Stable Hybrid Fuzzy Controller-based Architecture for Robotic Telesurgery Systems

Robotic surgery and remotely controlled teleoperational systems are on the rise. However, serious limitations arise on both the hardware and software side when traditional modeling and control approaches are taken. These limitations include the incomplete modeling of robot dynamics, tool–tissue interaction, human– machine interfaces and the communication channel. Furthermore, the inherent latency of long-distance signal transmission may endanger the stability of a robot controller. All of these factors contribute to the very limited deployment of real robotic telesurgery. This paper describes a stable hybrid fuzzy controller-based architecture that is capable of handling the basic challenges. The aim is to establish high fidelity telepresence systems for medical applications by easily handled modern control solution

An evaluation of user acceptance of a corporate intranet

Intranets represent an important organisational resource for knowledge sharing. However, as yet, there has been little research into the quality of intranets and the impact of quality on intranet user acceptance. In the study reported in this paper, an intranet quality assessment tool comprising the dimensions usability, design, and information quality, is combined with perceived usefulness and social influence from the technology acceptance literature to create an intranet acceptance model. The model is applied to the sales and marketing division of an international manufacturing company. Data is collected via a Web survey (n=131, response rate = 65.5%) and tested using the partial least squares approach to structural equation modelling. The results show that intranet quality is a significant factor in determining behavioural intention to use, although it is less important than perceived usefulness and social influence. Comments collected from respondents are used to illustrate the findings and provide an insight into user behaviour. The discussion considers the implications, future research (e.g., the role of social influence in intranet usage) and limitations. The paper rounds off with a short summary

Second Workshop on Modelling of Objects, Components and Agents

This report contains the proceedings of the workshop Modelling of Objects, Components, and Agents (MOCA'02), August 26-27, 2002.The workshop is organized by the 'Coloured Petri Net' Group at the University of Aarhus, Denmark and the 'Theoretical Foundations of Computer Science' Group at the University of Hamburg, Germany. The homepage of the workshop is: http://www.daimi.au.dk/CPnets/workshop02

Episodic Memory for Cognitive Robots in Dynamic, Unstructured Environments

Elements from cognitive psychology have been applied in a variety of ways to artificial intelligence. One of the lesser studied areas is in how episodic memory can assist learning in cognitive robots. In this dissertation, we investigate how episodic memories can assist a cognitive robot in learning which behaviours are suited to different contexts. We demonstrate the learning system in a domestic robot designed to assist human occupants of a house. People are generally good at anticipating the intentions of others. When around people that we are familiar with, we can predict what they are likely to do next, based on what we have observed them doing before. Our ability to record and recall different types of events that we know are relevant to those types of events is one reason our cognition is so powerful. For a robot to assist rather than hinder a person, artificial agents too require this functionality. This work makes three main contributions. Since episodic memory requires context, we first propose a novel approach to segmenting a metric map into a collection of rooms and corridors. Our approach is based on identifying critical points on a Generalised Voronoi Diagram and creating regions around these critical points. Our results show state of the art accuracy with 98% precision and 96% recall. Our second contribution is our approach to event recall in episodic memory. We take a novel approach in which events in memory are typed and a unique recall policy is learned for each type of event. These policies are learned incrementally, using only information presented to the agent and without any need to take that agent off line. Ripple Down Rules provide a suitable learning mechanism. Our results show that when trained appropriately we achieve a near perfect recall of episodes that match to an observation. Finally we propose a novel approach to how recall policies are trained. Commonly an RDR policy is trained using a human guide where the instructor has the option to discard information that is irrelevant to the situation. However, we show that by using Inductive Logic Programming it is possible to train a recall policy for a given type of event after only a few observations of that type of event