3D localization and pose tracking system for an indoor Autonomous Mobile Robot

This paper presents a new architecture that achieves 3D localization and pose tracking for an indoor Autonomous Mobile Robot (AMR). Specifically, for speeds of up to about 0.1m/s, the architecture could localize the position with an accuracy of 1.5 cm, and determine the orientation angle to within about 4 degrees error. The localization system consists of a microcontroller embedded in the AMR and five Wireless Sensor Nodes (WSN). Two ultrasonic transmitter nodes are attached to the AMR and three ultrasonic receiver nodes are fixed on a suspended bracket. Six ultrasonic time-of-flight (TOF) measurements are used to update the AMR's pose by utilizing an Extended Kalman Filter (EKF) algorithm. In order to verify the concept, two experiment prototypes were built. In the first experiment, aiming at the precision of localization, orientation angle and slope angle, the AMR moves slowly along a slope. In the second experiment, focusing on the transient performance of the system when the orientation angle varies from one quadrant to another, the AMR moves along an arc. The results proved that the new architecture provides a high performance of localization and pose tracking for an AMR in an indoor environment.

[1]  Ching-Chih Tsai,et al.  Ultrasonic Localization and Pose Tracking of an Autonomous Mobile Robot via Fuzzy Adaptive Extended Information Filtering , 2008, IEEE Transactions on Instrumentation and Measurement.

[2]  Michel Dhome,et al.  Monocular Vision for Mobile Robot Localization and Autonomous Navigation , 2007, International Journal of Computer Vision.

[3]  David Filliat,et al.  Map-based navigation in mobile robots: II. A review of map-learning and path-planning strategies , 2003, Cognitive Systems Research.

[4]  Joachim Hertzberg,et al.  High-speed laser localization for mobile robots , 2005, Robotics Auton. Syst..

[5]  Chia-Ju Wu,et al.  Localization of an Autonomous Mobile Robot Based on Ultrasonic Sensory Information , 2001, J. Intell. Robotic Syst..

[6]  Luis Moreno,et al.  Evolutionary filter for robust mobile robot global localization , 2006, Robotics Auton. Syst..

[7]  Joachim Hertzberg,et al.  An autonomous mobile robot with a 3D laser range finder for 3D exploration and digitalization of indoor environments , 2003, Robotics Auton. Syst..

[8]  Luc Van Gool,et al.  Omnidirectional Vision Based Topological Navigation , 2007, International Journal of Computer Vision.

[9]  Ching-Chih Tsai A localization system of a mobile robot by fusing dead-reckoning and ultrasonic measurements , 1998, IEEE Trans. Instrum. Meas..

[10]  Jean-Arcady Meyer,et al.  Map-based navigation in mobile robots: I. A review of localization strategies , 2003, Cognitive Systems Research.

[11]  Luis Moreno,et al.  Navigation of mobile robots: open questions , 2000, Robotica.

[12]  Hari Balakrishnan,et al.  6th ACM/IEEE International Conference on on Mobile Computing and Networking (ACM MOBICOM ’00) The Cricket Location-Support System , 2022 .

[13]  Tzuu-Hseng S. Li,et al.  Fuzzy target tracking control of autonomous mobile robots by using infrared sensors , 2004, IEEE Transactions on Fuzzy Systems.

[14]  Hari Balakrishnan,et al.  Tracking moving devices with the cricket location system , 2004, MobiSys '04.

[15]  Seth J. Teller,et al.  The cricket compass for context-aware mobile applications , 2001, MobiCom '01.

[16]  Bodhi Priyantha,et al.  The Cricket indoor location system , 2005 .

[17]  Bakir Lacevic,et al.  A 3-level autonomous mobile robot navigation system designed by using reasoning/search approaches , 2006, Robotics Auton. Syst..

[18]  Kevin L. Moore,et al.  A six-wheeled omnidirectional autonomous mobile robot , 2000 .

[19]  Ching-Chih Tsai,et al.  Multisensor 3D Posture Determination of a Mobile Robot Using Inertial and Ultrasonic Sensors , 2005, J. Intell. Robotic Syst..

[20]  Roland Siegwart,et al.  Introduction to Autonomous Mobile Robots , 2004 .