112 research outputs found

    Medical Treatment of Muscle Lesion

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    Etiology, Biology and Treatment of Muscular Lesions

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

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    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

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    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

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

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    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

    Marvin: an Innovative Omni-Directional Robotic Assistant for Domestic Environments

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    Population ageing and pandemics recently demonstrate to cause isolation of elderly people in their houses, generating the need for a reliable assistive figure. Robotic assistants are the new frontier of innovation for domestic welfare, and elderly monitoring is one of the services a robot can handle for collective well-being. Despite these emerging needs, in the actual landscape of robotic assistants there are no platform which successfully combines a reliable mobility in cluttered domestic spaces, with lightweight and offline Artificial Intelligence (AI) solutions for perception and interaction. In this work, we present Marvin, a novel assistive robotic platform we developed with a modular layer-based architecture, merging a flexible mechanical design with cutting-edge AI for perception and vocal control. We focus the design of Marvin on three target service functions: monitoring of elderly and reduced-mobility subjects, remote presence and connectivity, and night assistance. Compared to previous works, we propose a tiny omnidirectional platform, which enables agile mobility and effective obstacle avoidance. Moreover, we design a controllable positioning device, which easily allows the user to access the interface for connectivity and extends the visual range of the camera sensor. Nonetheless, we delicately consider the privacy issues arising from private data collection on cloud services, a critical aspect of commercial AI-based assistants. To this end, we demonstrate how lightweight deep learning solutions for visual perception and vocal command can be adopted, completely running offline on the embedded hardware of the robot.Comment: 20 pages, 9 figures, 3 tabl

    The Marvin Project: an Omni-Directional Robot for Home Assistance

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    In the last decades, many researchers are investigating how robotic solutions may be adopted to address the increasing need for home and personal assistance aggravated by current global challenges, e.g. population ageing and pandemic emergency. In this direction, the researchers at Politecnico di Torino, together with the colleagues from Edison S.p.A., developed the Marvin project which aims at designing a useful mobile robot for the domestic environment. In this work, the main features of the Marvin prototype and a first qualitative experimental validation are presented

    Enhancing Navigation Benchmarking and Perception Data Generation for Row-based Crops in Simulation

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    Service robotics is recently enhancing precision agriculture enabling many automated processes based on efficient autonomous navigation solutions. However, data generation and infield validation campaigns hinder the progress of large-scale autonomous platforms. Simulated environments and deep visual perception are spreading as successful tools to speed up the development of robust navigation with low-cost RGB-D cameras. In this context, the contribution of this work is twofold: a synthetic dataset to train deep semantic segmentation networks together with a collection of virtual scenarios for a fast evaluation of navigation algorithms. Moreover, an automatic parametric approach is developed to explore different field geometries and features. The simulation framework and the dataset have been evaluated by training a deep segmentation network on different crops and benchmarking the resulting navigation.Comment: Accepted at the 14th European Conference on Precision Agriculture (ECPA) 202

    Injury prevention strategies at the FIFA 2014 World Cup: perceptions and practices of the physicians from the 32 participating national teams

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    Purpose The available scientific research regarding injury prevention practices in international football is sparse. The purpose of this study was to quantify current practice with regard to (1) injury prevention of top-level footballers competing in an international tournament, and (2) determine the main challenges and issues faced by practitioners in these national teams. Methods A survey was administered to physicians of the 32 competing national teams at the FIFA 2014 World Cup. The survey included 4 sections regarding perceptions and practices concerning non-contact injuries: (1) risk factors, (2) screening tests and monitoring tools, (3) preventative strategies and (4) reflection on their experience at the World Cup. Results Following responses from all teams (100%), the present study revealed the most important intrinsic (previous injury, accumulated fatigue, agonist:antagonist muscle imbalance) and extrinsic (reduced recovery time, training load prior to and during World Cup, congested fixtures) risk factors during the FIFA 2014 World Cup. The 5 most commonly used tests for risk factors were: flexibility, fitness, joint mobility, balance and strength; monitoring tools commonly used were: medical screen, minutes/matches played, subjective and objective wellness, heart rate and biochemical markers. The 5 most important preventative exercises were: flexibility, core, combined contractions, balance and eccentric. Conclusions The present study showed that many of the National football (soccer) teams’ injury prevention perceptions and practices follow a coherent approach. There remains, however, a lack of consistent research findings to support some of these perceptions and practices
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