Ramón Rizo

A Genetic Algorithm for Robust Motion Planning

This paper proposes a solution by genetic algorithms to the problem of planning a robust and suboptimal trajectory in the velocity space of a mobile robot. Robust trajectories are obtained introducing cumulative noise in the evaluation of the fitness function and introducing modifications in the genetic algorithm to taking into account this new feature. Results are presented that show the performance of the algorithm in different environments and the influence of the noise in the planned trajectories.

Junction detection and grouping with probabilistic edge models and Bayesian A

In this paper, we propose and integrate two Bayesian methods, one of them for junction detection, and the other one for junction grouping. Our junction detection method relies on a probabilistic edge model and a log-likelihood test. Our junction grouping method relies on 6nding connecting paths between pairs of junctions. Path searching is performed by applying a Bayesian A ∗ algorithm. Such algorithm uses both an intensity and geometric model for de6ning the rewards of a partial path and prunes those paths with low rewards. We have extended such a pruning with an additional rule which favors the stability of longer paths against shorter ones. We have tested experimentally the e:ciency and robustness of the methods in an indoor image sequence. ? 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

Use of mathematical morphology in real‐time path planning

If an autonomous vehicle is working in an image‐based system which needs real‐time answers and whose response is critical, it will be very important to reduce computation times and, as we know, this could be performed by increasing the system parallelism. Since morphological filtering is the origin of several applications in computer vision, in this paper we are going to describe some new features to implement morphological operations by using digital signal processors. After that, an application to path planning is proposed. The standard shortest path planning problem determines a collision‐free path of shortest distance between two distinct locations in an environment scattered with obstacles. Consequently, a path planning algorithm which uses morphological operations and a DSP to process images is then described.

Deformable Templates for Tracking and Analysis of Intravascular Ultrasound Sequences

Deformable Template models are first applied to track the inner wall of coronary arteries in intravascular ultrasound sequences, mainly in the assistance to angioplasty surgery. A circular template is used for initializing an elliptical deformable model to track wall deformation when inflating a balloon placed at the tip of the catheter. We define a new energy function for driving the behavior of the template and we test its robustness both in real and synthetic images. Finally we introduce a framework for learning and recognizing spatio-temporal geometric constraints based on Principal Component Analysis (eigenconstraints).

L-system-driven self-assembly for swarm robotics

In this paper, an assembly swarm algorithm, that will generate microscopic rules from a macroscopic description of complex structures, will be presented. The global structure will be described in a formal way using L-systems (Lindenmayer systems). The proposed algorithm is mainly parallel and exhibit parsimony at microscopic level, being robust and adaptable. In addition, a comparation between a swarm with centralized control and our distributed swarm algorithm will be provided, comparing the time need by the swarm to be assembled and the number of messages exchanged between agents.

Agents for Swarm Robotics: Architecture and Implementation

Swarm robotics is a type of robotic systems based on many simple robots interactions. Such systems enjoy many benefits such as high tolerance and the possibility of increasing the number of robots in a transparent way to the programmer; but they also have many difficulties when applied to complex problems. In this paper, we will present a hybrid architecture for swarm robotics based on a multi-agent system. The main contribution of this architecture is to make possible the use of cognitive agents to lead a robotic swarm of simple agents without losing the advantages of swarms. Moreover, the implementation of this architecture within Real Swarm platform and the discussion of how to apply this architecture in real systems will be presented.

Using Gaussian Processes in Bayesian Robot Programming

In this paper, we present an adaptation of Gaussian Processes for learning a joint probabilistic distribution using Bayesian Programming. More specifically, a robot navigation problem will be showed as a case of study. In addition, Gaussian Processes will be compared with one of the most popular techniques for machine learning: Neural Networks. Finally, we will discuss about the accuracy of these methods and will conclude proposing some future lines for this research.

Obtaining a Bayesian map for data fusion and failure detection under uncertainty

This paper presents a generic Bayesian map and shows how it is used for the development of a task done by an agent arranged in an environment with uncertainty. This agent interacts with the world and is able to detect, using only readings from its sensors, any failure of its sensorial system. It can even continue to function properly while discarding readings obtained by the erroneous sensor/s. A formal model based on Bayesian Maps is proposed. The Bayesian Maps brings up a formalism where implicitly, using probabilities, we work with uncertainly. Some experimental data is provided to validate the correctness of this approach.

Minimization of an energy function with robust features for image segmentation

In this work, we propose an approach to the model based on Markov random field (MRF) as a systematic way for integrating constraints for robust image segmentation. To do that, robust features and their integration in the energy function, which directs the process, have been defined. The suitability of the method has been verified by comparing classic features with the robust ones. In this approach, the image is first segmented into a set of disjoint regions and the adjacent graph (AG) has been determined. This approach is applied by defining an MRF model on the corresponding AG. Robust features are incorporated to the energy function by means of clique functions, and optimal segmentation is then achieved by finding a labelling configuration, which minimizes the energy function using the simulated annealing.

Bayesian Models for Finding and Grouping Junctions

In this paper, we propose two Bayesian methods for detecting and grouping junctions. Our junction detection method evolves from the Kona approach, and it is based on a competitive greedy procedure inspired in the region competition method. Then, junction grouping is accomplished by finding connecting paths between pairs of junctions. Path searching is performed by applying a Bayesian A*algorithm that has been recently proposed. Both methods are efficient and robust, and they are tested with synthetic and real images.