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Towards hierarchical blackboard mapping on a whiskered robot

Fox, C. W.; Evans, M. H.; Pearson, M. J.; Prescott, T. J.; Fox, Charles W; Evans, Matthew; Pearson, Martin; Prescott, Tony J.


C. W. Fox

M. H. Evans

M. J. Pearson

T. J. Prescott

Charles W Fox

Matthew Evans

Tony J. Prescott


The paradigm case for robotic mapping assumes large quantities of sensory information which allow the use of relatively weak priors. In contrast, the present study considers the mapping problem for a mobile robot, CrunchBot, where only sparse, local tactile information from whisker sensors is available. To compensate for such weak likelihood information, we make use of low-level signal processing and strong hierarchical object priors. Hierarchical models were popular in classical blackboard systems but are here applied in a Bayesian setting as a mapping algorithm. The hierarchical models require reports of whisker distance to contact and of surface orientation at contact, and we demonstrate that this information can be retrieved by classifiers from strain data collected by CrunchBot's physical whiskers. We then provide a demonstration in simulation of how this information can be used to build maps (but not yet full SLAM) in an zero-odometry-noise environment containing walls and table-like hierarchical objects. © 2012 Elsevier B.V. All rights reserved.


Prescott, T. J., Pearson, M. J., Evans, M. H., Fox, C. W., Fox, C. W., Evans, M., …Prescott, T. J. (2012). Towards hierarchical blackboard mapping on a whiskered robot. Robotics and Autonomous Systems, 60(11), 1356-1366.

Journal Article Type Article
Publication Date Nov 1, 2012
Journal Robotics and Autonomous Systems
Print ISSN 0921-8890
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 60
Issue 11
Pages 1356-1366
Keywords bayesian, blackboard system, tactile, whiskers, mapping,
object recognition, hierarchical, shape recognition
Public URL
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