Publications

The use of unmanned aerial vehicles (UAVs) in precision agriculture is gaining more and more interest. In this paper, we present a deep learning based method for estimating the crop and weed distribution from images captured by a UAV. The proposed approach runs on an embedded board equipped with a GPU. Quantitative experimental results have been obtained using real images from two different public datasets. The results demonstrate the effectiveness of the proposed approach.

Spinal cord injury can cause cognitive impairments even when no cerebral lesion is appreciable. As patients are forced to explore the environment in a non-canonical position (i.e., seated on a wheelchair), a modified relation with space can explain motor-related cognitive differences compared to non-injured individuals. Peripersonal space is encoded in motor terms, that is, in relation to the representation of action abilities and is strictly related to the affordance of reachability. In turn, affordances, the action possibilities suggested by relevant properties of the environment, are related to the perceiver’s peripersonal space and motor abilities. One might suppose that these motor-related cognitive abilities are compromised when an individual loses the ability to move. We shed light on this issue in 10 patients with paraplegia and 20 matched controls. All have been administered an affordances-related reachability judgement task adapted from Costantini, Ambrosini, Tieri, Sinigaglia, and Committeri (2010, Experimental Brain Research, 207, 95) and neuropsychological tests. Our findings demonstrate that patients and controls show the same level of accuracy in estimating the location of their peripersonal space boundaries, but only controls show the typical overestimation of reaching range. Secondly, patients show a higher variability in their judgements than controls. Importantly, this finding is related to the patients’ ability to perform everyday tasks. Finally, patients are not faster in making their judgements on reachability in peripersonal space, while controls are. Our results suggest that not moving freely or as usual in the environment impact decoding of action-related properties even when the upper limbs are not compromised.

Despite the growing interest in brain–machine interface (BMI)-driven neuroprostheses, the translation of the BMI output into a suitable control signal for the robotic device is often neglected. In this article, we propose a novel control approach based on dynamical systems that was explicitly designed to take into account the nature of the BMI output that actively supports the user in delivering real-valued commands to the device and, at the same time, reduces the false positive rate. We hypothesize that such a control framework would allow users to continuously drive a mobile robot and it would enhance the navigation performance. 13 healthy users evaluated the system during three experimental sessions. Users exploit a 2-class motor imagery BMI to drive the robot to five targets in two experimental conditions: with a discrete control strategy, traditionally exploited in the BMI field, and with the novel continuous control framework developed herein. Experimental results show that the new approach: 1) allows users to continuously drive the mobile robot via BMI; 2) leads to significant improvements in the navigation performance; and 3) promotes a better coupling between user and robot. These results highlight the importance of designing a suitable control framework to improve the performance and the reliability of BMI-driven neurorobotic devices.

We analyze non-linear interactions of 2N -form Maxwell fields in a space-time of dimension D = 4N.Based on the Pasti-Sorokin-Tonin (PST) method, we derive the general consistency condition for the dynamics to respect both manifest SO(2)-duality invariance and manifest Lorentz invariance. For a generic dimension D = 4N, we determine a canonical class of exact solutions of this condition, which represent a generalization of the known non-linear duality invariant Maxwell theories in D = 4. The resulting theories are shown to be equivalent to a corresponding class of canonical theories formulated à la Gaillard-Zumino-Gibbons-Rasheed (GZGR), where duality is a symmetry only of the equations of motion. In dimension D = 8, via a complete solution of the PST consistency condition, we derive new non-canonical manifestly duality invariant quartic interactions. Correspondingly, we construct new non-trivial quartic interactions also in the GZGR approach, and establish their equivalence with the former. In the presence of charged dyonic p-brane sources, we reveal a basic physical inequivalence of the two approaches. The power of our method resides in its universal character, reducing the construction of non-linear duality invariant Maxwell theories to a purely algebraic problem.

