Object-shape recognition and 3D reconstruction from tactile sensor images

Multi-Layered Carbon-Black/Elastomer-Composite-Based Shielded Stretchable Capacitive Sensors for the Underactuated Robotic Hand

Soft and flexible strain sensors are becoming popular for many robotic applications. This article presents a stretchable capacitive sensor by combining a conductive filler of carbon black with elastomers and implementing shielding to reduce parasitic interference, applied to an underactuated robotic hand. Sensors with different configurations were explored. The results show that a shield introduced to the sensor does have some mitigation effect on external interference. Two sensor configurations were explored: longitudinal interdigitated capacitive (LIDC) sensor, where the interdigitated fingers lie along the same axis as the strain, and transverse interdigitated capacitive (TIDC) sensor, where the interdigitated fingers are orthogonal to the strain direction. The LIDC configuration had better performance than TIDC. The fabricated two-layered LIDC sensor had a gage factor of 0.15 pF/mm and the rates of capacitive creep of 0.000667 pF/s and 0.001 pF/s at loads of 120 g and 180 g, respectively. The LIDC sensors attached to an underactuated robotic hand demonstrate the sensors’ ability to determine the bending angles of the proximal interphalangeal (PIP) and metacarpophalangeal (MCP) joints.

3D Reconstruction for Road Scene with Obstacle Detection Feedback

This paper presented 3D reconstruction method for road scene with the help of obstacle detection. 3D reconstruction for road scene can be used in autonomous driving, driver assistance system, car navigation systems. However, some errors often rose when 3D reconstructing due to the shade from the moving object in the road scene. The presented 3D reconstruction method with obstacle detection feedback can avoid this problem. Firstly, this paper offers a framework for the 3D reconstruction of road scene by laser scanning and vision. A calibration method based on the location of horizon is proposed, and a method of attitude angle measuring based on vanishing point is proposed to revise the 3D reconstruction result. Secondly, the reconstruction framework is extended by integrating with an object recognition that can automatically detect and discriminate obstacles in the input video streams by a RANSAC approach and threshold filter, and localizes them in the 3D model. 3D reconstruction and obstacle detection are tightly integrated and benefit from each other. The experiment result verified the feasibility and practicability of the proposed method.

Texture- and deformability-based surface recognition by tactile image analysis

Deformability and texture are two unique object characteristics which are essential for appropriate surface recognition by tactile exploration. Tactile sensation is required to be incorporated in artificial arms for rehabilitative and other human-computer interface applications to achieve efficient and human-like manoeuvring. To accomplish the same, surface recognition by tactile data analysis is one of the prerequisites. The aim of this work is to develop effective technique for identification of various surfaces based on deformability and texture by analysing tactile images which are obtained during dynamic exploration of the item by artificial arms whose gripper is fitted with tactile sensors. Tactile data have been acquired, while human beings as well as a robot hand fitted with tactile sensors explored the objects. The tactile images are pre-processed, and relevant features are extracted from the tactile images. These features are provided as input to the variants of support vector machine (SVM), linear discriminant analysis and k-nearest neighbour (kNN) for classification. Based on deformability, six household surfaces are recognized from their corresponding tactile images. Moreover, based on texture five surfaces of daily use are classified. The method adopted in the former two cases has also been applied for deformability- and texture-based recognition of four biomembranes, i.e. membranes prepared from biomaterials which can be used for various applications such as drug delivery and implants. Linear SVM performed best for recognizing surface deformability with an accuracy of 83 % in 82.60 ms, whereas kNN classifier recognizes surfaces of daily use having different textures with an accuracy of 89 % in 54.25 ms and SVM with radial basis function kernel recognizes biomembranes with an accuracy of 78 % in 53.35 ms. The classifiers are observed to generalize well on the unseen test datasets with very high performance to achieve efficient material recognition based on its deformability and texture.