Hospital logistics can significantly benefit from autonomous robotic technologies capable of alleviating personnel workload, reducing operational costs, and streamlining internal workflows. This paper introduces HOSBOT (HOSpital roBOT), a modular and cost-effective robotic system designed to enhance hospital logistics and workflow management through autonomous missions and real-time monitoring of transported items. HOSBOT combines a commercially available autonomous mobile robot with a customisable non-motorised cart, the SmartRack, which accommodates standalone, sensorised SmartBoxes equipped with Radio Frequency IDentification technology. This paper describes the overall design of HOSBOT, including its mechanical, electrical, and software interfaces. The system has been validated in three pilot experiments in real hospital environments across Europe, demonstrating good levels of acceptability, usability and efficacy. This innovative robotic solution highlights the potential for scalable integration of autonomous mobile robots in healthcare and other indoor logistics settings.

Gut microbiome research has made tremendous progress, especially with the integration of machine learning and artificial intelligence that can provide new insights from complex microbiome data and its impact on human health. The use of explainable artificial intelligence is becoming critical in medicine and adopting it in precision medicine-models leveraging gut microbiome data is appealing for providing more transparency and trustworthiness in clinical research. This scoping review evaluates the use of machine learning and explainable artificial intelligence techniques and identifies existing gaps in knowledge in this research area to suggest future research directions. Online databases (PubMed and Scopus) were searched to retrieve papers published between 2018-2024, and from which we selected 76 publications. Different clinical applications of machine learning and artificial intelligence techniques in gut microbiome studies were explored in the reviewed articles. We observed a high prevalence in the use of black box models in the field, with Random Forest being the most used algorithm. The explainability remains somewhat limited in the field, but it appears to be improving. Researchers showed interest in SHAP applications as an explainable technique. Finally, not enough attention was paid to the reproducibility of the research work published. This review highlights opportunities for advancing research on explainable artificial intelligence models in the field of microbiome, supporting future applications of microbiome-based precision medicine.

Soft robots show compliance and have infinite degrees of freedom. Thanks to these properties, such robots can be leveraged for surgery, rehabilitation, biomimetics, unstructured environment exploring, and industrial grippers. In this case, they attract scholars from a variety of areas. However, nonlinearity and hysteresis effects also bring a burden to robot modeling. Moreover, following their flexibility and adaptation, soft robot control is more challenging than rigid robot control. In order to model and control soft robots, a large number of data-driven methods are utilized in pairs or separately. This review first briefly introduces two foundations for data-driven approaches, which are physical models and the Jacobian matrix, then summarizes three kinds of data-driven approaches, which are statistical method, neural network, and reinforcement learning. This review compares the modeling and controller features, e.g., model dynamics, data requirement, and target task, within and among these categories. Finally, we summarize the features of each method. A discussion about the advantages and limitations of the existing modeling and control approaches is presented, and we forecast the future of data-driven approaches in soft robots. A website (https://sites.google.com/view/23zcb) is built for this review and will be updated frequently.Note to Practitioners—This work is motivated by the need for a review introducing soft robot modeling and control methods in parallel. Modeling and control play significant roles in robot research, and they are challenging especially for soft robots. The nonlinear and complex deformation of such robots necessitates specific modeling and control approaches. We introduce the state-of-the-art data-driven methods and survey three approaches widely utilized. This review also compares the performance of these methods, considering some important features like data amount requirement, control frequency, and target task. The features of each approach are summarized, and we discuss the possible future of this area.

Screening of the lower gastrointestinal (GI) tract is of paramount importance for the early detection of precancerous lesions in the intestine, with an impact on reducing the high death rate of patients affected by cancer worldwide. Colonoscopy, i.e. standard procedure for screening the colon, is effective in reducing the incidence of colorectal cancer worldwide, nonetheless, this procedure remains an invasive method of screening, that typically causes discomfort and requires sedation for the patient. The SoftSCREEN system, a tethered robotic capsule designed for colonoscopy, aims to enable minimally invasive diagnosis of intestinal diseases through its innovative design that incorporates elastic tracks for locomotion and inflatable toroidal chambers for adaptable geometry to match the local lumen of the GI tract. After demonstrating the viability of the proposed design in a large-scale proof of concept in our previous work, the authors present here a miniaturised version of the SoftSCREEN system. We assess its performance in multiple phantom tests and evaluate the effect of pressure regulation on its locomotion. The conducted extensive tests demonstrate the capability of the soft robot to move inside intricate passages, capture internal images, and adjust its geometry to optimise traction. The results underscore the potential of the proposed design, offering promising advancements in the development of a robotic platform for efficient front-wheel locomotion and accurate intestinal screening.

