Fulvio Mastrogiovanni

Skin spatial calibration using force/torque measurements

This paper deals with the problem of estimating the position of tactile elements (i.e. taxels) that are mounted on a robot body part. This problem arises with the adoption of tactile systems with a large number of sensors, and it is particularly critical in those cases in which the system is made of flexible material that is deployed on a curved surface. In this scenario the location of each taxel is partially unknown and difficult to determine manually. Placing the device is in fact an inaccurate procedure that is affected by displacements in both position and orientation. Our approach is based on the idea that it is possible to automatically infer the position of the taxels by measuring the interaction forces exchanged between the sensorized part and the environment. The location of the contact is estimated through force/torque (F/T) measures gathered by a sensor mounted on the kinematic chain of the robot. Our method requires few hypotheses and can be effectively implemented on a real platform, as demonstrated by the experiments with the iCub humanoid robot.

Analysis of human behavior recognition algorithms based on acceleration data

The automatic assessment of the level of independence of a person, based on the recognition of a set of Activities of Daily Living, is among the most challenging research fields in Ambient Intelligence. The article proposes a framework for the recognition of motion primitives, relying on Gaussian Mixture Modeling and Gaussian Mixture Regression for the creation of activity models. A recognition procedure based on Dynamic Time Warping and Mahalanobis distance is found to: (i) ensure good classification results; (ii) exploit the properties of GMM and GMR modeling to allow for an easy run-time recognition; (iii) enhance the consistency of the recognition via the use of a classifier allowing unknown as an answer.

Towards automated self-calibration of robot skin

This paper deals with the problem of calibrating a large number of tactile elements (i.e., taxels) organized in a skin sensor system after fixing them to a robot body part. This problem has not received much attention in literature because of the lack of large-scale skin sensor systems. The proposed approach is based on a controlled compliance motion with respect to external objects whose pose is known, which allows a robot to determine the location of its own taxels. The major contribution of this work is the formulation of the skin calibration problem as a maximum-likelihood mapping problem in a 6D space, where both the position and the orientation of each taxel are recovered. An effective calibration process is envisaged that, given a compliance control law that assures prolonged contact maintainance between a given body part and an external object, returns a maximum-likelihood estimate of detected taxel poses. Simulations validate the approach.

Design of an embedded networking infrastructure for whole-body tactile sensing in humanoid robots

Whole-body tactile sensing, especially when aimed at implementing tactile-based motion control, requires well-defined infrastructures. Requirements related to Real-Time operation, bandwidth and robustness must be carefully addressed. With respect to a tactile technology described in [2], this paper discusses a number of infrastructural solutions based on a Ethernet Real-Time protocol. Associated benefits and drawbacks are discussed. The identified models and solutions are compared both analytically and with extensive numerical simulations.

Context assessment strategies for Ubiquitous Robots

This paper presents an architecture for context-aware Ubiquitous Robotics applications, where mobile robots cooperate with intelligent environments to fulfill their tasks. Specifically, the work is focused on distributed knowledge representation issues and context assessment strategies, and introduces a technique for on-line context recognition in highly dynamic environments. Experimental validation, performed in a civillian hospital building, is described and discussed.

On the Problem of the Automated Design of Large-Scale Robot Skin

This paper describes automated procedures for the design and deployment of artificial skin for humanoid robots. This problem is challenging under different perspectives: on the one hand, different robots are characterized by different shapes, thereby requiring a high degree of skin customization; on the other hand, it is necessary to define optimal criteria specifying how the skin must be placed on robot parts. This paper addresses the problem of optimally covering robot parts with tactile sensors, discussing possible solutions with reference to a specific artificial skin technology for robots, which has been developed in the past few years. Results show that it is possible to automate the majority of the required steps, with promising results in view of a future complete automation of the process.

Tactile sensing: Steps to artificial somatosensory maps

In this paper a framework for representing tactile information in robots is discussed. Control models exploiting tactile sensing are fundamental in social Human-Robot interaction tasks. Difficulties arising in rendering the sense of touch in robots are at different levels: both representation and computational issues must be considered. A layered system is proposed, which is inspired from tactile sensing in humans for building artificial somatosensory maps in robots. Experiments in simulation are used to validate the approach.

Towards the creation of tactile maps for robots and their use in robot contact motion control

The recent availability of large-scale tactile systems for robots implies the design and development of tactile representation frameworks able to inform tactile-based robot control strategies. As a matter of fact, this is a non-trivial problem in knowledge representation. Starting from the previous work, we introduce the notion of tactile maps for robots, and we propose an architecture that addresses all the various phases which allow us to implement tactile-based representation and robot control. The proposed architecture is validated using simulations, which are aimed at assessing the robustness and the performance of the chosen control strategy with respect to the accuracy of the robot skin representation as well as to the force feedback needed to implement tactile-based contact tasks. We introduce the notion of artificial somatosensory maps for robots.We propose an architecture that addresses all the various phases necessary to implement tactile-based representation and control.We show that artificial somatosensory maps are a good representation for controlling robot contact tasks.

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.

Semantic-Aware Real-Time Scheduling in Robotics

This paper introduces semantic-aware real-time (SeART), an extension to conventional operating systems, which deals with complex real-time robotics applications. SeART addresses the problem of selecting a subset of tasks to be scheduled depending on the current operating context: mission objectives, other tasks currently executed, the availability or unavailability of sensors and other resources, as well as temporal constraints. Toward this end, SeART is able to represent the semantics of tasks to be scheduled, i.e., what tasks are meant for, and to use this information in the scheduling process. This paper describes in detail the SeART architecture by focusing on representations and reasoning procedures, and presenting a case-study which is related to mobile robotics for autonomous objects transportation.