Andrea Cirillo

A Conformable Force/Tactile Skin for Physical Human–Robot Interaction

In this letter, a new sensorized flexible skin has been used to enhance safety and intuitiveness of physical human-robot interaction (HRI) in applications where both intentional and unintentional contacts may occur. The new technological contribution with respect to other skin sensors consists of the capability of measuring both the position of the contact point and the three components of the applied force with high repeatability and accuracy. To show how this innovative technology enables the exploitation of control laws for intuitive HRI, two standard control strategies have been implemented to perform both manual guidance with multiple contact points and safe reaction in case of unintentional collision detection, at the same time. In both cases, an admittance control scheme with a second order kinematic control is adopted. A multipriority redundancy resolution strategy is implemented in the case of manual guidance. The experimental verification of the sensor capabilities is made using a patch of the skin installed on a link of a KUKA LWR4 robot.

Experimental Comparison of Sensor Fusion Algorithms for Attitude Estimation

Abstract Inertial Measurement Unit is commonly used in various applications especially as a low-cost system for localization and attitude estimation. Some applications are: real-time motion capture system, gait analysis for rehabilitation purposes, biomedical applications, advanced robotic applications such as mobile robot localization and Unmanned Aerial Vehicles (UAV) attitude estimation. In all the mentioned applications the accuracy and the fast response are the most important requirements, thus the research is focused on the design and the implementation of highly accurate hardware systems and fast sensor data fusion algorithms, named Attitude and Heading Reference System (AHRS), aimed at estimating the orientation of a rigid body with respect to a reference frame. A large number of different solutions can be found in the literature, and an experimental comparison of the most popular is presented in this work. In particular, the algorithm based on the gradient descent method and the algorithm based on a nonlinear complementary filter are compared to a standard Extended Kalman Filter (EKF) with the aim to show that a general method can easily compete with ad-hoc solutions and even outperform them in particular conditions. In order to validate the estimation accuracy a Kuka robot is used to compute the ground truth. Moreover, in order to estimate the computational burden, the algorithms are implemented on an ARM-Cortex M4-based evaluation board.

A proximity/contact-force sensor for Human Safety in industrial robot environment

In this paper, a new approach based on a proximity/contact-force sensor to improve the Human Safety in the Human-Robot cooperative tasks is presented. The sensor is able to detect both the presence of a nearby object and the contact pressure exercised by an external object when a collision occurs. The sensor is interfaced with an ABB industrial robot using only the standard control unit; the standard RAPID primitives are used to define the robot task. The task speed is reduced as soon as an obstacle is detected by the proximity sensing element, while, the robot is stopped when a contact occurs, that is detected by a contact-force sensing element.

Cross-modal visuo-tactile object recognition using robotic active exploration

In this work, we propose a framework to deal with cross-modal visuo-tactile object recognition. By cross-modal visuo-tactile object recognition, we mean that the object recognition algorithm is trained only with visual data and is able to recognize objects leveraging only tactile perception. The proposed cross-modal framework is constituted by three main elements. The first is a unified representation of visual and tactile data, which is suitable for cross-modal perception. The second is a set of features able to encode the chosen representation for classification applications. The third is a supervised learning algorithm, which takes advantage of the chosen descriptor. In order to show the results of our approach, we performed experiments with 15 objects common in domestic and industrial environments. Moreover, we compare the performance of the proposed framework with the performance of 10 humans in a simple cross-modal recognition task.

A Distributed Tactile Sensor for Intuitive Human-Robot Interfacing

Safety of human-robot physical interaction is enabled not only by suitable robot control strategies but also by suitable sensing technologies. For example, if distributed tactile sensors were available on the robot, they could be used not only to detect unintentional collisions, but also as human-machine interface by enabling a new mode of social interaction with the machine. Starting from their previous works, the authors developed a conformable distributed tactile sensor that can be easily conformed to the different parts of the robot body. Its ability to estimate contact force components and to provide a tactile map with an accurate spatial resolution enables the robot to handle both unintentional collisions in safe human-robot collaboration tasks and intentional touches where the sensor is used as human-machine interface. In this paper, the authors present the characterization of the proposed tactile sensor and they show how it can be also exploited to recognize haptic tactile gestures, by tailoring recognition algorithms, well known in the image processing field, to the case of tactile images. In particular, a set of haptic gestures has been defined to test three recognition algorithms on a group of users. The paper demonstrates how the same sensor originally designed to manage unintentional collisions can be successfully used also as human-machine interface.

