Hand-eye calibration of laser profile sensors is the process of extracting the homogeneous transformation between the laser profile sensor frame and the end-effector frame of a robot in order to express the data extracted by the sensor in the robot’s global coordinate system. For laser profile scanners this is a challenging procedure, as they provide data only in two dimensions and state-of-the-art calibration procedures require the use of specialised calibration targets. This paper presents a novel method to extract the translation-part of the hand-eye calibration matrix with rotation-part known a priori in a target-agnostic way. Our methodology is applicable to any 2D image or 3D object as a calibration target and can also be performed in situ in the final application. The method is experimentally validated on a real robot-sensor setup with 2D and 3D targets.

Robots can grasp, even manipulate, objects with different shape, weight and size thanks to the their end-effectors. These are mostly constituted by two fingers, and are known as grippers. However, despite being quite simple for human beings, manipulation is not so straightforward to carry out on robotic systems. One of the main obstacles is the lack of reliable control methods: this is especially true for pneumatic grippers. Such devices are often mounted on industrial robots, though their behavior does not go beyond basic fully-open or fully-closed operations. This happens also as a consequence of the incapability of taking into account frictional effects limiting the pneumatic gripper performance. In this article, a new control strategy is delivered to solve this issue. The proposed strategy allows controlling the grasping force of a pneumatic gripper, without performance degradation due to friction. A pneumatic gripper was built and instrumented with several sensors to experimentally validate the proposed control strategy. The gripper was mechanically connected to a robotic arm and tested with different desired force profiles upon a wide range of force.

The paper introduce a robotics software control architecture suitable for the development of complete robotic industrial applications. The architecture fuse the state-of-the-art software technologies in a single standalone platform to provide an easy integration between all the software components necessary to control a robotic application, i.e. PLC logic, robot motion program. The main goal is to provide an architecture as much as possible hardware agnostic to develop easily portable software.

Mechanical systems are typically composed of a number of contacting surfaces that move against each other. Such surfaces are subject to friction forces. These dissipate part of the actuation energy and cause an undesired effect on the overall system functioning. Therefore, a suitable model of friction is needed to elide its action. The choice of such a model is not always straightforward, as it is influenced by the system properties and dynamics. In this paper, we show the identification of different friction models and evaluate their prediction capability on an experimental dataset. Despite being state-of-the-art models, some modifications were introduced to improve their performance. A pneumatic gripper was used to collect the data for the models evaluation. Two experimental setups were mounted to execute the experiments: information from two pressure sensors, a load cell and a position sensor was employed for the identification. During the experiments, the gripper was actuated at different constant velocities. Results indicate that all the identified models offer a proper prediction of the real friction force.

In recent years the robotics field has witnessed an interesting new trend. Several companies started the production of service robots whose aim is to cooperate with humans. The robots developed so far are either rather expensive or unsuitable for manipulation tasks. This article presents the result of a project which wishes to demonstrate the feasibility of an affordable humanoid robot. R1 is able to navigate, and interact with the environment (grasping and carrying objects, operating switches, opening doors etc). The robot is also equipped with a speaker, microphones and it mounts a display in the head to support interaction using natural channels like speech or (simulated) eye movements. The final cost of the robot is expected to range around that of a family car, possibly, when produced in large quantities, even significantly lower. This goal was tackled along three synergistic directions: use of polymeric materials, light-weight design and implementation of novel actuation solutions. These lines, as well as the robot with its main features, are described hereafter.

Tactile sensing is of fundamental importance for object manipulation and perception. Several sensors for hands have been proposed in the literature, however, only a few of them can be fully integrated with robotic hands. Typical problems preventing integration include the need for deformable sensors that can be deployed on curved surfaces, and wiring complexity. In this paper we describe a fingertip for the hands of the iCub robot, each fingertip consists of 12 sensors. Our approach builds on previous work on the iCub tactile system. The sensing elements of the fingertip are capacitive sensors made from a flexible PCB, and a multi-layer fabric that includes the dielectric material and the conductive layer. The novelty the proposed sensor lies in incorporating the multi-layer fabric technology into a small fingertip sensor that can be attached to the hands of a humanoid robot. The new sensors are more robust. The manufacturing is easier and relies on industrial techniques for the fabrication of the components, which results in higher repeatability. We performed experimental characterization of the sensor. We show that the sensor is able to detect forces as low as 0.05 N with no cross-talk between the taxels. We identified some hysteresis in the response of the sensor which must be taken into account if the robot exerts large forces for a long period of time. The taxels have spatially overlapping receptive fields, this has been demonstrated to be a useful property that allows hyperacuity.

