ABSTRACT
At present, due to the COVID-19, China's social and economic development has slowed down. Some life service e-commerce platforms have successively launched "contactless delivery" services, which can effectively curb the spread of the epidemic. Robot distribution is the current mainstream, but robots are different from people and need to have accurate program settings. Both path planning and obstacle avoidance are currently top issues. This requires the mobile robot to successfully arrive at the destination while minimizing the impact on the surrounding environment and pedestrians, and avoiding encroachment on the movement space of pedestrians. Therefore, the mobile robot needs to be able to actively avoid moving pedestrians in a dynamic environment, in addition to avoiding static obstacles, and safely and efficiently integrate into the pedestrian movement environment. In this paper, the path planning problem of unmanned delivery robot is studied, and the path of mobile robot in the crowd is determined by global planning and local planning, and the matlab simulation is used for verification. © The Authors. Published under a Creative Commons Attribution CC-BY 3.0 License.
ABSTRACT
With the upgrading and innovation of the logistics industry, the requirements for the level of transportation smart technologies continue to increase. The outbreak of the COVID-19 has further promoted the development of unmanned transportation machines. Aimed at the requirements of intelligent following and automatic obstacle avoidance of mobile robots in dynamic and complex environments, this paper uses machine vision to realize the visual perception function, and studies the real-time path planning of robots in complicated environment. And this paper proposes the Dijkstra-ant colony optimization (ACO) fusion algorithm, the environment model is established by the link viewable method, the Dijkstra algorithm plans the initial path. The introduction of immune operators improves the ant colony algorithm to optimize the initial path. Finally, the simulation experiment proves that the fusion algorithm has good reliability in a dynamic environment. © 2022 IEEE.
ABSTRACT
In recent years, the novel corona virus pandemic is raging around the world, and the safety of home environment and public environment has become the focus of people's attention [2]. Therefore, the research on disinfection robot has become one of the important directions in the field of machinery and artificial intelligence. This paper proposes a robot with the STM32 MCU as the core of disinfection, and is equipped with a variety of sensors and a camera vision, has the original cloud service management platform, the remote deployment of navigation, based on visual SLAM to realize high precision navigation and positioning, can realize to indoor environment autonomously route planning, automatic obstacle avoidance checking, disinfection, epidemic prevention function, at the same time can pass Bit computer software realizes remote control of robot, which has great development potential. © 2022 ACM.
ABSTRACT
In view of the fact that Covid-19 is highly contagious, which poses great threat and inconvenience to people's production and life, a multifunctional robot control system with single-chip microcomputer as the control core is designed, aiming at the problems of centralized isolation points in communities, complicated situation and difficult management. Firstly, Gmapping algorithm is used to realize the robot's autonomous positioning and avoidance. Secondly, a three-degree-of-freedom robot arm is designed to disinfect any indoor space. Finally, Gmapping algorithm is used to recognize and measure the temperature of human face. Through the simulation experiment, this method can improve the efficiency of searching the shortest path and carry out disinfection work while reducing human contact, improving public safety and has practical value. © 2023 IEEE.
ABSTRACT
The entire world witnessed the covid-19pandemicinthe year 2020. The actual outbreak of this corona virus was first reported in Wuhan, China and later declared to be epidemic by (WHO) World Health Organization. The whole world was under tremendous pressure in monitoring health, managing, and maintaining hospitals and inventing new drugs. Initially, India was very much worried because of the huge population. The pandemic posed a critical challenge for healthcare sectors, since doctors and nursing professionals were among the most severely affected and it's clear that India must adopt new measures to increase healthcare proportional ratio and adoption of new technologies to manage large population groups. Robotics is one area which may largely always support the segment. The proposed research project emphasized on developing robotic devices with robotic vision, sensors-based motion planning, dynamic obstacle detection, and autonomous navigation in a hospital environment and supported the medical and nursing teams in reducing their workload and improving patient health monitoring, also the research explored multi-robot exploration and integration. © 2022 IEEE.
ABSTRACT
After the occurrence of the COVID-19, preventing cross infection has become a top priority. Therefore, it is proposed to use robots to replace people to distribute anti epidemic materials, so as to reduce human contact. By planning the trajectory of the robot in advance, and using mechanical arms and claws to achieve accurate grasp and delivery of anti epidemic materials, it can carry out material distribution in the isolated inpatient department, and can independently locate and deliver products, goods, etc. in a complex environment. It has strong cargo carrying capacity, and has the dual functions of traditional delivery robots and indoor delivery services. Its use can greatly reduce the infection rate in the epidemic and deliver materials in time. © 2022 IEEE.
