Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Active Sensing Capabilities of the Rat Whisker System
Autonomous Robots
Biomimetic whiskers for shape recognition
Robotics and Autonomous Systems
Whiskerbot: A Robotic Active Touch System Modeled on the Rat Whisker Sensory System
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Spike-timing in primary sensory neurons: a model of somatosensory transduction in the rat
Biological Cybernetics
Whisker-based texture discrimination on a mobile robot
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
A model of sensorimotor coordination in the rat whisker system
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
SCRATCHbot: active tactile sensing in a whiskered mobile robot
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
Adaptive cancelation of self-generated sensory signals in a whisking robot
IEEE Transactions on Robotics
A general classifier of whisker data using stationary naive bayes: application to BIOTACT robots
TAROS'11 Proceedings of the 12th Annual conference on Towards autonomous robotic systems
CrunchBot: a mobile whiskered robot platform
TAROS'11 Proceedings of the 12th Annual conference on Towards autonomous robotic systems
Towards hierarchical blackboard mapping on a whiskered robot
Robotics and Autonomous Systems
Biomimetic tactile target acquisition, tracking and capture
Robotics and Autonomous Systems
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Actuated artificial whiskers modeled on rat macrovibrissae can provide effective tactile sensor systems for autonomous robots. This article focuses on texture classification using artificial whiskers and addresses a limitation of previous studies, namely, their use of whisker deflection signals obtained under relatively constrained experimental conditions. Here we consider the classification of signals obtained from a whiskered robot required to explore different surface textures from a range of orientations and distances. This procedure resulted in a variety of deflection signals for any given texture. Using a standard Gaussian classifier we show, using both hand-picked features and ones derived from studies of rat vibrissal processing, that a robust rough-smooth discrimination is achievable without any knowledge of how the whisker interacts with the investigated object. On the other hand, finer discriminations appear to require knowledge of the target's relative position and/or of the manner in which the whisker contact its surface.