A FE analysis of a silicone deformable interface for distributed force sensors

The authors propose a novel modular artificial skin sensor, based on optoelectronic technology, able to estimate both normal and shear contact force components. The skin is constituted by sensor modules, each one characterized by four sensing elements that consist of a couple of infrared Light Emitting Diode and Photo-Detector covered by a silicone layer that transduces the external force in a mechanical deformation, measured by the four photodetectors. The skin prototype is obtained from the interconnection of several sensing modules and a single deformable layer is obtained with the use of two different silicone materials that differ for the shore hardness. Several FEM simulations have been carried out in order to demonstrate that the use of the two materials allows to obtain a single silicone structure with a very low coupling between two adjacent sensing modules.

Force/Tactile Sensors Based on Optoelectronic Technology for Manipulation and Physical Human–Robot Interaction

Design and realization of autonomous robotic platforms require crucial information on their surroundings especially when robots should interact with the environment and humans. In many cases, perception is necessary to correctly accomplish tasks in a dynamic environment where a model is hard to obtain. Grasping and fine manipulation of objects with different shapes and surface characteristics as well as detection of contacts between the robot and the environment are easily enabled by tactile sensing. The sense of touch represents the most natural way to obtain relevant information during an interaction task, such as parameters like surface friction, exchanged forces and torques, object shape. This chapter provides an overview of the authors’ work on force/tactile sensors development. By exploiting optoelectronic technology, the authors designed and realized two tactile sensors that can be used to execute both fine manipulation of objects and safe interaction tasks with humans. The chapter describes both sensors in detail and provides an experimental validation of their capabilities.

A mechatronic approach for robust stiffness estimation of variable stiffness actuators

This paper proposes a novel mechatronic approach for on-line robust estimation of both torque and stiffness of Variable Stiffness Actuators (VSA). The proposed solution is demonstrated robust with respect to uncertainties affecting both the dynamic parameters of the motor and to the efficiency of the motor gearbox. The strategy adopted to gain robustness compared to existing techniques is based on adaptive algorithms that exploit an additional measurement, i.e, the instantaneous elastic energy stored in the transmission. A novel optoelectronic sensor to perform such measurement in a minimally invasive and low-cost manner is also proposed and experimentally tested.

Control of linear and rotational slippage based on six-axis force/tactile sensor

In-hand manipulation is certainly one of the most challenging problems in robotic manipulation. Solutions to this problem depend on the specific device used to grab the object, but nowadays, the trend is to exploit not only the gripper but also external constraints, such as other objects in the environment or external forces, like gravity. This allows a robot to manipulate an object even with very simple grippers, like a parallel gripper. Nevertheless, even for a simple grasping task, which aims at grabbing the object with a given fixed orientation or for executing a controlled slip, information on the contact between the fingers of the gripper and the object is relevant. In these cases, both linear and rotational slipping should be controlled during the grasping phase and during the motion phase. The present paper proposes a control strategy for the first objective, namely slipping avoidance. The strategy is based on contact information provided by a six-axis force/tactile sensor, able to measure contact force and torque as well as able to provide information on the contact geometry, that means orientation of the object with respect to the gripper. Experiments on a parallel gripper sensorized with a new force/tactile sensor and mounted on a Kuka iiwa show how the strategy successfully allows the robot to safely manipulate a rigid object in various friction conditions of its surface.

Design and evaluation of tactile sensors for the estimation of grasped wire shape

This work presents the design of the tactile sensor within the WIRES project, aimed at automating the cabling process of switchgears. The design objective is the development of a sensor able to estimate both position and orientation of the grasped wire, with respect to a known reference frame. To this aim, an extensive Finite Element analysis has been performed to optimize the number of taxels, by simulating the contact between the sensor and wires with various diameters and grasping conditions. A specific algorithm for the reconstruction of the grasped wire shape has been developed and used for defining suitable metrics adopted in the design phase. Based on simulation results, a first prototype of the sensor has been realized and the proposed reconstruction algorithm has been experimentally tested.