Automatic Mapping of Dynamic Office Environments

We present a robot, InductoBeast, that greets a new office building by learning the floorplan automatically, with minimal human intervention and a priori knowledge. Our robot architecture is unique because it combines aspects of both abductive and inductive mapping methods to solve this problem. We present experimental results spanning three ofiice environments, mapped and navigated during normal business hours. We hope these results help to establish a performance benchmark against which robust and adaptive mapping robots of the future may be measured.

[1]  Viii Supervisor Sonar-Based Real-World Mapping and Navigation , 2001 .

[2]  Don Ray Murray,et al.  Stereo vision based mapping and navigation for mobile robots , 1997, Proceedings of International Conference on Robotics and Automation.

[3]  Sebastian Thrun,et al.  An approach to learning mobile robot navigation , 1995, Robotics Auton. Syst..

[4]  Eric Horvitz,et al.  Challenge problems for artificial intelligence , 1996, AAAI 1996.

[5]  Reid G. Simmons,et al.  Probabilistic Robot Navigation in Partially Observable Environments , 1995, IJCAI.

[6]  Illah R. Nourbakhsh,et al.  DERVISH - An Office-Navigating Robot , 1995, AI Mag..

[7]  Panayiotis Tsanakas,et al.  A sensory uncertainty field model for unknown and non-stationary mobile robot environments , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[8]  Alan C. Schultz,et al.  Mobile robot exploration and map-building with continuous localization , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[9]  Ricardo Gutierrez-Osuna,et al.  LOLA Probabilistic Navigation for Topological Maps , 1996, AI Mag..

[10]  Lindsay Kleeman,et al.  Sonar based map building for a mobile robot , 1997, Proceedings of International Conference on Robotics and Automation.

[11]  Murray Shanahan,et al.  Default Reasoning about Spatial Occupancy , 1995, Artif. Intell..

[12]  B. Kuipers,et al.  The Semantic Hierarchy in Robot Learning , 1992 .

[13]  Anibal T. de Almeida,et al.  Map building using fuzzy ART, and learning to navigate a mobile robot on an unknown world , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[14]  Günther Schmidt,et al.  Building a global map of the environment of a mobile robot: the importance of correlations , 1997, Proceedings of International Conference on Robotics and Automation.

[15]  Oussama Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Autonomous Robot Vehicles.

[16]  Erann Gat,et al.  Integrating Planning and Reacting in a Heterogeneous Asynchronous Architecture for Controlling Real-World Mobile Robots , 1992, AAAI.

[17]  M. F.,et al.  Bibliography , 1985, Experimental Gerontology.

[18]  Pascal Vasseur,et al.  Incremental map building for mobile robot navigation in an indoor environment , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[19]  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).

[20]  Artur Dubrawski,et al.  Artificial neural network for mobile robot topological localization , 1995, Robotics Auton. Syst..