ABSTRACT
Over the years, the robotic industry has made significant growth in the manufacturing sector due to the need for collaborative and interactive robots. But it is not the case for service sectors, especially in the healthcare sector. A lack of emphasis is given to healthcare which has led to new opportunities for developing assistive robots which can aid patients with disabilities and illnesses. Furthermore, COVID-19 has acted as a catalyst for the development of assistive robots in the healthcare sector in an attempt to overcome the difficulties faced due to viruses and bacteria. This paper demonstrates the simulation of a multi-purpose medical assistive robot using ROS(Robot Operating System). This intelligent robot is successfully simulated and visualized in the ROS environment. To achieve real-Time autonomous motion Google Cartographer SLAM(Simultaneous Localization And Mapping) is used to generate real-Time maps of unknown environments. It usually focuses on how these robots can provide assistance to health workers, customers, and organizations in different sectors of the healthcare environment. © 2023 IEEE.
ABSTRACT
The outbreak of the Covid-19 epidemic has devastated the generation and impacted multiple layers of the healthcare sector. Resulting from this kind of exceptionally contagious virus and a shortfall of medical workers in the hospitals, front-line health workers, and patients are at risk. Thus, with an aim to diminish the risk of infections, a mobile robotic system is proposed that can autonomously ensure safety and protection in the hospital. The system can monitor the patients by moving autonomously and sanitizing the floor throughout the hospital, which is implemented by Robot Operating System (ROS), SLAM (Simultaneous Localization and Mapping) algorithm, and A∗ search algorithm, and then it uses the MobileNetV2 algorithm for safety mask detection and giving voice alert. The system also offers AI voice communication to assist and diagnose the patients, which can lessen person-to-person contact. The system has anticipated 89% accuracy for AI custom dataset, whereas the validation accuracy for face mask detection is 95%. © 2022 IEEE.
ABSTRACT
Solid waste management is one of the critical challenges seen everywhere, and the coronavirus disease (COVID-19) pandemic has only worsened the problems in the safe disposal of infectious waste. This paper outlines a design for a mobile robot that will intelligently identify, grasp, and collect a group of medical waste items using a six-degree of freedom (DoF) arm, You Only Look Once (YOLO) neural network, and a grasping algorithm. Various designs are generated before running simulations on the selected virtual model using Robot Operating System (ROS) and Gazebo simulator. A lidar sensor is also used to map the robot's surroundings and navigate autonomously. The robot has good scope for waste collection in medical facilities, where it can help create a safer environment.
ABSTRACT
Solid waste management is one of the critical challenges seen everywhere, and the coronavirus disease (COVID-19) pandemic has only worsened the problems in the safe disposal of infectious waste. This paper outlines a design for a mobile robot that will intelligently identify, grasp, and collect a group of medical waste items using a six-degree of freedom (DoF) arm, You Only Look Once (YOLO) neural network, and a grasping algorithm. Various designs are generated before running simulations on the selected virtual model using Robot Operating System (ROS) and Gazebo simulator. A lidar sensor is also used to map the robot's surroundings and navigate autonomously. The robot has good scope for waste collection in medical facilities, where it can help create a safer environment.
ABSTRACT
Advanced robotics does not always have to be associated with Industry 4.0, but can also be applied, for example, in the Smart Hospital concept. Developments in this field have been driven by the coronavirus disease (COVID-19), and any improvement in the work of medical staff is welcome. In this paper, an experimental robotic platform was designed and implemented whose main function is the swabbing samples from the nasal vestibule. The robotic platform represents a complete integration of software and hardware, where the operator has access to a web-based application and can control a number of functions. The increased safety and collaborative approach cannot be overlooked. The result of this work is a functional prototype of the robotic platform that can be further extended, for example, by using alternative technologies, extending patient safety, or clinical tests and studies. Code is available at https://github.com/ Steigner/ Robo_ Medicinae_ I. © 2022, Brno University of Technology. All rights reserved.
