This thesis proposes a parsimonious approach to localization, mapping and object recognition for a pseudo-mobile robot equipped with a biomimetic array of tactile whiskers to autonomously interact, explore and represent a real-world environment. Tactile whisker sensors enable the robotic platform to perceive unique environmental properties and can operate in extreme conditions that preclude the use of conventional sensors, however, such sensors are disadvantaged by their limited range and sample sparsity. To address the sparsity, the information contained in each contact should be fully exploited, whilst the limited range of the array can be addressed through appropriate movement and placement of the whiskers and the array. An existing Simultaneous Localization and Mapping (SLAM) algorithm called RatSLAM was adopted as the basis for the inference of location and demonstrated as suitable for correcting odometry errors using whisker tactile sensing. The adoption of a closed loop contact induced whisker placement strategy, directly inspired by rat whisking behavior, improved the performance of the algorithm in further reducing odometry error. The ﬁdelity of object shape reconstruction through the forward kinematic projection of whisker contact locations was analyzed and a number of machine learning approaches compared to assess their ecacy at discerning radial distance to contact and thus improve object shape reconstruction. A support vector regression technique was found to reliably improve estimates of radial distance to contact along the whisker shaft following natural, unconstrained whisker contacts. A framework for combining the 3D pose estimation from RatSLAM with a 6D pose estimation system suitable for object recognition is proposed with the 6D system implemented and demonstrated correctly identifying household objects through tactile whisker exploration. The adoption of whisker array placement strategies inspired by cutaneous-tactile research improved the robustness of object identiﬁcation and two regional search strategies were investigated for the purpose of reducing the time taken to correctly classify objects.
|Date of Award||28 Nov 2019|
- The University of Bristol
|Supervisor||Martin Pearson (Supervisor)|