Accession Number : ADA307732

Title :   Learning Maps for Indoor Mobile Robot Navigation.

Descriptive Note : Research rept.,

Corporate Author : CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF COMPUTER SCIENCE

Personal Author(s) : Thrun, Sebastian ; Buecken, Arno

PDF Url : ADA307732

Report Date : APR 1996

Pagination or Media Count : 37

Abstract : Autonomous robots must be able to learn and maintain models of their environments. Research on mobile robot navigation has produced two major paradigms for mapping indoor environments: grid-based and topological. While grid-based methods produce accurate metric maps, their complexity often prohibits efficient planning and problem solving in large-scale indoor environments. Topological maps, on the other hand, can be used much more efficiently, yet accurate and consistent topological maps are considerably difficult to learn in large-scale environments. This paper describes an approach that integrates both paradigms: grid-based and topological. Grid-based maps are learned using artificial neural networks and Bayesian integration. Topological maps are generated on top of the grid- based maps, by partitioning the latter into coherent regions. By combining both paradigms-grid-based and topological-, the approach presented here gains the best of both worlds: accuracy/consistency and efficiency. The paper gives results for autonomously operating a mobile robot equipped with sonar sensors in populated multi-room environments.

Descriptors :   *ROBOTS, *AUTONOMOUS NAVIGATION, MATHEMATICAL MODELS, ALGORITHMS, SOFTWARE ENGINEERING, IMAGE PROCESSING, POSITION(LOCATION), INTEGRATED SYSTEMS, NEURAL NETS, AUTOMATION, REAL TIME, GRIDS, RESOLUTION, ORIENTATION(DIRECTION), ACCURACY, EFFICIENCY, LEARNING MACHINES, RULE BASED SYSTEMS, POSITION FINDING, MOBILE, SELF OPERATION, INFRARED DETECTORS, PATTERN RECOGNITION, MAPPING, BAYES THEOREM, DYNAMIC PROGRAMMING, COLLISION AVOIDANCE, TOPOGRAPHIC MAPS, SONAR.

Subject Categories : Cybernetics
      Navigation and Guidance

Distribution Statement : APPROVED FOR PUBLIC RELEASE