ABSTRACT
In recent years, engineering degree programs have become fundamental to the teaching of robotics and incorporate many fundamental STEM concepts. Some authors have proposed different platforms for teaching different topics related to robotics, but most of these platforms are not practical for classroom use. In the case of teaching autonomous navigation algorithms, the absence of platforms in classrooms limits learning because students are unable to perform practice activities or cannot evaluate and compare different navigation algorithms. The main contribution of this study is the implementation of a free platform for teaching autonomous-driving algorithms based on the Robot Operating System without the use of a physical robot. The authors present a case study using this platform as a teaching tool for instruction in two undergraduate robotic courses. Students evaluated the platform quantitatively and qualitatively. Our study demonstrates that professors and students can carry out different tests and compare different navigation algorithms to analyze their performance under the same conditions in class. In addition, the proposed platform provides realistic representations of environments and data visualizations. The results claim that the use of simulations helps students better understand the theoretical concepts, motivates them to pay attention, and increases their confidence.
ABSTRACT
The outbreak of the Covid-19 pandemic has resulted in a surge in the generation of medical waste. Due to the transmissible nature of the Virus and the lack of effort at proper disposal, the safety of the front-line health workers, as well as the disposer, is at risk. Hence, to mitigate the spread of infectious diseases, a system is proposed that uses a robotic arm for segregating medical waste automatically. The robotic arm is operable through voice commands, and the segregating operation could function in automatic and manual mode. The system uses the YOLOv3 (You Only Look Once) algorithm to detect and classify the medical waste and then uses the Robot Operating System (ROS) platform to pick up and drop the waste object into color-coded bins. For this research, the medical waste has been categorized into 4 types, and for each type, a color-coded bin has been used for segregation. Our system has achieved 94% training accuracy for the YOLOv3 model on a custom dataset, whereas the system's overall accuracy in automated mode was 82.1%, derived after 30 trials. In the case of manual mode, an average accuracy of 82.5% has been achieved for the same number of trials. © 2022 IEEE.
ABSTRACT
This paper describes the design, implementation, and testing of control and vision algorithms for an AUV in virtual and real environments. Hardware design and the software stack of the vec6 underwater vehicle are described in this paper. The paper also presents a simulation test-bed, the uwv-simulator, which is developed using ROS and Gazebo. A custom arena similar to that used in the Singapore AUV Challenge is constructed in the simulation environment. One of the main motivations to develop the open-source simulation test-bed is to provide easy integration and testing of different cases and algorithms for autonomy. The software stack is designed to execute higher and algorithms without the trouble of going through the lower-level functions. © 2021 MTS.
ABSTRACT
This paper presents a design of an autonomous mobile robot system, primarily focuses on hospital environments, which focuses on disinfecting the hospital's isolation ward. This paper also primarily concentrates on making this system cost efficient with minimalistic sensors. This system consists of an advance autonomous indoor navigating mobile robot and a ground station, LIDAR, an UVC disinfectant lamp, encoder motors, IMU, etc. and uses ROS to navigate in indoor autonomously, the hospital environment is a busy environment so this paper equally concentrates on dynamically environment navigation, path planning based on global planner algorithms, localisation and navigation is done using particle filter, dwa_local_planner, navfn. The autonomous mobile robot system has been tested and simulated whose results are reliable and efficient. © 2021 IEEE.
ABSTRACT
Due to COVID-19 pandemic, there is an increasing demand for mobile robots to substitute human in disinfection tasks. New generations of disinfection robots could be developed to navigate in high-risk, high-touch areas. Public spaces, such as airports, schools, malls, hospitals, workplaces and factories could benefit from robotic disinfection in terms of task accuracy, cost, and execution time. The aim of this work is to integrate and analyse the performance of Particle Swarm Optimization (PSO) algorithm, as global path planner, coupled with Dynamic Window Approach (DWA) for reactive collision avoidance using a ROS-based software prototyping tool. This paper introduces our solution - a SLAM (Simultaneous Localization and Mapping) and optimal path planning-based approach for performing autonomous indoor disinfection work. This ROS-based solution could be easily transferred to different hardware platforms to substitute human to conduct disinfection work in different real contaminated environments.