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An Introduction to Swarm Robotics

Swarm robotics is a field of multi-robotics in which large number of robots are coordinated in a distributed and decentralised way. It is based on the use of local rules, and simple robots compared to the complexity of the task to achieve, and inspired by social insects. Large number of simple robots can perform complex tasks in a more efficient way than a single robot, giving robustness and flexibility to the group. In this article, an overview of swarm robotics is given, describing its main properties and characteristics and comparing it to general multi-robotic systems. A review of different research works and experimental results, together with a discussion of the future swarm robotics in real world applications completes this work.

Fuzzy controllers: lifting the linear-nonlinear frontier

Abstract This paper brings back some ideas related with the linear analysis of fuzzy controllers. A fuzzy control algorithm does not go further than a non-linear function described by its inference map. Firstly, it is possible to obtain the inference map corresponding to a classical PID controller by choosing adequately the linguistic terms, the membership functions and the table of rules. Then the inverse problem is presented: to obtain the closer PID to a given fuzzy controller. From these ideas, the paper focuses its attention on the obtainment of a useful tool to design fuzzy controllers at least as good as the PID that allows the system to follow a specified behaviour. The next step is to improve the fuzzy controller parameters. Finally, an industrial application of these ideas is shown. The design of a fuzzy controller over a clinker cooler with grill, improving P and PI controllers already functioning, is discussed.

The fuzzy Kalman filter: State estimation using possibilistic techniques

A new method to implement fuzzy Kalman filters is introduced. The combination of possibilistic techniques and the extended Kalman filter has special application in fields where inaccurate information is involved. The novelty of this article comes from the fact that by using possibility distributions, instead of Gaussian distributions, a fuzzy description of the expected state and observation is sufficient to obtain a good estimation. Some characteristics of this approach are that uncertainty does not need to be symmetric, and that a wide region of possible values for the expectations is allowed. To implement the algorithm, this approach also contributes a method to propagate uncertainty through the process model and the observation model, based on trapezoidal possibility distributions. Finally, several examples of a real mobile robot moving through a localization process, while using qualitative landmarks, are shown.

Multisensor Fusion: An Autonomous Mobile Robot

A conventional autonomous mobile robot is introduced. The main idea is the integration of many conventional and sophisticated sensor fusion techniques, introduced by several authors in recent years. We show the actual possibility of integrating all these techniques together, rather than analyzing implementation details. The topics of multisensor fusion, observation integration and sensor coordination are widely used throuhout the article. The final goal is to demonstrate the validity of both mathematical and artificial intelligence techniques in guaranteeing vehicle survival in a dynamic environment, while the robot carries out a specific task. We review conventional techniques for the management of uncertainty while we describe an implementation of a mobile robot which combines on-line heterogeneous sensors in its navigation and localisation tasks.

Building geometric feature based maps for indoor service robots

Abstract This paper presents an efficient geometric approach to the Simultaneous Localization and Mapping problem based on an Extended Kalman Filter. The map representation and building process is formulated, fully implemented and successfully experimented in different indoor environments with different robots. The use of orthogonal shape constraints is proposed to deal with the inconsistency of the estimation. Built maps are successfully used for the navigation of two different service robots: an interactive tour guide robot, and an assistive walking aid for the frail elderly.

Consistency improvement for SLAM - EKF for indoor environments

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

Local maps fusion for real time multirobot indoor simultaneous localization and mapping

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.

Fridges, elephants, and the meaning of autonomy and intelligence

Intelligence is the exploitation of information to perform better. From this perspective we propose a definition of autonomy that will be used to gather to a common ground different approaches to autonomy trying to combine some of their research topics under a common point of view. The paper concludes with some comments on the future of the discipline of artificial intelligence in real settings.

A Survey of Collective Movement of Mobile Robots

Collective movement of mobile robots is the problem of how to control a group of robots making them move as a group, in a cohesive way, towards a common direction. Collective movement serves not only to move a group of robots from one point to another, but to perform more complex tasks such as using the group of robots as a moving sensor array, collective mapping and searching tasks. In this article, a survey of collective movement of mobile robots is done, including a classification and characterization of its different types, a review of the most important architectures and a list of its promising applications.

Fast exact feature based data correspondence search with an efficient bit-parallel MCP solver

The problem of finding the optimal correspondence between two sets of geometric entities or features is known to be NP-hard in the worst case. This problem appears in many real scenarios such as fingerprint comparisons, image matching and global localization of mobile robots. The inherent complexity of the problem can be avoided by suboptimal solutions, but these could fail with high noise or corrupted data. The correspondence problem has an interesting equivalent formulation in finding a maximum clique in an association graph. We have developed a novel algorithm to solve the correspondence problem between two sets of features based on an efficient solution to the Maximum Clique Problem using bit parallelism. It outperforms an equivalent non bit parallel algorithm in a number of experiments with simulated and real data from two different correspondence problems. This article validates for the first time, to the best of our knowledge, that bit parallel optimization techniques can greatly reduce computational cost, thus making feasible the use of an exact solution in real correspondence search problems despite their inherent NP computational complexity.