From CySkin to ProxySKIN: Design, Implementation and Testing of a Multi-Modal Robotic Skin for Human-Robot Interaction.

The Industry 5.0 paradigm has a human-centered vision of the industrial scenario and foresees a close collaboration between humans and robots. Industrial manufacturing environments must be easily adaptable to different task requirements, possibly taking into account the ergonomics and production line flexibility. Therefore, external sensing infrastructures such as cameras and motion capture systems may not be sufficient or suitable as they limit the shop floor reconfigurability and increase setup costs. In this paper, we present the technological advancements leading to the realization of ProxySKIN, a skin-like sensory system based on networks of distributed proximity sensors and tactile sensors. This technology is designed to cover large areas of the robot body and to provide a comprehensive perception of the surrounding space. ProxySKIN architecture is built on top of CySkin, a flexible artificial skin conceived to provide robots with the sense of touch, and arrays of Time-of-Flight (ToF) sensors. We provide a characterization of the arrays of proximity sensors and we motivate the design choices that lead to ProxySKIN, analyzing the effects of light interference on a ToF, due to the activity of other sensing devices. The obtained results show that a large number of proximity sensors can be embedded in our distributed sensing architecture and incorporated onto the body of a robotic platform, opening new scenarios for complex applications.

An Atlas for the Inkjet Printing of Large-Area Tactile Sensors.

This review aims to discuss the inkjet printing technique as a fabrication method for the development of large-area tactile sensors. The paper focuses on the manufacturing techniques and various system-level sensor design aspects related to the inkjet manufacturing processes. The goal is to assess how printed electronics simplify the fabrication process of tactile sensors with respect to conventional fabrication methods and how these contribute to overcoming the difficulties arising in the development of tactile sensors for real robot applications. To this aim, a comparative analysis among different inkjet printing technologies and processes is performed, including a quantitative analysis of the design parameters, such as the costs, processing times, sensor layout, and general system-level constraints. The goal of the survey is to provide a complete map of the state of the art of inkjet printing, focusing on the most effective topics for the implementation of large-area tactile sensors and a view of the most relevant open problems that should be addressed to improve the effectiveness of these processes.

Contact Modelling and Tactile Data Processing for Robot Skins.

Tactile sensing is a key enabling technology to develop complex behaviours for robots interacting with humans or the environment. This paper discusses computational aspects playing a significant role when extracting information about contact events. Considering a large-scale, capacitance-based robot skin technology we developed in the past few years, we analyse the classical Boussinesq⁻Cerruti's solution and the Love's approach for solving a distributed inverse contact problem, both from a qualitative and a computational perspective. Our contribution is the characterisation of the algorithms' performance using a freely available dataset and data originating from surfaces provided with robot skin.

Architecture and design of a robotic mastication simulator for interactive load testing of dental implants and the mandible.

STATEMENT OF PROBLEM: Determination of interactive loading between a dental prosthesis and the host mandible is essential for implant prosthodontics and to preserve bone. PURPOSE: The purpose of this study was to develop and evaluate a robotic mastication simulator to replicate the human mastication force cycle to record the required interactive loading using specifically designed force sensors. MATERIAL AND METHODS: This robotic mastication simulator incorporated a Stewart parallel kinematic mechanism (PKM) controlled in the force-control loop. The hydraulically operated PKM executed the wrench operation, which consisted of the combined effect of forces and moments exhibited by the mastication process. Principal design features of this robotic simulator included PKM kinematic modeling, static force analysis to realize the masticatory wrench characteristics, and the architecture of its hydraulic system. Additionally, the design of a load-sensing element for the mandible and implant interaction was also incorporated. This element facilitated the quantification of the load distribution between implants and the host bone during the masticatory operation produced by the PKM. These loading tests were patient-specific and required separate artificial mandibular models for each patient. RESULTS: The simulation results demonstrated that the robotic PKM could replicate human mastication. These results validated the hydraulic system modeling for the required range of masticatory movements and effective forces of the PKM end-effector. The overall structural design of the robotic mastication simulator presented the integration of the PKM and its hydraulic system with the premeditated load-recording mechanism. CONCLUSIONS: The developed system facilitated the teeth-replacement procedure. The PKM accomplished the execution of mastication cycle involving 6 degrees of freedom, enabling any translation and rotation in sagittal, horizontal, and vertical planes. The mechanism can simulate the human mastication cycle and has a force application range of up to 2000 N. The designed load-sensing element can record interactive forces within the range of 200 N to 2000 N with fast response and high sensitivity to produce a robotic mastication simulator with custom-made modules.

Fabric Classification Using a Finger-Shaped Tactile Sensor via Robotic Sliding.

Tactile sensing endows the robots to perceive certain physical properties of the object in contact. Robots with tactile perception can classify textures by touching. Interestingly, textures of fine micro-geometry beyond the nominal resolution of the tactile sensors can also be identified through exploratory robotic movements like sliding. To study the problem of fine texture classification, we design a robotic sliding experiment using a finger-shaped multi-channel capacitive tactile sensor. A feature extraction process is presented to encode the acquired tactile signals (in the form of time series) into a low dimensional (≤7D) feature vector. The feature vector captures the frequency signature of a fabric texture such that fabrics can be classified directly. The experiment includes multiple combinations of sliding parameters, i.e., speed and pressure, to investigate the correlation between sliding parameters and the generated feature space. Results show that changing the contact pressure can greatly affect the significance of the extracted feature vectors. Instead, variation of sliding speed shows no apparent effects. In summary, this paper presents a study of texture classification on fabrics by training a simple k-NN classifier, using only one modality and one type of exploratory motion (sliding). The classification accuracy can reach up to 96%. The analysis of the feature space also implies a potential parametric representation of textures for tactile perception, which could be used for the adaption of motion to reach better classification performance.