scispace - formally typeset
A

Alessandro Ridolfi

Researcher at University of Florence

Publications -  175
Citations -  2572

Alessandro Ridolfi is an academic researcher from University of Florence. The author has contributed to research in topics: Underwater robotics & Kalman filter. The author has an hindex of 21, co-authored 155 publications receiving 1936 citations. Previous affiliations of Alessandro Ridolfi include University of Pisa & University of Geneva.

Papers
More filters
Journal ArticleDOI

BIOTEX—Biosensing Textiles for Personalised Healthcare Management

TL;DR: A wearable sensing system has been developed that integrates a textile-based fluid handling system for sample collection and transport with a number of sensors including sodium, conductivity, and pH sensors, which has huge implications for the field of sports and human performance.
Journal ArticleDOI

A new AUV navigation system exploiting unscented Kalman filter

TL;DR: The authors present an innovative navigation strategy specifically designed for AUVs, based on the Unscented Kalman Filter (UKF), which proves to be effective if applied to this class of vehicles and allows the authors to achieve a satisfying accuracy improvement compared to standard navigation algorithms.
Journal ArticleDOI

Animal-assisted activity and emotional status of patients with Alzheimer's disease in day care.

TL;DR: In this sample of severe AD patients in ADCC, AAA was associated with a decrease in anxiety and sadness and an increase in positive emotions and motor activity in comparison with a control activity.
Journal ArticleDOI

An Attitude Estimation Algorithm for Mobile Robots Under Unknown Magnetic Disturbances

TL;DR: In this paper, an attitude estimation strategy for autonomous underwater vehicles (AUV) is proposed, which includes the identification of some critical issues that arise when AUV attitude estimation algorithms are applied in practice.
Journal ArticleDOI

An unscented Kalman filter based navigation algorithm for autonomous underwater vehicles

TL;DR: In this article, the authors present a comparison between the Extended Kalman Filter (EKF) approach, classically used in the field of underwater robotics and an unscented Kalman filter (UKF), adapted to the AUV case, demonstrates to be a good trade-off between estimation accuracy and computational load.