In this paper we tackle the problem of estimating the local compliance of tactile arrays exploiting global measurements from a single force and torque sensor. The proposed procedure exploits a transformation matrix (describing the relative position between the local tactile elements and the global force/torque measurements) to define a linear regression problem on the unknown local stiffness. Experiments have been conducted on the foot of the iCub robot, sensorized with a single force/torque sensor and a tactile array of 250 tactile elements (taxels) on the foot sole. Results show that a simple calibration procedure can be employed to estimate the stiffness parameters of virtual springs over a tactile array and to use these model to predict normal forces exerted on the array based only on the tactile feedback. Leveraging on previous works [1] the proposed procedure does not necessarily need a-priori information on the transformation matrix of the taxels which can be directly estimated from available measurements.

Even though the sense of touch is crucial for humans, most humanoid robots lack tactile sensing. While a large number of sensing technologies exist, it is not trivial to incorporate them into a robot. We have developed a compliant “skin” for humanoids that integrates a distributed pressure sensor based on capacitive technology. The skin is modular and can be deployed on nonflat surfaces. Each module scans locally a limited number of tactile-sensing elements and sends the data through a serial bus. This is a critical advantage as it reduces the number of wires. The resulting system is compact and has been successfully integrated into three different humanoid robots. We have performed tests that show that the sensor has favorable characteristics and implemented algorithms to compensate the hysteresis and drift of the sensor. Experiments with the humanoid robot iCub prove that the sensors can be used to grasp unmodeled, fragile objects.

A framework to discriminate tactile stimuli delivered to an artificial touch sensor is presented. Following a neuromimetic approach, we encode the signals from a 24-capacitive sensor fingertip into spiking activity through a network of leaky integrate-and-fire neurons. The activity resulting from the stimulation of the touch sensor through Braille-like dot patterns is then analysed by means of a newly defined Information measure which explicitly takes into consideration the metrics of the spike train space. Results show that an optimal discrimination of the entire set of 26 stimuli (i.e. 100% correct classification) is reached early after the stimulus onset. Interestingly, the method proves to be effective with both statically and dynamically delivered stimulation which are hard to decode because of the similarity between encoded firing activity given to the proximity of the patterns presented. The decoding analysis allowed us to corroborate the working hypothesis that human tactile discrimination relies on optimal encoding/decoding processes already at the level of the primary stage neurons in the somatosensory pathway.

In order to successfully perform object manipulation, humanoid robots must be equipped with tactile sensors. However, the limited space that is available in robotic fingers imposes severe design constraints. In [1] we presented a small prototype fingertip which incorporates a capacitive pressure system. This paper shows an improved version, which has been integrated on the hand of the humanoid robot iCub. The fingertip is 14.5 mm long and 13 mm wide. The capacitive pressure sensor system has 12 sensitive zones and includes the electronics to send the 12 measurements over a serial bus with only 4 wires. Each synthetic fingertip is shaped approximately like a human fingertip. Furthermore, an integral part of the capacitive sensor is soft silicone foam, and therefore the fingertip is compliant. We describe the structure of the fingertip, their integration on the humanoid robot iCub and present test results to show the characteristics of the sensor.

The sense of touch is of major importance for object handling. Nevertheless, adequate cutaneous sensors for humanoid robot hands are still missing. Designing such sensors is challenging, because they should not only give reliable measurements and integrate many sensing points into little space, but they should also be compliant and should not obstruct the other functions of the robot. This paper presents a capacitive pressure sensor system with 108 sensitive zones for the hands of the humanoid robot iCub. In particular, the palm has 48 taxels and each of the five fingertips has 12 taxels. The size and the shape of the hand are similar to that of a human child. When designing the sensors, we paid special attention to the integration on the robot. Also the ease and speed of production was an important design factor. Furthermore, the sensor incorporates silicone foam and is therefore compliant. We show the working principle of the sensor, how it has been integrated into the hands, and describe experiments that have been performed to show the characteristics of the sensor.

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Eye motion strategies in animals and humans have the goal of optimizing visual perception, therefore, the study of eye motions plays an important role in the design of humanoid robot eye systems. Saccades and smooth pursuit in humans and primates are a significant class of ocular motions, which obey the so called Listing's Law, stating that admissible eye's orientations have always zero torsion during motion. In this paper we present a model of the eye plant proving that Listing's Law implementation is strongly related with the geometry of the eye and its actuation system (extraocular muscles). The proposed model has been used to provide the guidelines for the design of a tendon driven humanoid robot eye. Experimental tests, presented in this paper, validate the model by performing a quantitative comparison of the performance of the robot eye with physiological data measured in humans and primates during saccades.

This paper presents a model for a tendon driven robot eye designed to emulate the actual saccadic and smooth pursuit movements performed by human eyes. Physiological saccadic motions obey the so called Listing's law which constrains the admissible eye's angular velocities. The paper discusses conditions making possible to implement the Listing's law on a purely mechanical basis, i.e. without active control