This paper presents the structure and operation principles of an emotional model. It is a dynamic state-space model where state variables represent the emotional state. Model matrices are time variant. A method based on fuzzy inference systems is developed to calculate the matrix coefficients at each time step. The simulation results of the guide robot case study are presented.

This paper presents a general purpose methodology to derive new heuristics for constraint satisfaction problems using the information contained in an auxiliary graph. The method is then applied to implement a powerful heuristic for Sudoku which is ‘almost perfect’ in the sense that a depth first search procedure needs very few backtracks to solve the 16x16 puzzle. The heuristic uses a least-constraining tie break for variable ordering, something very unusual in CSP domains. The paper also presents a minimal set of logical rules which the authors conjecture to solve all 9x9 Sudoku grids with unique solution with just one level of recursion at most.

It is the authors' belief that the ability of processors to compute bit parallel operations should have a right to exist as an optimization discipline, rather than a state-of- the-art technique. This paper is a step forward in this direction analysing a number of key issues related to bit model design and implementation of search problems. Building efficient search algorithms optimized at bit level is a difficult task It requires not only a good implementation but also an adequate model so as to make bitwise operations meaningful, and only by addressing design and implementation globally an improvement in efficiency can be obtained. On the other hand, we have proved in this paper that the improvement can conceivably be exponential on the size of the CPU register for problems in NP. This global approach has been used to implement the fastest complete algorithm, to the best of our knowledge, for the maximum clique problem, an important NP-hard combinatorial problem from the graph domain. Our solver clearly outperforms other existing algorithms for hard instances, in some cases by nearly an order of magnitude.

The solution to the simultaneous localization and mapping (SLAM) problem using an extended Kalman filter (EKF) is the most extended despite the inconsistency of its estimation, a problem that has been largely avoided in the literature. We review current existing approaches and present novel solutions to this problem that let us to build large monolithic feature based maps of indoor environments

This paper shows a way to speed up search by using an encoding at bit level to model a particular domain. A bitboard is an unsigned integer whose bits have been given an interpretation in the world of discourse. In models encoded in such a way, states will be described internally as a set of bitboard tuples, whilst operators which allow for transitions between states are essentially bitwise operations over elements belonging to that set. Given a 64-bit processor word, for each transition it would be theoretically possible to reach another state 64 times faster than with a normal encoding, fast transitions being one of the main issues for efficient search algorithms. We have analysed some other key issues related to efficient bitboard model design and formalised the concepts of well formed heuristics and bitboard guides. We have used this approach to solve instances of the maximum clique problem thus increasing the performance of one of the fastest algorithms known in the domain for optimal search

Guido is a healthcare robot that serves as support and navigation aid for the frail and visually impaired. The previous assistive navigation software has been replaced by a full map based control, including real time feature based map building (SLAM) and pose tracking (both using an EKF), and path planning and collision avoidance. Real experiments have been carried out, both in the lab and in the hospital, with great success, showing a high improvement in the ease of use and convenience of the system. The map building and editing tools have proved to be extremely useful, enabling the system to be installed in a new environment in just minutes.

This paper presents an implementation of the local maps fusion concept for the simultaneous localization and mapping (SLAM) problem within the extended Kalman filter (EKF) framework. Several problems never addressed before, arise while implementing the solution for indoor environments, and are successfully solved to obtain maps of quite large real indoor environments with more than one robot in real time.

Presents the first approach towards the main goal of developing a completely autonomous robot that serves as a local and remote guide at a trade fair. Innovative solutions are provided to solve several problems found in this environment. Reactive-perceptual behaviors are executed to provide motion while a low level controller avoids collisions. A virtual corridor map, simulated perception, and an extended Kalman filter for localization, are used to overcome the lack of perception. Voice synthesizing is proved to be an effective aid for navigation, as well as for the overall success and acceptance of the system. A Denning Mrv4 robot called Blacky was used to carry out experiments in actual environments on three occasions, and the obtained conclusions sound promising for future research.

In this paper, a fuzzy based sliding-mode (SM) control is presented. The main objective is to obtain an improved performance of highly non-linear unstable systems with guaranteed stability. Both the fuzzy controller and the controlled system are represented by the affine Takagi-Sugeno fuzzy model. The chattering problem which is considered the main drawback of the SM control is solved by introducing a boundary layer around the switching surface. The stability issue is also examined. Finally the proposed method is explained by applying it on an inverted pendulum. The results show a robust and stable behaviour when the system is subjected to a perturbation effect.

Stability methods for discrete fuzzy systems based on Lyapunov stability theorem are discussed. First, we represent the fuzzy system by a Takagi-Sugeno model and we recall its stability conditions. Second, we analyze the difficulties encountered in finding a general solution for stability of such systems, and then, we study the effect of the membership functions and the dependence of the basin of attraction on the type of such functions. The methods are illustrated by examples.

Digital regulators for continuous plants are designed today by means of conventionalz-transfer function methods. With thesez-transfer functions the value of the system outputy(t) can be learned only at instants spacedTseconds apart, whereTis the sampling period. A technique is proposed where the value ofy(t) can be learned at instants spacedT/nseconds apart, wherenis any integer. This technique is based on multirate sampling. First a method to calculate the output multirate sampled sequence is described. Then an efficient algorithm to calculate the multirate sampling discrete transfer function from the normal discrete transfer function is proposed.