Adaptive autonomous navigation of mobile robots in unknown environments

Autonomous navigation of a mobile robot is a challenging task. Much work has been done in indoor navigation in the last decade. Fewer results have been obtained in outdoor robotics. Since the early 90's, the Global Positioning System (GPS) has been the main navigation system for ships and aircrafts. In open fields, satellite navigation gives absolute position accuracy. The absolute heading information is also obtained by satellite navigation when the mobile robot is in motion. However, the use of GPS satellite navigation is mainly restricted to open areas where at least three satellites can be seen. For example, mobile robots working in underground or deep open mines cannot use satellite navigation at all, and in forest or city areas, there are serious limitations to its use. Laser range finder technology has evolved remarkably over the last decade, and offers a fast and accurate method for environment modeling. Furthermore, it can be used to define robot position and heading relative to the environment. It is obvious that the use of several alternative sensors according to the environment will make the navigation system more flexible. Laser range finder technology is particularly suitable for indoors or feature rich outdoor environments. The goal of this thesis is to develop a multi sensor navigation system for unknown outdoor environments, and to verify the system with a service robot. Navigation should be possible in unstructured outdoors as well as indoor environments. The system should use all available sensor information and emphasize those that best suit the particular environment. The sensors considered in this thesis include a scanning laser range finder, a GPS receiver, and a heading gyro. The main contribution of the thesis is a flexible navigation system developed and tested with a service robot performing versatile tasks in an outdoor environment. The used range matching method is novel and has not been verified earlier in outdoor environments. No unique solution can be guaranteed in the developed map matching algorithm, although it seems to work well in the practical tests. Position and heading errors grow without bound in successive map matchings, which could be referred to as laser odometry. Therefore, the position and heading have been corrected by means of global matching when the robot returns to a place it has previously visited. Alternatively, structured landmarks have been used for position and heading correction. In field tests, tree trunks and walls have been used as structured …

[1]  Hugh F. Durrant-Whyte,et al.  Map-building and map-based localization in an underground-mine by statistical pattern matching , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[2]  Wolfram Burgard,et al.  Tracking multiple moving targets with a mobile robot using particle filters and statistical data association , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[3]  Werner von Seelen,et al.  VISOCAR: an autonomous industrial transport vehicle guided by visual navigation , 1989, Proceedings, 1989 International Conference on Robotics and Automation.

[4]  Hans P. Moravec,et al.  High resolution maps from wide angle sonar , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[5]  Peter I. Corke,et al.  Steering kinematics for a center-articulated mobile robot , 2001, IEEE Trans. Robotics Autom..

[6]  Rafael Murrieta-Cid,et al.  Visual Navigation in Natural Environments: From Range and Color Data to a Landmark-Based Model , 2002, Auton. Robots.

[7]  Kurt Konolige,et al.  Markov Localization using Correlation , 1999, IJCAI.

[8]  Wolfram Burgard,et al.  Monte Carlo localization for mobile robots , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[9]  Experimental modelling of time-of-flight sonar , 1998, Robotics Auton. Syst..

[10]  E. Nebot,et al.  Simultaneous Localization and Map Building Using Natural features in Outdoor Environments , .

[11]  R. Shumway,et al.  AN APPROACH TO TIME SERIES SMOOTHING AND FORECASTING USING THE EM ALGORITHM , 1982 .

[12]  Tucker R. Balch,et al.  Constraint-Based Landmark Localization , 2002, RoboCup.

[13]  Patric Jensfelt.  Localization using laser scanning and minimalistic environmental models , 1999 .

[14]  Hannu Mäkelä,et al.  Overview of LHD navigation without artificial beacons , 2001, Robotics Auton. Syst..

[15]  Riccardo Poli,et al.  Robust mobile robot localisation from sparse and noisy proximity readings using Hough transform and probability grids , 2001, Robotics Auton. Syst..

[16]  Eduardo Nebot,et al.  The Use of Quaternions In a Low Cost Strapdown INS Unit , 1998 .

