Whisker-Based Texture Discrimination on a Mobile Robot

Fend, Miriam

doi:10.1007/11553090_31

Cited by 31 publications

(41 citation statements)

References 13 publications

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“…Proof that navigation from such sensors is possible is readily found in biology: rats navigate through dark underground tunnels using their whiskers [7,2], having ranges of only a few centimetres. In robotics, whisker sensors are relatively cheap in both material and computational processing terms, and their use has previously been considered in constrained tasks [44,43,30,29,15]. The previous robotic attempts at mapping from sparse local sensors have either used the extremely strong generic prior that the whole world is made entirely of north-south and east-west straight edges [53] or have used relatively long range but sparse ray sensors integrated over multiscans [3].…”

Section: Introductionmentioning

confidence: 99%

“…Tip contacts are generally the most useful, because they provide a standardised, constrained setting (i.e. with the contact point at a known location at precisely the end of the whisker) from which surface properties such as orientation and texture can be identified [30], [15]. In contrast, shaft contacts are less informative.…”

Section: Introductionmentioning

confidence: 99%

“…Individual components of such a system have previously been investigated in isolation, including whiskered texture recognition [15], [26], [11], [17], [32], surface shape recognition [30], [24], [21], [13], and object recognition [19]. These components have previously been tested under ideal laboratory conditions or in individual mobile settings [41]; here we present steps integrating them into a single platform for hierarchical object recognition, along with results and observations on their performance 'in the wild' in a common arena environment.…”

Section: Introductionmentioning

confidence: 99%

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Towards hierarchical blackboard mapping on a whiskered robot

Fox

Evans

Pearson

et al. 2012

Robotics and Autonomous Systems

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Towards hierarchical blackboard mapping on a whiskered robot

Fox

Evans

Pearson

et al. 2012

Robotics and Autonomous Systems

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“…In robotics, touch sensors are relatively cheap in both material and computational processing terms, and their use has previously been considered to enhance navigation in cheap household robots [5], [6]. (Related work on research robot platforms includes [7], [8], [9], [10], [11]). …”

Section: Introductionmentioning

confidence: 99%

Mapping with Sparse Local Sensors and Strong Hierarchical Priors

Fox

Prescott

2011

Towards Autonomous Robotic Systems

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Abstract-The paradigm case for robotic mapping, as in Simultaneous Localisation and Mapping problems, considers a mobile robot with noisy odometry and laser scanners. Laser scanners provide large amounts of sensory information, and have effectively unlimited range in indoor environments. Such large quantities of input information allow the use of relatively weak priors. In contrast, the present study considers the mapping problem in environments where only sparse, local sensory information is available. To compensate for the lack of likelihood evidence, we make use of strong hierarchical object priors. Hierarchical models were popular in classical blackboard systems but hare here applied in a Bayesian setting and novelly deployed as a mapping algorithm. We give proof of concept results, intended to demonstrate the algorithm's applicability as a part of a tactile SLAM module for the whiskered ScratchBot mobile robot platform. I. INTRODUCTIONThe paradigm case for robotic mapping, as in Simultaneous Localisation and Mapping (SLAM) problems [1], considers a mobile robot with noisy odometry and SICK laser scanners. Laser scanners provide large amounts of sensory information, and have effectively unlimited range in indoor environments. Such large quantities of input information allow the use of relatively weak priors, such as independent grid cell occupancy or flat priors over the belief of small feature sets [1].In contrast, the present study considers the mapping problem in environments where only sparse, local sensory information is available. For example, a fire-fighting robot building up a map in a smoke-filled house cannot rely on laser scanners functioning at all times, and could instead operate by feeling its way around with touch sensors. Proof that this type of navigation is possible is found in biology: electric fish make use of highly localised electric field sensors [2] and rats navigate through dark underground tunnels using their whiskers [3], [4], both having ranges of a few centimetres. In robotics, touch sensors are relatively cheap in both material and computational processing terms, and their use has previously been considered to enhance navigation in cheap household robots [5]

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“…Solutions to the actuation problem have included miniaturised conventional electric motors, and actuators with more muscle-like properties such as shape-memory alloys [28] or 'air-muscles' [37]. In functional terms, previous work on robotic whisker sensing has provided proof of principle that artificial vibrissal systems can compute estimates of distance and shape [37], [36], [32], [20] and can distinguish between textures with different spatial frequencies [12], [19]. In the following we briefly summarise past research specifically related to the texture discrimination problem.…”

Section: Introductionmentioning

confidence: 99%

Contact type dependency of texture classification in a whiskered mobile robot

et al. 2009

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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. (146 words)

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Whisker-Based Texture Discrimination on a Mobile Robot

Cited by 31 publications

References 13 publications

Towards hierarchical blackboard mapping on a whiskered robot

Towards hierarchical blackboard mapping on a whiskered robot

Mapping with Sparse Local Sensors and Strong Hierarchical Priors

Contact type dependency of texture classification in a whiskered mobile robot

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