ADVANCES INROBOTNAVIGATION  EditedbyAlejandraBarrera             Advances in Robot Navigation Edited by Alejandra Barrera Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2011 InTech All chapters are Open Access articles distributed under the Creative Commons Non Commercial Share Alike Attribution 3.0 license, which permits to copy, distribute, transmit, and adapt the work in any medium, so long as the original work is properly cited. After this work has been published by InTech, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book. Publishing Process Manager Natalia Reinić  Technical Editor Teodora Smiljanic Cover Designer Jan Hyrat Image Copyright VikaSuh, 2010. Used under license from Shutterstock.com First published June, 2011 Printed in Croatia A free online edition of this book is available at www.intechopen.com Additional hard copies can be obtained from orders@intechweb.org Advances in Robot Navigation Edited by Alejandra Barrera p. cm. ISBN 978-953-307-346-0 free online editions of InTech Books and Journals can be found at www.intechopen.com   Contents  Preface IX Part 1 Robot Navigation Fundamentals 1 Chapter 1 Conceptual Bases of Robot Navigation Modeling, Control and Applications 3 Silas F. R. Alves, João M. Rosário, Humberto Ferasoli Filho, Liz K. A. Rincón and Rosana A. T. Yamasaki Chapter 2 Vision-only Motion Controller for Omni-directional Mobile Robot Navigation 29 Fairul Azni Jafar, Yuki Tateno, Toshitaka Tabata, Kazutaka Yokota and Yasunori Suzuki Chapter 3 Application of Streaming Algorithms and DFA Learning for Approximating Solutions to Problems in Robot Navigation 55 Carlos Rodríguez Lucatero Chapter 4 SLAM and Exploration using Differential Evolution and Fast Marching 81 Santiago Garrido, Luis Moreno and Dolores Blanco Part 2 Adaptive Navigation 99 Chapter 5 Adaptive Navigation Control for Swarms of Autonomous Mobile Robots 101 Yasuhiro Nishimura, Geunho Lee, Nak Young Chong, Sang Hoon Ji and Young-Jo Cho Chapter 6 Hybrid Approach for Global Path Selection & Dynamic Obstacle Avoidance for Mobile Robot Navigation 119 D. Tamilselvi, S. Mercy Shalinie, M. Hariharasudan and G. Kiruba Chapter 7 Navigation Among Humans 133 Mikael Svenstrup VI Contents Part 3 Robot Navigation Inspired by Nature 159 Chapter 8 Brain-actuated Control of Robot Navigation 161 Francisco Sepulveda Chapter 9 A Distributed Mobile Robot Navigation by Snake Coordinated Vision Sensors 179 Yongqiang Cheng, Ping Jiang and Yim Fun Hu Part 4 Social Robotics 205 Chapter 10 Knowledge Modelling in Two-Level Decision Making for Robot Navigation 207 Rafael Guirado, Ramón González, Fernando Bienvenido and Francisco Rodríguez Chapter 11 Gait Training using Pneumatically Actuated Robot System 223 Natasa Koceska, Saso Koceski, Pierluigi Beomonte Zobel and Francesco Durante .    Preface  Robot navigation includes different interrelated activities such as perception‐ obtainingandinterpretingsensoryinformation; exploration‐thestrategy that guides the robot to select the next direction to go; mapping‐the construction of a spatial representation by using the sensory information perceived; localization‐the strategy to estimate the robot position within the sp atial map; path planning ‐the strategy to find a path towards a goal location being optimal or not; and path execution, where motoractionsaredeterminedandadaptedtoenvironmentalchanges. The book integrates results from the research work of several authors all over the world, addressing the abovementioned activities and analyzing the critical im plications of dealing with dynamic environments. Different solutions providing adaptive navigation are taken from nature inspiration and diverse applications are describedinthecontextofanimportantfieldofstudy,socialrobotics. Thebookincludes11chaptersorganizedwithin4partsasfollows. RobotNavigationFundamentals In order to contex tualize the different approaches proposed by authors, this part provides an overview of core concepts involved in robot navigation. Specifically, Chapter1 introducesthe basics ofa mobile robot physicalstructure, its dynamic and kinematic modeling, the mechanisms for mapping, localization, and trajectory planning and reviews the state of the art of navigation methods and control architectures which enables high degree of autonomy. Chapter 2 describes a navigational system providing vision‐based localization and topological mapping of theenvironment.Chapter3depictspotentialproblemswhichmightariseduringrobot motion planning, while trying to define the appropriate sequence of movements to achieveagoalwithinanunce rtainenvironment. Closing this part, Chapter 4 presents a robot navigation method  combining an exploratory strategy that drives the robot to the most unexplored region of the environment, a SLAM algorithm to build a consistent map, and the Voronoi Fast Marchingtechniquetoplanthetrajectoryto wardsthegoal. X Preface AdaptiveNavigation Real scenarios involve uncertainty, thus robot navigation must deal with dynamic environments. The chapters included within this part are concerned with environmentaluncertaintyproposingnovelapproachestothischallenge.Particularly, Chapter 5 presents a multilayered approach to wheeled mobile robot  navigation incorporating dynamic mapping, deliberative planning, path following, and two dist inct layers of point‐to‐point reactive control. Chapter 6 describes a robot path planning strategy within an indoor environment employing the Distance Transform methodology and the Gilbert–Johnson–Keerthi distance algorithm to avoid colliding with dynamic obstacles. This hybrid method enables the robot to select the shortest pathtothegoaldu ringnavigation.Finally,Chapter7proposesanadaptivesystemfor natural motion interaction between mobile robots and humans. The system finds the position and orientation of people by using a laser range finder based method, estimates human intentions in real time through a Case‐Based Reasoning approach, allowsthe robot tonavigate arounda pers onby means ofanadaptive potential field that adjusts according to the person intentions, and plans a safe and comfortable trajectory employing an adapted Rapidly‐exploring Random Tree algorithm. The robotcontrolledbythissystemisendowedwiththeabilitytoseehumansasdynamic