Applying Evolutionary Optimisation to Robot Obstacle Avoidance

This paper presents an artificial evolutionbased method for stereo image analysis and its application to real-time obstacle detection and avoidance for a mobile robot. It uses the Parisian approach, which consists here in splitting the representation of the robot's environment into a large number of simple primitives, the "flies", which are evolved following a biologically inspired scheme and give a fast, low-cost solution to the obstacle detection problem in mobile robotics

Applying Evolutionary Optimisation to Robot Obstacle Avoidance

This paper presents an artificial evolutionbased method for stereo image analysis and its application to real-time obstacle detection and avoidance for a mobile robot. It uses the Parisian approach, which consists here in splitting the representation of the robot's environment into a large number of simple primitives, the "flies", which are evolved following a biologically inspired scheme and give a fast, low-cost solution to the obstacle detection problem in mobile robotics

Evolutionary Optimisation for Obstacle Detection and Avoidance in Mobile Robotics

http://www.fujipress.jpThis paper presents an artificial evolution-based method for stereo image analysis and its application to real-time obstacle detection and avoidance for a mobile robot. It uses the Parisian approach, which consists here in splitting the representation of the robot's environment into a large number of simple primitives, the “flies”, which are evolved according to a biologically inspired scheme. Results obtained on real scene with different fitness functions are presented and discussed, and an exploitation for obstacle avoidance in mobile robotics is proposed

Application de techniques d'évolution artificielle à la stéréovision en robotique mobile autonome

L' Algorithme des Mouches est un algorithme évolutionnaire de vision stéréo. Il permet de donner une représentation 3D approximative - sous forme de nuage de points - des surfaces visibles des objets d'une scène, à partir d'une paire d'images. Afin d'améliorer son efficacité et sa précision en vue d'une application temps réel en robotique mobile autonome, diverses voies d'amélioration de l'algorithme sont étudiées et évaluées (fonction de fitness, opérateurs génétiques, auto-adaptivité, etc.). Trois catégories d'applications sont explorées. Tout d'abord, une manière de dresser une carte de l'environnement d'un robot mobile à partir des prises de vues successives traitées par l'algorithme est présentée. Un deuxième type d'application s'appuie sur la considération de vitesses propres aux points de l'espace considérés, et cherche à déterminer les vitesses des objets de la scène. Enfin, une application à la conduite automatisée (arrêt et évitement d'obstacles) est présentée sur véhicule électrique (CyCab).The "Fly Algorithm" is an evolutionary algorithm used for stereo reconstruction It gives a rough description of the visible surfaces of the objects in a 3D scene, from a couple of stereo To fmprove the efficiency and precision of the algorithm so as to apply its output to autonomous mobile robotics, several ways of improvement are examined and^ evaluated (fitness function, genetic operators, self-adaptivity etc.). Three categories of applications are then explored. Firstly, a way to build a map of the environment of a mobile robot from he successive frames processed by the algorithm. A second type of application relies on the consideration of a speed given to each considered 3D points, and aims to determine the speeds of the objects of the scene. Finally, and application to automated driving (stop and obstacle avoidance) is presented on an electrical vehicle (Cycab).PARIS5-BU Saints-Pères (751062109) / SudocSudocFranceF

User-centric image segmentation using an interactive parameter adaptation tool

Creating successful machine vision systems often begins a process of developing customised reliable image segmentation algorithms for the detection, and possibly categorisation of regions of interest within images. This can require significant investment of time from both the image processing and the domain experts to set up. Frequently this process is mediated via interviews, or language-based systems which may not fully capture the visual decision-making process of the domain experts. The resulting algorithms can also often be "brittle" in the sense of being highly specialised to the task for which they are tuned, and are consequently sensitive to changes in operating conditions or image specifications. One approach is to use interactive evolution for developing rapidly reconfigurable systems in which the users' tacit knowledge and requirements can be elicited and used for finding the appropriate parameters to achieve the required segmentation without any need for specialised knowledge of the underlying machine vision systems. This paper presents an interactive tool that can be used to quickly and easily evolve optimal image segmentation parameters from scratch. Building on previous work, the new algorithm reported here incorporates user-guided local search and makes the fitness function more flexible to facilitate the underlying multi-objective decision-making process. One of the key requirements for any interactive system is a high level of usability, both in terms of effectiveness-being able to build accurate models that meet end-user requirements-and efficiency-being able to achieve the required results within a minimal amount of time and undue effort. The system described in this paper has been designed with these considerations in mind to ensure a high level of user-experience of the interaction process. We present results from a series of experiments with a range of users to analyse the effect of the improvements that have been made over the previous system. The efficiency of the tool is also tested with "novice users", and its usability by "novice users" is analysed. © 2009 Elsevier Ltd. All rights reserved

Obstacle detection by Evolutionary Algorithm: the Fly Algorithm

Artificial vision is a key element in robots autonomy. The Fly algorithm is a fast evolutionary algorithm designed for real time obstacle detection using pairs of stereo images. It aims to be used in particular in the fields of mobile robotics and automated vehicles. Based on the Parisian approach, the Fly algorithm produces a set of 3-D points which gather on the surfaces of obstacles. This paper describes the use of the Fly algorithm for obstacle detection in a real environment, and a possible use for vehicle control is presented

Paper: jc9-6-2402 2005/7/29 Evolutionary Optimisation for Obstacle Detection and Avoidance

This paper presents an artificial evolution-based method for stereo image analysis and its application to real-time obstacle detection and avoidance for a mobile robot. It uses the Parisian approach, which consists here in splitting the representation of the robot’s environment into a large number of simple primitives, the “flies”, which are evolved according to a biologically inspired scheme. Results obtained on real scene with different fitness functions are presented and discussed, and an exploitation for obstacle avoidance in mobile robotics is proposed

Performance of hidden Markov model and dynamic Bayesian network classifiers on handwritten Arabic word recognition

This paper presents a comparative study of two machine learning techniques for recognizing handwritten Arabic words, where hidden Markov models (HMMs) and dynamic Bayesian networks (DBNs) were evaluated. The work proposed is divided into three stages, namely preprocessing, feature extraction and classification. Preprocessing includes baseline estimation and normalization as well as segmentation. In the second stage, features are extracted from each of the normalized words, where a set of new features for handwritten Arabic words is proposed, based on a sliding window approach moving across the mirrored word image. The third stage is for classification and recognition, where machine learning is applied using HMMs and DBNs. In order to validate the techniques, extensive experiments were conducted using the IFN/ENIT database which contains 32,492 Arabic words. Experimental results and quantitative evaluations showed that HMM outperforms DBN in terms of higher recognition rate and lower complexity