Lucia Seminara

Piezoelectric Polymer Transducer Arrays for Flexible Tactile Sensors

The paper focuses on the manufacturing technology of modular components for large-area tactile sensors, which are made of arrays of polyvinylidene fluoride (PVDF) piezoelectric polymer taxels integrated on flexible PCBs. PVDF transducers were chosen for the high electromechanical transduction frequency bandwidth (up to 1 kHz for the given application). Patterned electrodes were inkjet printed on the PVDF film. Experimental tests on skin module prototypes demonstrate the feasibility of the proposed approach and reveal the potentiality to build large area flexible and conformable robotic skin.

A Tensor-Based Pattern-Recognition Framework for the Interpretation of Touch Modality in Artificial Skin Systems

Artificial skin systems support human-robot interactions through touch. The interpretation of touch modalities indeed represents a crucial component for the future development of robots that can properly interact with humans. Independently of the specific employed transducer, one of the key issues is how to process the massively complex and high-dimensional tactile data. In this paper, machine learning technologies (namely, support vector machines and extreme learning machines) support a pattern-recognition framework that can fully exploit the tensor morphology of the tactile signal. Furthermore, a practical strategy is provided to address the intricacies of the training procedure. Experimental results show the effectiveness of the proposed approach.

Real-time reconstruction of contact shapes for large area robot skin

Tactile sensing is considered a key technology for implementing complex robot interaction tasks. The contribution of this article is two-fold: (i) we propose a general-purpose algorithm for the reconstruction of deformation and force distributions for capacitance-based skin-like systems; (ii) real-time performance can be tuned according to available computational resources, which leads to an any-time formulation. Experiments (both in simulation and with real robot skin) provide a quantitative analysis of results.

The ROBOSKIN Project: Challenges and Results

The goal of the ROBOSKIN project is to develop and demonstrate a range of new robot capabilities based on the tactile feedback provided by a robotic skin covering large areas of the robot body. So far, a principled investigation of these issues has been limited by the lack of tactile sensing technology enabling large scale experimental activities. As a matter of fact, skin based technology and embedded tactile sensors have been mostly demonstrated only at the stage of prototypes. The new capabilities are expected to improve the ability of robots to effectively and safely operate in unconstrained environments, as well as to communicate and cooperate with each other and with humans.

A tensor-based approach to touch modality classification by using machine learning

The use of piezoelectric sensor arrays to measure contact forces has been extensively studied in connection to robotics. In this research, Polyvinylidene Fluoride (PVDF) has been used for direct measurement of the mechanical stress and large bandwidth electromechanical transduction. Additionally, a machine learning algorithm has been specifically designed to deal with the inherent tensor morphology of raw tactile data. An experiment involving 70 participants has been organized to collect the output signals under different modalities of touch. The proposed pattern-recognition system showed good accuracy in performing touch classification in a three-class classification experiment, opening interesting scenarios for the application of tensor-based models to support human-robot interactions. PVDF piezoelectric films have been used to build tactile sensor arrays.ML based pattern-recognition system is designed to treat raw data in tensor form.Different touch modalities have been collected involving 70 participants.The adopted classification tools showed good performance on a three-class experiment.

A Tactile Sensing System Based on Arrays of Piezoelectric Polymer Transducers

Tactile sensing enables robots to interact safely and effectively with unstructured environments and humans in case of both voluntary and reactive interaction tasks. Focusing on humanoid robots, there is increasing interest in avoiding negative human feelings towards the “entity” [1], enabling robots to interact with humans in more intuitive and meaningful ways [2-4]. This requires designing methods for extracting important information from tactile stimuli leading to classification of touch modalities [5-8]. It is still unclear, however, whether touch modality actually plays an important role in the communication of social messages. A very interesting research area consists in exploring social touch for robotics through an interpretation of emotions and other social messages [9].

Towards prosthetic systems providing comprehensive tactile feedback for utility and embodiment.

The present research moves in the direction of enabling a bidirectional communication between the subject brain and the prosthetic limb, by providing the prosthesis with an artificial cutaneous sensing through an electronic skin. In this preliminary study, the skin response to the applied mechanical stimuli is conveyed to the human subject using electrotactile stimulation. Experimental tests on two healthy subjects show that a short training through reinforced learning increased considerably the success rate in the identification of the impact location. This preliminary study demonstrates the feasibility of communicating the tactile information from the electronic skin to the human subject using multichannel electrocutaneous stimulation. The result is promising since it implies that it might be possible to achieve the embodiment of the artificial skin into the body scheme of the human subject, relying on the brain ability to successfully process the artificial tactile information.

Tactile sensors with integrated piezoelectric polymer and low voltage organic thin-film transistors

This paper presents a novel approach for realizing tactile sensors by integrating a piezoelectric polymer with a low voltage Organic Thin-Film Transistor (OTFT) on flexible plastic substrates. The transducing element consists of PVDF piezoelectric film. The proposed device architecture is similar to the extended gate scheme, whereby the transducing material is connected to OTFT via extended gate. In this scheme, the OTFTs and the transducer material may be or same substrate, as in this case, or they may be on different substrates. The piezoelectric effect induced by an external mechanical stimulus modulates the output current of the OTFT. This scheme represents a simple and innovative solution for artificial sense of touch and is particularly suitable for the low-cost fabrication of so-called “electronic skin”. The electromechanical characterization shows that the sensing devices can detect forces stimuli within the range 0.5 - 8N.

Interface electronics for tactile sensing arrays

This paper presents the interface electronics design and implementation of a tactile sensing system for humanoid robot applications. The tactile system is designed to cover the human tactile sensing bandwidth ranging from 1Hz to 1kHz and to operate on a wide range of input forces/pressures. Some interface electronics prototypes have been designed and fabricated. The paper reports the experimental results and the validation of the proposed implementation. We report also results of the electro-mechanical response of the tactile sensing system (i.e. tactile sensing array + interface electronics) to external mechanical stimuli. The current implementation is a first step towards dedicated IC integration.

Computational Intelligence Techniques for Tactile Sensing Systems

Tactile sensing helps robots interact with humans and objects effectively in real environments. Piezoelectric polymer sensors provide the functional building blocks of the robotic electronic skin, mainly thanks to their flexibility and suitability for detecting dynamic contact events and for recognizing the touch modality. The paper focuses on the ability of tactile sensing systems to support the challenging recognition of certain qualities/modalities of touch. The research applies novel computational intelligence techniques and a tensor-based approach for the classification of touch modalities; its main results consist in providing a procedure to enhance system generalization ability and architecture for multi-class recognition applications. An experimental campaign involving 70 participants using three different modalities in touching the upper surface of the sensor array was conducted, and confirmed the validity of the approach.