Machine Learning for Perception and Autonomous Navigation of Service Mobile Robots

L'abstract è presente nell'allegato / the abstract is in the attachmen

Pauli Tomography: complete characterization of a single qubit device

The marriage of Quantum Physics and Information Technology, originally motivated by the need for miniaturization, has recently opened the way to the realization of radically new information-processing devices, with the possibility of guaranteed secure cryptographic communications, and tremendous speedups of some complex computational tasks. Among the many problems posed by the new information technology there is the need of characterizing the new quantum devices, making a complete identification and characterization of their functioning. As we will see, quantum mechanics provides us with a powerful tool to achieve the task easily and efficiently: this tools is the so called quantum entanglement, the basis of the quantum parallelism of the future computers. We present here the first full experimental quantum characterization of a single-qubit device. The new method, we may refer to as ''quantum radiography'', uses a Pauli Quantum Tomography at the output of the device, and needs only a single entangled state at the input, which works on the test channel as all possible input states in quantum parallel. The method can be easily extended to any n-qubits device

Bioethanol in biofuels checked by an amperometric organic phase enzyme electrode (OPEE) working in “substrate antagonism” format

The bioethanol content of two samples of biofuels was determined directly, after simple dilution in decane, by means of an amperometric catalase enzyme biosensor working in the organic phase, based on substrate antagonisms format. The results were good from the point of view of accuracy, and satisfactory for what concerns the recovery test by the standard addition method. Limit of detection (LOD) was on the order of 2.5 × 10−5 M. © 2016 by the authors; licensee MDPI, Basel, Switzerland

Radioluminescence of synthetic and natural quartz

The effect of X-ray irradiation and thermal treatments on the radio-luminescence emission spectrum of both a natural pegmatitic quartz and a synthetic one was investigated. All the emission spectra could be deconvolved into the same set of five Gaussian components. Among the identified RL bands, a blue emission at 2.53 eV (480 nm) is enhanced under X-ray irradiation. A strong correlation with the sensitization of the so called "110 degrees C" TSL peak (in our measurements seen at lower temperature due to the lower heating rate) was proved, suggesting that the recombination centers associated with the 2.53 eV band are produced under X-ray irradiation and are involved in both RL and TSL luminescence mechanisms. When each irradiation was followed by heating up to 500 degrees C a strong sensitization of the RL band emitting at 3.44 eV and of the 110 degrees C TSL peak were observed. A perfect correlation between the RL and TSL emissions suggests that the recombination centers involved in the RL and TSL emissions are the sam

Human-Centered Navigation and Person-Following with Omnidirectional Robot for Indoor Assistance and Monitoring

Robot assistants and service robots are rapidly spreading out as cutting-edge automation solutions to support people in their everyday life in workplaces, health centers, and domestic environments. Moreover, the COVID-19 pandemic drastically increased the need for service technology to help medical personnel in critical conditions in hospitals and domestic scenarios. The first requirement for an assistive robot is to navigate and follow the user in dynamic environments in complete autonomy. However, these advanced multitask behaviors require flexible mobility of the platform to accurately avoid obstacles in cluttered spaces while tracking the user. This paper presents a novel human-centered navigation system that successfully combines a real-time visual perception system with the mobility advantages provided by an omnidirectional robotic platform to precisely adjust the robot orientation and monitor a person while navigating. Our extensive experimentation conducted in a representative indoor scenario demonstrates that our solution offers efficient and safe motion planning for person-following and, more generally, for human-centered navigation tasks

RL-DWA Omnidirectional Motion Planning for Person Following in Domestic Assistance and Monitoring

Robot assistants are emerging as high-tech solutions to support people in everyday life. Following and assisting the user in the domestic environment requires flexible mobility to safely move in cluttered spaces. We introduce a new approach to person following for assistance and monitoring. Our methodology exploits an omnidirectional robotic platform to detach the computation of linear and angular velocities and navigate within the domestic environment without losing track of the assisted person. While linear velocities are managed by a conventional Dynamic Window Approach (DWA) local planner, we trained a Deep Reinforcement Learning (DRL) agent to predict optimized angular velocities commands and maintain the orientation of the robot towards the user. We evaluate our navigation system on a real omnidirectional platform in various indoor scenarios, demonstrating the competitive advantage of our solution compared to a standard differential steering following

Comparison between a direct-flow SPR immunosensor for ampicillin and a competitive conventional amperometric device: analytical features and possible applications to real samples

In this research, we developed a direct-flow surface plasmon resonance (SPR) immunosensor for ampicillin to perform direct, simple, and fast measurements of this important antibiotic. In order to better evaluate the performance, it was compared with a conventional amperometric immunosensor, working with a competitive format with the aim of finding out experimental real advantages and disadvantages of two respective methods. Results showed that certain analytical features of the new SPR immunodevice, such as the lower limit of detection (LOD) value and the width of the linear range, are poorer than those of a conventional amperometric immunosensor, which adversely affects the application to samples such as natural waters. On the other hand, the SPR immunosensor was more selective to ampicillin, and measurements were more easily and quickly attained compared to those performed with the conventional competitive immunosensor

Exploiting quantum parallelism of entanglement for a complete experimental quantum characterization of a single qubit device

We present the first full experimental quantum tomographic characterization of a single-qubit device achieved with a single entangled input state. The entangled input state plays the role of all possible input states in quantum parallel on the tested device. The method can be trivially extended to any n-qubits device by just replicating the whole experimental setup n times.Comment: 4 pages in revtex4 with 4 eps figure

PIC4rl-gym: a ROS2 modular framework for Robots Autonomous Navigation with Deep Reinforcement Learning

Learning agents can optimize standard autonomous navigation improving flexibility, efficiency, and computational cost of the system by adopting a wide variety of approaches. This work introduces the \textit{PIC4rl-gym}, a fundamental modular framework to enhance navigation and learning research by mixing ROS2 and Gazebo, the standard tools of the robotics community, with Deep Reinforcement Learning (DRL). The paper describes the whole structure of the PIC4rl-gym, which fully integrates DRL agent's training and testing in several indoor and outdoor navigation scenarios and tasks. A modular approach is adopted to easily customize the simulation by selecting new platforms, sensors, or models. We demonstrate the potential of our novel gym by benchmarking the resulting policies, trained for different navigation tasks, with a complete set of metrics

Online Learning of Wheel Odometry Correction for Mobile Robots with Attention-based Neural Network

Modern robotic platforms need a reliable localization system to operate daily beside humans. Simple pose estimation algorithms based on filtered wheel and inertial odometry often fail in the presence of abrupt kinematic changes and wheel slips. Moreover, despite the recent success of visual odometry, service and assistive robotic tasks often present challenging environmental conditions where visual-based solutions fail due to poor lighting or repetitive feature patterns. In this work, we propose an innovative online learning approach for wheel odometry correction, paving the way for a robust multi-source localization system. An efficient attention-based neural network architecture has been studied to combine precise performances with real-time inference. The proposed solution shows remarkable results compared to a standard neural network and filter-based odometry correction algorithms. Nonetheless, the online learning paradigm avoids the time-consuming data collection procedure and can be adopted on a generic robotic platform on-the-fly