Introduction: Patients with late-onset Pompe disease (LOPD) receiving enzyme replacement therapy (ERT) may develop IgG antibodies against alglucosidase alpha (anti-rhGAA) in the first 3 months of treatment. The exact role of these antibodies in modulating efficacy of ERT in this group of patients is still not fully understood. To assess whether anti rh-GAA antibodies interfere with ERT efficacy, we studied a large Italian cohort of LOPD patients. Methods: We analyzed clinical findings and performed serial measurements of IgG anti rh-GAA antibody titers from 64 LOPD patients treated with ERT. The first examination (T0) was completed on average at 17.56 months after starting ERT, while the follow-up (T1) was collected on average at 38.5 months. Differences in T0-T1 delta of the six-minute walking test (6MWT), MRC sum score (MRC), gait, stairs and chair performance (GSGC) and forced vital capacity (FVC) were considered and then related to the antibody titers. Results: Almost 22% of the patients never developed antibodies against GAA, while 78.1% had a positive titer (31.2% patients developed a low titer, 43.8% a medium titer and 3.1% a high titer). No statistical significance was found in relating the T0-T1 delta differences and antibody titers, except for MRC sum score values in a subgroup of patients treated36 months, in which those with a null antibody titer showed a greater clinical improvement than patients with a positive titer. Conclusion: Our results confirm that in a large cohort of LOPD patients, anti rh-GAA antibody generation did not significantly affect either clinical outcome or ERT efficacy. However, in the first 36 months of treatment, a possible interference of low-medium antibody titers with the clinical status could be present. Therefore, a careful and regular evaluation of antibody titers, especially in cases with evidence of clinical decline despite ERT, should be performed.

Human detection and tracking is an essential task for service robots, where the combined use of multiple sensors has potential advantages that are yet to be fully exploited. In this paper, we introduce a framework allowing a robot to learn a new 3D LiDAR-based human classifier from other sensors over time, taking advantage of a multisensor tracking system. The main innovation is the use of different detectors for existing sensors (i.e. RGB-D camera, 2D LiDAR) to train, online, a new 3D LiDAR-based human classifier based on a new ‘trajectory probability’. Our framework uses this probability to check whether new detection belongs to a human trajectory, estimated by different sensors and/or detectors, and to learn a human classifier in a semi-supervised fashion. The framework has been implemented and tested on a real-world dataset collected by a mobile robot. We present experiments illustrating that our system is able to effectively learn from different sensors and from the environment, and that the performance of the 3D LiDAR-based human classification improves with the number of sensors/detectors used.

In nature, lightweight and low-powered insects are ideal model systems to study motion perception strategies. Understanding the underlying characteristics and functionality of insects’ visual systems is not only attractive to neural system modellers but also critical in providing effective solutions to future robotics. This paper presents a novel modelling of dynamic vision system inspired by Drosophila physiology for mimicking fast motion tracking and a closed-loop behavioural response to fixation. The proposed model was realised on the embedded system in an autonomous micro robot which has limited computational resources. A monocular camera was applied as the only motion sensing modality. Systematic experiments including open-loop and closed-loop bio-robotic tests validated the proposed visual fixation model: the robot showed motion tracking and fixation behaviours similarly to insects; the image processing frequency can maintain 25 45Hz. Arena tests also demonstrated a successful following behaviour aroused by fixation in navigation.

The efficient use and transport of byproduct gypsum to be used as a secondary raw material implies its conditioning in a suitable physical form. Most frequently demands are to supply a concentrated suspension, which is sufficiently fluid and stable to be pumped and stored in tanks. The preparation of a fluid concentrated gypsum slurry is a demanding task, since particle anisotropy and surface interactions prevent the attainment of high solid loadings. In this paper we present the testing of different families of dispersants, and discuss the modifications of the surface interactions exerted by each, in terms of surface potential and macroscopic rheological properties. We show that by appropriately choosing the dispersing system it is possible to prepare pumpable concentrated slurries with a solid content approaching 80% by weight. Star-shaped polymers are shown to enable the preparation of highly concentrated suspensions, which exhibit good rheological properties and retain stability both under rest and at the highest shear rates, thus being adapted to be stored in tanks, and to be transferred by pumping. These dispersing agents do not induce electrostatic repulsion among the particles, ζ-potential being nearly zero. Steric repulsion is responsible for the dispersion, and even at the highest concentration the suspension does not show jamming and thickening.

This paper presents a physiological monitoring system for assistive robots using a thermal camera. It is based on the detection of subtle changes in temperature observed on different parts of the face. First, we segment and estimate these face regions on thermal images. Then, by applying Fourier analysis on temperature data, we estimate respiration and heartbeat rate. This physiological monitoring system has been integrated in an assistive robot for elderly people at home, as part of the ENRICHME project. Its performance has been evaluated on a new thermal dataset for physiological monitoring, which is made publicly available for researchpurposes.