obstacleshavin gsocialzonesthatmustberespected. RobotNavigationInspiredbyNature In this part, authors focused on nature of proposing interesting approaches to robot navigation.Specifically,Chapter8addressesbrain interfaces‐systemsaimingtoena‐ bleuser control ofadevicebased on brain activity‐related signals. The author is con‐ cerned with brain‐computer interfaces that use non‐invasive technology, discussing theirpotentialbenefitstothefieldofrobotnavigation,especiallyindifficultscenarios in which the robot cannot successfully perform all functions without human assis‐ tance,suchasindangerousareaswheresensorsoralgorithmsmayfail. On the other hand, Chapter 9 investig ates the use of animal low level intelligence to controlrobotnavigation.Authorstookinspirationfrominsecteyeswithsmallnervous systems mimicking a mosaic eye to propose a bio‐mimetic snake algorithm that di‐ videstherobotpathintosegmentsdistributedamongdifferentvisionsen sorsproduc‐ ingcollisionfreenavigation. SocialRobotics Oneofthemostattractiveapplicationsofroboticsis,withoutdoubt,thehuman‐robot interactionby providing useful services. This final partincludes practicalcasesof ro‐ botsservingpeopleintwoimportantfields:guidingandrehabilitation. In Chapter 10, authors present a social robot specif ically designed and equipped for human‐robotinteraction,includingallthebasiccomponentsofsensorizationandnav‐ igation within real indoor/outdoor environments, and a two‐level decision making [...]... avoidance In the sense of positioning, sensors can be classified as relative or absolute (Borenstein et al., 1995) Relative positioning sensors includes odometry and inertial navigation, which are methods that measures the robot position in relation to the robot initial point and its movements Distinctively, absolute positioning sensors recognize structures on the environment which position is known, allowing... are related to the internal elements of the robot, so they monitor the state of its inner mechanisms and devices, including joints positions In a different manner, exteroceptive sensors gather information from the environment where the robot is placed and generally are related to the robot navigation and application From the viewpoint of the measuring method, sensors are classified into active and passive... magnetic compasses readings are affected by power lines, metal structures, and even the robot movement, which introduces error to the system (Ojeda & Borenstein, 2000) 6 Advances in Robot Navigation Active beacons are devices which emits a signal that is recognized by the robot Since the active beacons are placed in known locations, the robot is able to estimate its position using triangulation or trilateration... color while moving in a certain way The robot navigates through line following and odometry a) b) Fig 12 Platform Robots (Mainardi, 2010): a)ASURO, b) Robotino 4.2 Mapping and location The localization task uses an internal representation of the world as an map of environment to find the position through the environment perception around them The topological maps divide the search space in nodes and... 13) Mapping and location can guide the 21 Conceptual Bases of Robot Navigation Modeling, Control and Applications robot in different environments, these methods give information about of objects in the space a) b) Fig 13 a) Topological Map, b) Map with frame path (Mainardi,2010) 4.3 Path and trajectory planning The path planning provides the points where the robot must pass For this, the planning uses... are related to finding the correspondence between a local map, discovered with the robot sensors, and a known global map (Borenstein et al., 1995) Inside model matching techniques, we can point out the Simultaneous Localization and Mapping (SLAM) The SLAM addresses to the problem of acquiring the map of the environment where the robot is placed while simultaneously locating the robot in relation to... of organizing the production chain, optimizing processes and reducing execution times 4.1 Navigation robots platforms The robots navigation platform uses one ASURO robot with hybrid control architecture AuRA (Mainardi, 2010), where the reactive layer uses motor-schemas based on topological maps for navigation The environment perception is obtained through of signals from sensors The ASURO robot has... positioning uses odometry or inertial navigation Odometry is a simple and inexpensive navigation system; however it suffers from cumulative errors The inertial navigation (Barshan & Durrant-White, 1995) uses rotation and acceleration measures for extracting positioning information Barshan and Durrant-White (1995) presented an inertial navigation system and discusses the challenges related to mobile robot. .. the inconsistency and uncertainties about of the information, Cooperative: different sensors which works together to measure a phenomena that a single sensor is not capable of measuring, Independent: independent sensors are those of which measures does not affect or complement other sensor data 2.2.1 Robot Navigation sensors When dealing with Robot Navigation, sensors are usually used for positioning... autonomy, a mobile robot must use a control architecture The architecture is closely linked to how the sensor data are handled to extract information from the world and how this information is used for planning and navigating in pursuit of the objectives, besides involving technological issues (Rich & Knight, 1994) It is defined by the principle of operation of control modules, which defines the functional . The robot controlledbythissystemisendowedwiththeabilitytoseehumansasdynamic obstacleshavin gsocialzonesthatmustberespected. Robot Navigation InspiredbyNature In this part, authors focused on nature of proposing interesting approaches to robot navigation. Specifically,Chapter8addressesbrain interfaces‐systemsaimingtoena‐ bleuser. 2011 Printed in Croatia A free online edition of this book is available at www.intechopen.com Additional hard copies can be obtained from orders@intechweb.org Advances in Robot Navigation. approach to wheeled mobile robot  navigation incorporating dynamic mapping, deliberative planning, path following, and two dist inct layers of point‐to‐point reactive control.