CrunchBot is a robot platform for developing models of tactile perception and navigation. We present the architecture of CrunchBot,
and show why tactile navigation is difficult. We give novel real-time performance results from components of a tactile navigation
system and a description of how they may be integrated at a systems level. Components include floor surface classification,
radial distance estimation and navigation. We show how tactile-only navigation differs fundamentally from navigation tasks
using vision or laser sensors, in that the assumptions about the data preclude standard algorithms (such as extended Kalman
Filters) and require brute-force methods.
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.
"Contact speed is uncertain in many cases, including uncertain robot body velocity from odometry noise; uncertain angular whisker velocity during active whisking (the oscillatory whisker movements generated by rats to explore environments, and control the duration and force of contacts ); and the possibility of contact with moving objects in the environment. While other studies , have considered contact distance along the whisker shaft as a further variable, we have found in practice  that almost all real-world contacts for mobile robots occur at the tip of the whisker, and the few that do not can be used to reposition the robot to obtain a tip contact . To address this problem of discriminating textures with brief contacts at an uncertain whisker pose we have developed an artificial whisker and XY positioning robot system to generate large datasets and comprehensively explore the whisker-object contact parameter space. "
[Show abstract][Hide abstract]ABSTRACT: 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.
"On first consideration, it may appear that touch-based SLAM is doomed to failure due to the sparsity of the likelihood functions. A previous study  gave examples of whisker-based likelihood functions, and showed them to have high spatial variances as well as many discontinuities and non-Gaussian shapes. The high spatial variances occur "
[Show abstract][Hide abstract]ABSTRACT: 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.
"SCRATCHbot is used here in a " head-fixed " protocol to show how the classifier and features developed on the XY positioning robot can be applied to data from a less restricted whisking robot. Thirdly, CrunchBot is a mobile whiskered robot with stationary whiskers (Fox et al., 2011). This robot has fewer degrees of freedom than SCRATCHbot, allowing more straightforward robot control and data collection in a mobile setting. "
[Show abstract][Hide abstract]ABSTRACT: Whisker movement has been shown to be under active control in certain specialist animals such as rats and mice. Though this whisker movement is well characterized, the role and effect of this movement on subsequent sensing is poorly understood. One method for investigating this phenomena is to generate artificial whisker deflections with robotic hardware under different movement conditions. A limitation of this approach is that assumptions must be made in the design of any artificial whisker actuators, which will impose certain restrictions on the whisker-object interaction. In this paper we present three robotic whisker platforms, each with different mechanical whisker properties and actuation mechanisms. A feature-based classifier is used to simultaneously discriminate radial distance to contact and contact speed for the first time. We show that whisker-object contact speed predictably affects deflection magnitudes, invariant of whisker material or whisker movement trajectory. We propose that rodent whisker control allows the animal to improve sensing accuracy by regulating contact speed induced touch-to-touch variability.
Full-text · Article · Jan 2012 · Frontiers in Neurorobotics