the latter two. The task for each controller is to learn to follow a moving target and identify its trajectory using only local information. We show how the learned behaviours of each architecture rely on different sensory representations, although good results are obtained in all cases

The role of sensory-motor coordination: identifying environmental motion dynamics with dynamic neural networks

We describe three recurrent neural architectures inspired by the proprioceptive system found in mammals; Exo-sensing, Ego-sensing, and Composite. Through the use of Particle Swarm Optimisation the robot controllers are adapted to perform the task of identifying motion dynamics within their environment. We highlight the effect of sensory-motor coordination on the performance in the task when applied to each of the three neural architectures

A dissimilarity visualisation system for CT : Pilot study

One of the capabilities of the human vision process when visualising images is the ability to visualise them at different levels of details. A segmentation procedure has been developed to mimic this capability of human vision process. The developed hierarchical clustering based segmentation (HCS) procedure automatically generates a hierarchy of segmented images. The hierarchy represents the continuous merging of similar, spatially adjacent or disjoint, regions as the allowable threshold value of dissimilarity between regions, for merging, is gradually increased. By the very nature of the HCS procedure a large amount of visual information is produced. A graphical user interface (GUI) was designed to present the segmentation output in an informative way for the user to view and interpret. In addition the GUI displays the original image data by optimally mapping the range of data values to the available 256 gray level values. The purpose of this paper is to describe the development of the designed image visualisation system and to demonstrate some of its functionalities

Machine vision methods for autonomous micro-robotic systems

Purpose - To develop customised machine vision methods for closed-loop micro-robotic control systems. The micro-robots have applications in areas that require micro-manipulation and micro-assembly in the micron and sub-micron range. Design/methodology/approach - Several novel techniques have been developed to perform calibration, object recognition and object tracking in real-time under a customised high-magnification camera system. These new methods combine statistical, neural and morphological approaches. Findings - An in-depth view of the machine vision sub-system that was designed for the European MiCRoN project (project no. IST-2001-33567) is provided. The issue of cooperation arises when several robots with a variety of on-board tools are placed in the working environment. By combining multiple vision methods, the information obtained can be used effectively to guide the robots in achieving the pre-planned tasks. Research limitations/implications - Some of these techniques were developed for micro-vision but could be extended to macro-vision. The techniques developed here are robust to noise and occlusion so they can be applied to a variety of macro-vision areas suffering from similar limitations. Practical implications - The work here will expand the use of micro-robots as tools to manipulate and assemble objects and devices in the micron range. It is foreseen that, as the requirement for micro-manufacturing increases, techniques like those developed in this paper will play an important role for industrial automation. Originality/value - This paper extends the use of machine vision methods into the micron range

Computer vision methods for optical microscopes

As the fields of micro- and nano-technology mature, there will be an increased need to build tools that are able to work in these areas. Industry will require solutions for assembling and manipulating components, much as it has done in the macro range. With this need in mind, a new set of challenges requiring novel solutions have to be met. One of them is the ability to provide closed-loop feedback control for manipulators. We foresee that machine vision will play a leading role in this area. This paper introduces a technique for integrating machine vision into the field of micro-technology including two methods, one for tracking and one for depth reconstruction under an optical microscope. (C) 2006 Elsevier B.V. All rights reserved

Advanced transmission electron microscope triboprobe with automated closed-loop nanopositioning

Here the design and operation of a novel transmission electron microscope (TEM) triboprobe instrument with real-time vision control for advanced in situ electron microscopy is demonstrated. The NanoLAB triboprobe incorporates a new high stiffness coarse slider design for increased stability and positioning performance. This is linked with an advanced software control system which introduces both new and flexible in situ experimental functional testing modes, plus an automated vision control feedback system. This advancement in instrumentation design unlocks new possibilities of performing a range of new dynamical nanoscale materials tests, including novel friction and fatigue experiments inside the electron microscope

Evolution of Undrained Strength Under a Test Embankment

Increased traffic and environmental loads necessitate re-evaluation of the stability of existing road and railway embankments built on soft sensitive clays. Thus, the current mobilised undrained shear strength needs to be quantified. A methodology to evaluate changes in undrained shear strength under embankment loading is developed and applied for the case of Haarajoki test embankment. The methodology combines boundary value modelling of embankment loading with integration point level stress path probing using the Creep-SCLAY1S model. The changes in the stress state and the relevant state parameters resulting from the boundary value modelling enable the quantification of the mobilised undrained shear strength. The results indicate an increase in the undrained shear strength up to 17% in the most affected clay layer. The high pre-overburden pressure in the top of clay deposit prevents significant changes in the undrained shear strength in the case of Haarajoki. Thus, when assessing changes in the undrained shear strength, one of main parameters to determine is the initial preconsolidation pressure

Developing robust vision modules for microsystems applications

In this work, several robust vision modules are developed and implemented for fully automated micromanipulation. These are autofocusing, object and end-effector detection, real-time tracking and optical system calibration modules. An image based visual servoing architecture and a path planning algorithm are also proposed based on the developed vision modules. Experimental results are provided to asses the performance of the proposed visual servoing approach in positioning and trajectory tracking tasks. Proposed path planning algorithm in conjunction with visual servoing imply successful micromanipulation tasks