[17]  Eduardo Mario Nebot,et al.  Localisation in large-scale environments , 2001, Robotics Auton. Syst..

[18]  Kai Oliver Arras,et al.  Hybrid, high-precision localisation for the mail distributing mobile robot system MOPS , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[19]  Hugh F. Durrant-Whyte,et al.  Natural landmark-based autonomous navigation using curvature scale space , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[20]  Nick Pears,et al.  Feature extraction and tracking for scanning range sensors , 2000, Robotics Auton. Syst..

[21]  John Hancock.  LASER INTENSITY-BASED OBSTACLE DETECTION AND TRACKING , 1999 .

[22]  J. Balaram.  Kinematic observers for articulated rovers , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[23]  Hugh F. Durrant-Whyte,et al.  A Kalman filter model for GPS navigation of land vehicles , 1994, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'94).

[24]  Johan Forsberg,et al.  Mobile robot localization: integrating measurements from a time-of-flight laser , 1996, IEEE Trans. Ind. Electron..

[25]  Clark F. Olson,et al.  Probabilistic self-localization for mobile robots , 2000, IEEE Trans. Robotics Autom..

[26]  Alan C. Schultz,et al.  Continuous localization using evidence grids , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[27]  Hugh F. Durrant-Whyte,et al.  Initial calibration and alignment of low-cost inertial navigation units for land vehicle applications , 1999, J. Field Robotics.

[28]  John M. Evans,et al.  HelpMate: an autonomous mobile robot courier for hospitals , 1994, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'94).

[29]  Pekka Appelqvist,et al.  Mechatronics design of a robot society : a case study of minimalist underwater robots for distributed perception and task execution , 2000 .

[30]  Wolfram Burgard,et al.  Map learning and high-speed navigation in RHINO , 1998 .

[31]  A. Halme,et al.  A biofilm reactor for a bacteria fuel cell system , 1999 .

[32]  Evangelos E. Milios,et al.  Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[33]  Wolfram Burgard,et al.  Probabilistic mapping of an environment by a mobile robot , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[34]  Pekka Forsman,et al.  Three-dimensional localization and mapping of static environments by means of mobile perception , 2001 .

[35]  Martial Hebert,et al.  Autonomous Driving with Concurrent Goals and Multiple Vehicles: Experiments and Mobility Components , 2002, Auton. Robots.

[36]  J. Guivant.  Navigation Algorithms for Autonomous Machines in Off-Road Applications , 2000 .

[37]  Aarne Halme,et al.  Robot motion by simultaneously wheel and leg propulsion , 2001 .

[38]  Kurt Konolige,et al.  Incremental mapping of large cyclic environments , 1999, Proceedings 1999 IEEE International Symposium on Computational Intelligence in Robotics and Automation. CIRA'99 (Cat. No.99EX375).

[39]  Hugh F. Durrant-Whyte,et al.  Autonomous underground navigation of an LHD using a combined ICP-EKF approach , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[40]  Peter I. Corke,et al.  Autonomous control of underground mining vehicles using reactive navigation , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[41]  Eduardo Mario Nebot,et al.  High accuracy navigation using laser range sensors in outdoor applications , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[42]  Aarne Halme,et al.  WorkPartner - centaur like service robot , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[43]  F. Lu,et al.  Shape registration using optimization for mobile robot navigation , 1996 .

[44]  Luis Moreno,et al.  Mobile Robot Multitarget Tracking In Dynamic Environments , 1992, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems.

[45]  Aarne Halme,et al.  NAVIGATION SYSTEM OF AN OUTDOOR SERVICE ROBOT WITH HYBRID LOCOMOTION SYSTEM , 2001 .

[46]  Nicola Tomatis,et al.  Improving robustness and precision in mobile robot localization by using laser range finding and monocular vision , 1999, 1999 Third European Workshop on Advanced Mobile Robots (Eurobot'99). Proceedings (Cat. No.99EX355).