Adaptive cancelation of self-generated sensory signals in a whisking robot

Sensory signals are often caused by one's own active movements. This raises a problem of discriminating between self-generated sensory signals and signals generated by the external world. Such discrimination is of general importance for robotic systems, where operational robustness is dependent on the correct interpretation of sensory signals. Here, we investigate this problem in the context of a whiskered robot. The whisker sensory signal comprises two components: one due to contact with an object (externally generated) and another due to active movement of the whisker (self-generated). We propose a solution to this discrimination problem based on adaptive noise cancelation, where the robot learns to predict the sensory consequences of its own movements using an adaptive filter. The filter inputs (copy of motor commands) are transformed by Laguerre functions instead of the often-used tapped-delay line, which reduces model order and, therefore, computational complexity. Results from a contact-detection task demonstrate that false positives are significantly reduced using the proposed scheme

On exploiting haptic cues for self-supervised learning of depth-based robot navigation affordances

This article presents a method for online learning of robot navigation affordances from spatiotemporally correlated haptic and depth cues. The method allows the robot to incrementally learn which objects present in the environment are actually traversable. This is a critical requirement for any wheeled robot performing in natural environments, in which the inability to discern vegetation from non-traversable obstacles frequently hampers terrain progression. A wheeled robot prototype was developed in order to experimentally validate the proposed method. The robot prototype obtains haptic and depth sensory feedback from a pan-tilt telescopic antenna and from a structured light sensor, respectively. With the presented method, the robot learns a mapping between objects' descriptors, given the range data provided by the sensor, and objects' stiffness, as estimated from the interaction between the antenna and the object. Learning confidence estimation is considered in order to progressively reduce the number of required physical interactions with acquainted objects. To raise the number of meaningful interactions per object under time pressure, the several segments of the object under analysis are prioritised according to a set of morphological criteria. Field trials show the ability of the robot to progressively learn which elements of the environment are traversable.info:eu-repo/semantics/acceptedVersio

Vibrissa-based design of tapered tactile sensors for object sensing

Numerous mammals possess whiskers (tactile hairs, also known as vibrissae) to explore their environment. These complex mechano-sensitive vibrissae are located, e.g. in the snout region (mystacial vibrissae). Because of the deformation of the vibrissa by contact with objects and obstacles, the animal gets additional information about the environment. Despite different morphology of animal vibrissae (e.g., cylindrically or conically shaped, precurved, multi-layer structure), these biological tactile hairs are modeled in a mechanical way to develop and analyze models concerning their bending behavior with a glance to get hints for a technical implementation as a technical sensor. At first, we investigate the bending behavior of cylindrically shaped and tapered rods which are one-sided clamped and are under the load of an external force, using the Euler-Bernoulli non-linear bending theory. Then, a quasi-static sweep of these rods along various obstacle profiles is used for an obstacle profile reconstruction procedure. While scanning the object, the clamping reactions are determined, which are the only observables an animal relies on in biology. In plotting these observables and using them in a reconstruction algorithm to determine the scanned contour, we try to identify special features in dependence on the different geometries of the rods. The clamping reactions tremendously depend on the form and position of the profile which is shown in several numerical simulations

Sensing device with whisker elements

A sensing device includes an elongated whisker element having a flexible cantilever region and a base region where a change in moment or curvature is generated by bending of the cantilever region when it contacts an object. One or more sensor elements cooperatively associated with the whisker element provide one or more output signals that is/are representative of two orthogonal components of change in moment or curvature at the whisker base region to permit determination of object distance, fluid velocity profile, or object contour (shape) with accounting for lateral slip of the whisker element and frictional characteristics of the object. Multiple sensing devices can be arranged in arrays in a manner to sense object contour without or with adjustment for lateral slip

Object contour sensing using artificial rotatable vibrissae

Recent research topics in bionics focus on the analysis and synthesis of mammal’s perception of their environment by means of their vibrissae. Using these complex tactile sense organs, rats and mice, for example, are capable of detecting the distance to an object, its contour and its surface texture. In this paper, we focus on developing and investigating a biologically inspired mechanical model for object scanning and contour reconstruction. A vibrissa – used for the transmission of a stimulus – is frequently modeled as a cylindrically shaped Euler-Bernoulli-bending rod, which is one-sided clamped and swept along an object translationally. Due to the biological paradigm, the scanning process within the present paper is adapted for a rotational movement of the vibrissa. Firstly, we consider a single quasi-static sweep of the vibrissa along a strictly convex profile using nonlinear Euler-Bernoulli theory. The investigation leads to a general boundary-value problem with some unknown parameters, which have to be determined in using shooting methods. Then, it is possible to calculate the support reactions of the system. These support reactions together with the boundary conditions to the support, which all form quantities an animal solely relies on in nature, are used for the reconstruction of the object contour. Afterwards, the scanning process is extended by rotating the vibrissa in opposite direction in order to enlarge the reconstructable area of the profile

Object shape recognition and reconstruction using pivoted tactile sensors

Many mammals use some special tactile hairs, the so-called mystacial macrovibrissae, to acquire information about their environment. In doing so, rats and mice, e.g., are able to detect object distances, shapes, and surface textures. Inspired by the biological paradigm, we present a mechanical model for object contour scanning and shape reconstruction, considering a single vibrissa as a cylindrically shaped Euler-Bernoulli-bending rod, which is pivoted by a bearing. In doing so, we adapt our model for a rotational scanning movement, which is in contrast to many previous modeling approaches. Describing a single rotational quasi-static sweep of the vibrissa along a strict convex contour function using nonlinear Euler-Bernoulli theory, we end up in a boundary-value problem with some unknown parameters. In a first step, we use shooting methods in an algorithm to repeatedly solve this boundary-value problem (changing the vibrissa base angle) and generate the support reactions during a sweep along an object contour. Afterwards, we use these support reactions to reconstruct the object contour solving an initial-value problem. Finally, we extend the scanning process adding a second sweep of the vibrissa in opposite direction in order to enlarge the reconstructable area of the profile

Obstacle scanning by technical vibrissae with compliant support

Rodents, like mice and rats, use tactile hairs in the snout region (mystacial vibrissae) to acquire information about their environment, e.g. the shape or contour of obstacles. For this, the vibrissa is used for the perception of stimuli due to an object contact. Mechanoreceptors are processing units of this stimuli measured in the compliant support (follicle sinus complex). We use this behavior from biology as an inspiration to set up a mechanical model for object contour scanning. An elastic bending rod interacts with a rigid obstacle in the plane. Analyzing only one quasi-static sweep of the rod along the obstacle (in contrast to literature), we determine a) the support reactions (the only observables of the problem), and then b) the (discrete) obstacle contour in form of a set of contact points. In doing this, we first assume a stiff support (clamping) of the vibrissa, but in a next step we increase the elasticity of the support in focussing on a bearing with a rotational spring (also to control or delimitate the bending moment at the support). Thereby, we present a fully analytical treatment of the non-linear differential equations emerging from Bernoulli’s rod theory and a representation by Standard Elliptic Integrals