Skin cancer represents a significant global health concern, with melanoma alone accounting for thousands of deaths annually. Early diagnosis is crucial for improving survival rates and reducing healthcare costs. While traditional diagnostic approaches involve visual inspection followed by biopsy, emerging technologies offer less invasive options with improved precision. In this study, a novel non-invasive device was designed, developed, and validated to employ near-infrared reflectance spectroscopy for skin lesion analysis. Furthermore, this work presents a machine learning approach aimed at classifying different types of skin lesions, as well as a new sequential approach to distinguish benign from malignant lesions based on spectral data and exploring the impact of anamnestic features. The device was used in two independent hospitals in Italy to collect data from 69 patients in total, including various types of skin lesions, all of whom followed the standard protocol for screening and diagnosis intervention. The implemented model achieved a recall of 93.8% and an accuracy of 75% for melanoma and benign classification, and a recall of 100% and an accuracy of 98.6% in distinguishing non-melanoma cancer from benign lesions, demonstrating promising results for skin cancer diagnosis utilizing spectral and anamnestic data. In summary, this study contributes to the development of allied non-invasive diagnostic tools and underscores the potential of machine learning in dermatology using spectroscopic data.

Flexible Endoscopes (FEs) for colonoscopy present several limitations due to their inherent complexity, resulting in patient discomfort and lack of intuitiveness for clinicians. Robotic FEs with autonomous control represent a viable solution to reduce the workload of endoscopists and the training time while improving the procedure outcome. Prior works on autonomous endoscope FE control use heuristic policies that limit their generalisation to the unstructured and highly deformable colon environment and require frequent human intervention. This work proposes an image-based FE control using Deep Reinforcement Learning, called Deep Visuomotor Control (DVC), to exhibit adaptive behaviour in convoluted sections of the colon. DVC learns a mapping between the images and the FE control signal. A first user study of 20 expert gastrointestinal endoscopists was carried out to compare their navigation performance with DVC using a realistic virtual simulator. The results indicate that DVC shows equivalent performance on several assessment parameters, being more safer. Moreover, a second user study with 20 novice users was performed to demonstrate easier human supervision compared to a state-of-the-art heuristic control policy. Seamless supervision of colonoscopy procedures would enable endoscopists to focus on the medical decision rather than on the control of FE.

Quality control in industry involves trained operators to manipulate and inspect metallic surfaces in order to identify, and eventually correct, manufacturing defects. These tasks are manually performed, and a poor performance (e.g., missing defects) leads to an increase of the costs and prolongation of the manufacturing time cycle. In this work, we propose a multi-agent robotic platform to autonomously perform Industry 4.0 quality control processes of metallic surfaces. The platform consists of three anthropomorphic robots with custom-made end-effectors designed to manipulate, inspect, and eventually correct a metallic frame of a motorcycle. The description of a novel multi-agent platform is followed by the presentation of the developed inspection procedure, in which a linear laser scanner is used to reconstruct the three-dimensional metallic surface of a motorcycle with a resolution of ~0.1 mm. In order to validate the platform, we perform a set of experiments to assess the performance of the robotic platform in a real Industry 4.0 scenario. Results confirmed that such a system guarantees a sub-millimetric precision to identify defects on complex-shaped metallic surfaces and effectively correct them. The proposed robotic platform can be adopted for overcoming the drawbacks of a traditional procedure that relies on visual-tactile manual defects correction (e.g., low-repeatability, high-subjectivity) and is scalable to different industrial applications. The proposed approach aims to elevate the role of operators to expert supervisors of the process, limiting the interactions with potentially-dangerous tools/procedures and thus improving the working conditions in an industrial 4.0 scenario.Note to Practitioners—This work was motivated by a crucial need in industry,i.e.to automatize the manufacturing quality control, translating the commonly-used visual-based manual approach performed by operators to an objective robotic one that relies on defect detection by using a linear laser scanner. A novel multi-agent robotic platform, developed by the authors, showed its effectiveness in automatizing complex tasks, in which huge workspace and different tools are required. The aim of the paper was to develop a fully automatized application that covers the entire quality control process, while focusing on one specific phase,i.e.automatic inspection. The integrated software and the infrastructural communication protocols of the entire robotic platform were designed to be flexible in order to realize a new reference for industrial applications, where a multi-agent approach is demanded. The experimental validation focused on a specific use case (a motorcycle frame selected due to its complex structure,i.e.multiple curvatures with variable radii, and large volumes), but the proposed platform and the implemented methodology have to be intended as a general purpose approach, adaptable to any industrial process and mechanical component. The authors, starting by a laboratory development with extensive tests, applied and demonstrated the feasibility of the proposed approach in a real Industry 4.0 scenario.

