Tactile SLAM with a biomimetic whiskered robot

Future robots may need to navigate where visual sensors fail. Touch sensors provide an alternative modality, largely unexplored in the context of robotic map building. We present the first results in grid based simultaneous localisation and mapping (SLAM) with biomimetic whisker sensors, and show how multi-whisker features coupled with priors about straight edges in the world can boost its performance. Our results are from a simple, small environment but are intended as a first baseline to measure future algorithms against

Simultaneous localisation and mapping on a multi-degree of freedom biomimetic whiskered robot

A biomimetic mobile robot called “Shrewbot” has been built as part of a neuroethological study of the mammalian facial whisker sensory system. This platform has been used to further evaluate the problem space of whisker based tactile Simultaneous Localisation And Mapping (tSLAM). Shrewbot uses a biomorphic 3-dimensional array of active whiskers and a model of action selection based on tactile sensory attention to explore a circular walled arena sparsely populated with simple geometric shapes. Datasets taken during this exploration have been used to parameterise an approach to localisation and mapping based on probabilistic occupancy grids. We present the results of this work and conclude that simultaneous localisation and mapping is possible given only noisy odometry and tactile information from a 3-dimensional array of active biomimetic whiskers and no prior information of features in the environment

Whisker-object contact speed affects radial distance estimation

Whiskered mammals such as rats are experts in tactile perception. By actively palpating surfaces with their whiskers, rats and mice are capable of acute texture discrimination and shape perception. We present a novel system for investigating whisker-object contacts repeatably and reliably. Using an XY positioning robot and a biomimetic artificial whisker we can generate signals for different whisker-object contacts under a wide range of conditions. Our system is also capable of dynamically altering the velocity and direction of the contact based on sensory signals. This provides a means for investigating sensory motor interaction in the tactile domain. Here we implement active contact control, and investigate the effect that speed has on radial distance estimation when using different features for classification. In the case of a moving object contacting a whisker, magnitude of deflection can be ambiguous in distinguishing a nearby object moving slowly from a more distant object moving rapidly. This ambiguity can be resolved by finding robust features for contact speed, which then informs classification of radial distance. Our results are verified on a dataset from SCRATCHbot, a whiskered mobile robot. Building whiskered robots and modelling these tactile perception capabilities would allow exploration and navigation in environments where other sensory modalities are impaired, for example in dark, dusty or loud environments such as disaster areas. © 2010 IEEE

The robot vibrissal system: Understanding mammalian sensorimotor co-ordination through biomimetics

Chapter 10 The Robot Vibrissal System: Understanding Mammalian Sensorimotor Co-ordination Through Biomimetics Tony J. Prescott, Ben Mitchinson, Nathan F. Lepora, Stuart P. Wilson, Sean R. Anderson, John Porrill, Paul Dean, Charles ..

Towards hierarchical blackboard mapping on a whiskered robot

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

Biomimetic tactile target acquisition, tracking and capture

Good performance in unstructured/uncertain environments is an ongoing problem in robotics; in biology, it is an everyday observation. Here, we model a particular biological system - hunting in the Etruscan shrew - as a case study in biomimetic robot design. These shrews strike rapidly and accurately after gathering very limited sensory information from their whiskers; we attempt to mimic this performance by using model-based simultaneous discrimination and localisation of a 'prey' robot (i.e. by using strong priors to make sense of limited sensory data), building on our existing low-level models of attention and appetitive behaviour in small mammals. We report performance that is comparable, given the spatial and temporal scale differences, to shrew performance, and discuss what this study reveals about biomimetic robot design in general. © 2013 Elsevier B.V. All rights reserved

Whiskered texture classification with uncertain contact pose geometry

Tactile sensing can be an important source of information for robots, and texture discrimination in particular is useful in object recognition and terrain identification. Whisker based tactile sensing has recently been shown to be a promising approach for mobile robots, using simple sensors and many classification approaches. However these approaches have often been tested in limited environments, and have not been compared against one another in a controlled way. A wide range of whisker-object contact poses are possible on a mobile robot, and the effect such contact variability has on sensing has not been properly investigated. We present a novel, carefully controlled study of simple surface texture classifiers on a large set of varied pose conditions that mimic those encountered by mobile robots. Namely, single brief whisker contacts with textured surfaces at a range of surface orientations and contact speeds. Results show that different classifiers are appropriate for different settings, with spectral template and feature based approaches performing best in surface texture, and contact speed estimation, respectively. The results may be used to inform selection of classifiers in tasks such as tactile SLAM