Editorial for the Special Issue on Tactile Sensing for Soft Robotics and Wearables

Tactile feedback is needed for the interaction of humans with a worn device, and to enable robots to gather environmental cues and react to their surroundings [...].

long-term perspectives. From an economical point of view, currently, the technology is expensive and highly customized, limiting the high-volume manufacturing needed to reach an entry-market level. Hence, the authors concluded that particular effort in innovating mass production techniques and in discovering novel manufacturing procedures are needed.
In [2], Wu et al. proposed a flexible annular sectorial sensor that could be wrapped on the curved surface of a robotic arm that had a five layer structure and was designed for contact detection during robot movements. The transduction principle was based on a constant electric field on the upper and lower conductive layers. A mathematical model was developed to establish a relationship between the coordinates of the contact position and the corresponding electric field. The finite element method (FEM), using COMSOL®software, was used to simulate the sensor behavior. Results showed very good agreement between the experiments and theoretical and numerical predictions, indicating that the sensor performed well when wrapped around a robot arm.
From the electronic hardware point of view, Yamakawa et al. [3] presented a new network system with a high sampling rate based on the synchronization of a PC clock and data acquisition system. The high frequency (over 500 Hz) enabled the acquisition of data obtained from a large number of sensors. The scheme was made of multiple sensor nodes including PCs that were connected via the Ethernet for data communication and clock synchronization. The PC's local clocks controlled the acquisition in each sensor node, while all clocks were globally synchronized over the network simultaneously to the data acquisition. Three different systems were implemented using this method: a high-speed tactile sensor node with a high-speed vision node, a high-speed tactile sensor node with three acceleration sensor nodes, and a high-speed tactile sensor node with two acceleration sensor nodes and a gyro sensor node. In all cases, the experiments showed that the timing error was less than 15 µs, well below the acquisition time of 2 ms. The results also confirmed that the proposed method could be applied in several areas such as robot control with real-time sensory feedback and intelligent transport systems as well as security and surveillance.
Machine learning is becoming a useful and powerful tool for tactile sensing in soft systems. Song et al. [4] faced the challenge of decoupling single components of three-axial force sensors by means of a machine learning method based on the improved back-propagation (BP) algorithm. This was applied in a 6 x 6 tactile sensor array to obtain the three-dimensional forces from the resistances of force-sensitive conductive pillars. The decoupling results demonstrated that the k-cross validation (k-CV) algorithm was an effective method to improve the decoupling precision of force components for the novel tactile sensor. The results were quite good, and in future studies, the authors plan to work on the recognition of the contact patterns due to three-dimensional forces, and on the development of different scales and densities for optimized performances.
Regarding the applications of haptic technologies in the biomedical field, this Special Issue includes a research paper on experiments with visual impaired (VI) people using a pin array matrix (PAM) for an orientation and mobility task. Brayda et al. [5] tested the effectiveness of a PAM representing the scaled map of a real room with the position of some target objects. After the participants haptically explored the PAM, they entered the real room to try to reach the targets three times. The first group of patients after each trial touched the same PAM again, while the second group could use an updated PAM that also included the positions that they previously reached in the room. This second group experienced significant improvements across trials, thus reducing both errors and completion time. As a result, the authors found that updated tactile feedback on programmable displays could be a powerful tool, giving much better performances with respect to conventional static tactile maps, therefore promoting more independent living for VI people.
We would like to take this opportunity to thank all of the authors for their efforts in presenting their research work in this Special Issue. We also thank all the reviewers for dedicating their time to assist in improving the quality of the submitted papers. Finally, we highly appreciate the remarkable support from the editorial staff of Micromachines.

Conflicts of Interest:
The authors declare no conflict of interest.