Foodservice workers perform several burdensome, tedious, and unsafe tasks that risk their health and well-being. This could be mitigated or even more avoided by using autonomously-actuated machines. Therefore, this article aims to build the foundation to support the development of a new field of robotics research dedicated to foodservice and with a human/worker-centered framework. As so, we introduce a two-level taxonomy of basic actions that compose the physical tasks of foodservice workers; it can guide future studies to design bio-inspired control models for foodservice robots. Actions are clustered in 16 categories according to their purpose and to the handled food. Furthermore, authors make a critical review of single-action equipment (SAE) and advanced equipment (AE) currently available for foodservice, which allowed us to identify opportunities for research. As a result, authors found some categories of actions rarely automated, aimed at i) separating solid-solid food parts, ii) moving food between workstations or independent appliances in the kitchen, iii) introducing food into another solid food or recipient, and iv) other specific actions, e.g. trussing food. In addition, authors discuss the applicability of collaborative robotics and human-robot collaboration to different contexts in foodservice, and show how artificial intelligence is improving the capabilities of SAE and AE and what else it could improve in this context.Note to Practitioners—This paper was motivated by a critical need in foodservice: the ability to produce consistent and high-quality meals ad-hoc, without overloading the workers or harming their health. Robotic and autonomous systems are promising technologies to solve this. However, there is not a unified framework in robotics research focused on the professional foodservice environment. This paper provides two tools for researchers and engineers in this field: (i) a taxonomy of basic actions that foodservice workers perform during their physical tasks; and (ii) a systematic review of mechatronic systems being developed or already in use in foodservice. The taxonomy can be immediately useful to divide research and development by the classes of actions. In addition, we found specific categories of actions that have been rarely automated so far and need further investigation. The results of our review can be readily applied in industry, too: presently, most equipment is a custom-built machine with limited adaptiveness; when systems include industrial robots, cobots are being preferred; the implementation of collaborative operations between humans and robots is not common yet and its applicability may be suitable only for certain contexts; finally, we identify scientific publications introducing adaptive control strategies and movement policies for some actions that can be implemented today to achieve a more robust actuation.

To improve decision-making strategies and prediction based on epidemiological data, so far biased by highly-variable criteria, algorithms using unbiased morbidity parameters, i.e. Intensive Care Units (ICU) and Ordinary Hospitalizations (OH), are proposed. ICU/OH acceleration and velocities are mathematically modeled using available and official data to derive two thresholds, alerting on 30 % ICU and 40 % OH of COVID-19 daily occupancy settled by the Italian Minister of Health, as a case of study. A predictive model is also proposed to estimate the daily occupancy of ICU and OH in hospitals for each region, using a Susceptible-Infected-Recovered-Death (SIRD) epidemic model to further extend occupancy prediction in each regional district. Computed data validated the proposed models in Italy after almost two years of pandemic, obtaining agreements with the Italian Presidential Decree regardless of the different regional trends of epidemic waves. Therefore, the decision-making algorithm and prediction model resulted valuable tools, retrospectively, to be tested prospectively in sustainable strategies to curb the impact of COVID-19, or of any other pandemic threats with any aggregate of data, on local healthcare systems.

Minimally invasive wireless devices, allowing the sampling of gut’s bacteria, are needed for a longitudinal understanding of the role of the microbiota on the human health. Herein, we present a novel magnetic actuation system fitting inside a 11.5 × 30.5 mm wireless ingestible capsule. Lacking any electronic components, the capsule robot is designed for the collection of microbiota’s samples through mechanical brushing. Wireless activation and in situ sampling are enabled by an external permanent magnetic source. This component, when approaching the capsule, progressively allows: (1) the adhesion of the device to the mucosa, (2 the exposure of the brushes, and (3) the sampling by multiple rotations. Numerical and analytical models were developed for dimensioning the system, and were validated by benchtop experiments.

The study of cooking tasks, such as grilling, is hindered by several adverse conditions for sensors, such as the proximity to humidity, fat, and heat. Still, robotics research could benefit from understanding the human control of forces and torques in important contact interactions of kitchen-utensils with food. This work presents a preliminary study on the dynamics of grilling tasks (i.e. food flipping movements). A spatula and kitchen-tweezers were instrumented to measure forces and torque in multiple directions. Furthermore, we designed an experimental setup to keep sensors distant from heat/humidity and to, simultaneously, hold the effects of grilling (stickiness/slipperiness) during the tasks execution and recording. This allowed a successful data collection of 1426 movements with the spatula (flipping hamburgers, chicken, zucchini and eggplant slices) and 660 movements with the tweezers (flipping zucchini and eggplant slices), performed by chefs and ordinary home cooks. Finally, we analyzed three dynamical characteristics of the tasks for the different food: bending force and torsion torque on the impact to unstick food, and maximum pinching with tweezers. We verified that bending on impact and maximum pinching are adjusted to the food by both chefs and home cooks.

Robotic endoscopic systems offer a minimally invasive approach to the examination of internal body structures, and their application is rapidly extending to cover the increasing needs for accurate therapeutic interventions. In this context, it is essential for such systems to be able to perform measurements, such as measuring the distance traveled by a wireless capsule endoscope, so as to determine the location of a lesion in the gastrointestinal tract, or to measure the size of lesions for diagnostic purposes. In this paper, we investigate the feasibility of performing contactless measurements using a computer vision approach based on neural networks. The proposed system integrates a deep convolutional image registration approach and a multilayer feed-forward neural network into a novel architecture. The main advantage of this system, with respect to the state-of-the-art ones, is that it is more generic in the sense that it is 1) unconstrained by specific models, 2) more robust to nonrigid deformations, and 3) adaptable to most of the endoscopic systems and environment, while enabling measurements of enhanced accuracy. The performance of this system is evaluated under ex vivo conditions using a phantom experimental model and a robotically assisted test bench. The results obtained promise a wider applicability and impact in endoscopy in the era of big data.

Various modalities are used for the examination of the gastrointestinal (GI) tract. One such modality is Wireless Capsule Endoscopy (WCE), a noninvasive technique which consists of a swallowable color camera that enables the detection of GI pathology with only minimal patient discomfort. Currently, tracking of the capsule position is estimated in the 3D abdominal space, using radio-frequency (RF) triangulation. The RF triangulation technique, however, does not provide sufficient information about the location of the capsule along the GI lumen, and consequently, the localization of any possible abnormality. Recently, we proposed a geometric visual odometry (VO) method for the localization of the capsule in the GI lumen. In this paper, we extend this state-of-art method by exploiting an artificial neural network (ANN) to augment the geometric method and achieve higher localization accuracy. The results of this novel approach are validated with an in-vitro experiment that provides ground truth information about the location of the capsule. The mean absolute error obtained, for a distance of 19.6cm, is 0.79±0.51cm.

Supervised robotic platforms, able to perform a non-invasive therapy or minimal invasive surgery, represent one of the main achievements in recent years. Robotic-assisted medical procedures with medical doctor, patient and medical assistants interacting with a robotic platform can be seen as a paradigmatic example of the coexistence between system autonomy and human action in medicine. However, this can involve unpredicted and dangerous contacts between robotic structures and humans, contacts that have to be managed with appropriate safety strategies, often embedding specific sensitive components into the robot itself. In this paper, a smart sensorized polymeric skin based on textile multi-touch piezoresistive sensors, able to sense and safely manage pressure exerted during a collision with the surrounding environment (e.g., humans), has been designed, fabricated, integrated on a robotic manipulator and tested. The proposed system shows promising results in managing the pressure exerted during the collision, with a close correlation with the analytical analysis (difference lower than 5.6 kPa - error of 9%).

Wireless capsule endoscopy offers significant advantages compared with traditional endoscopic procedures, since it limits the invasiveness of gastrointestinal tract screening and diagnosis. Moreover, active locomotion devices would allow endoscopy to be performed in a totally controlled manner, avoiding failures in the correct visualization of pathologies. Previous works demonstrated that magnetic locomotion through a robotic-aided platform would allow us to reach this goal reliably. In this paper, the authors present a comparative evaluation of control methodologies and user interfaces for a robotic-aided magnetic platform for capsule endoscopy, controlled through human-robot cooperative and teleoperated control algorithms. A detailed statistical analysis of significant control parameters was performed: teleoperated control is the more reliable control approach, and a serial kinematic haptic device results as the most suitable control interface to perform effective robotic-aided endoscopic procedures.