ABSTRACT
After the new coronavirus has undergone multiple mutations, its infectivity and severity have greatly increased, which has caused great threats and inconvenience to people's production and life. In order to disinfect the isolated area comprehensively, a control system of disinfection robot for epidemic prevention and control is designed. The robot takes STM32 as the main controller, collects and analyses the environmental information by lidar EKF-SLAM. In addition, Improved Ant Colony Algorithm is used for optimal path planning, and 3-DOF robotic arm is carried out to sanitize the designated area. The system can achieve the functions such as mapping, real-time localization, robot distribution and disinfection. The feasibility and superiority of the 3D reconstruction, path planning algorithm and end-effector pose control method are verified by MATLAB simulation. It can reduce the contact frequency of the crowd and the workload of the disinfection staff, and making contributions to epidemic prevention and control further. © 2022 IEEE.
ABSTRACT
The COVID-19 pandemic shows growing demand of robots to replace humans for conducting multiple tasks including logistics, patient care, and disinfection in contaminated areas. In this paper, a new autonomous disinfection robot is proposed based on aerosolized hydrogen peroxide disinfection method. Its unique feature lies in that the autonomous navigation is planned by developing an atomization disinfection model and a target detection algorithm, which enables cost-effective, point-of-care, and full-coverage disinfection of the air and surface in indoor environment. A prototype robot has been fabricated for experimental study. The effectiveness of the proposed concept design for automated indoor environmental disinfection has been verified with air and surface quality monitoring provided by a qualified third-party testing agency. Note to Practitioners - Robots are desirable to reduce the risk of human infection of highly contagious virus. For such purpose, a novel autonomous disinfection robot is designed herein for automated disinfection of air and surface in indoor environment. The robot structure consists of a mobile carrier platform and an atomizer disinfection module. The disinfection modeling is conducted by using the measurement data provided by a custom-built PM sensor array. To achieve cost-effective and qualified disinfection, a full-coverage path planning scheme is proposed based on the established disinfection model. Moreover, for specifically disinfecting the frequently contacted objects (e.g., tables and chairs in offices and hospitals), a target perception algorithm is proposed to mark the localization of these objects in the map, which are disinfected by the robot more carefully in these marked areas. Experimental results indicate that the developed disinfection robot offers great effectiveness to fight against the COVID-19 pandemic. © 2004-2012 IEEE.
ABSTRACT
Industrial automation has become increasingly more prominent in many industries, such as manufacturing, automotive, pharmaceuticals, and food processing industries, as the technology evolves and Industry 4.0 revolution advances. Th demand of automation and personnel with automation skills has ever been increasing since Covid-19 Pandemic. Industrial robots and machine vision inspection are essential systems for manufacturing automation. Industrial robots are capable of performing various tasks like part handling, machine tending, assembly, palletizing, arc welding, or laser cutting with high speeds, repeatability and accuracy. Machine Vision Inspection (MVI) systems are used for part quality inspection, manufacturing and assembly supervision and robot guidance. A MVI system integrated with an industrial robot provides a hand-eye coordination to the robot for flexible material handling and operations. Vision-guided robotics serves as the next-generation research instrument that opens new opportunities to advance the boundaries in science and engineering research. This paper focuses on teaching industrial robot programming to engineering students using an offline virtual robotic simulation software, Fanuc ROBOGUIDE and iRVision software. Using a virtual robot and offline programming with ROBOGUIDE reduces a risk by enabling visualization of the robot operations before an actual installation and operations. The ROBOGUIDE software will provide students with an experience of programming an industrial robot and will enhance the effectiveness of the teaching and learning process. The developed programs can be imported and implemented onto a real robot with a minimum configuration setup. The step by step approach of developing and programming a 2D vision guided material handling cell using ROBOGUIDE has been discussed in the paper such that other educators and students can learn and implement the project with ease. Copyright 2022. © by the International Institute of Informatics and Systemics. All rights reserved.
ABSTRACT
Disinfection robots have applications in promoting public health and reducing hospital acquired infections and have drawn considerable interest due to the COVID-19 pan-demic. To disinfect a room quickly, motion planning can be used to plan robot disinfection trajectories on a reconstructed 3D map of the room's surfaces. However, existing approaches discard semantic information of the room and, thus, take a long time to perform thorough disinfection. Human cleaners, on the other hand, disinfect rooms more efficiently by prioritizing the cleaning of high-touch surfaces. To address this gap, we present a novel GPU-based volumetric semantic TSDF (Truncated Signed Distance Function) integration system for semantic 3D reconstruction. Our system produces 3D reconstructions that distinguish high-touch surfaces from non-high-touch surfaces at approximately 50 frames per second on a consumer-grade GPU, which is approximately 5 times faster than existing CPU-based TSDF semantic reconstruction methods. In addition, we extend a UV disinfection motion planning algorithm to incorporate semantic awareness for optimizing coverage of disinfection tra-jectories. Experiments show that our semantic-aware planning outperforms geometry-only planning by disinfecting up to 20% more high-touch surfaces under the same time budget. Further, the real-time nature of our semantic reconstruction pipeline enables future work on simultaneous disinfection and mapping. Code is available at: https://github.com/uiuc-iml/RA-SLAM © 2022 IEEE.
ABSTRACT
Since 2019, the COVID-19 virus has spread worldwide, posing a significant health and safety concern. The application of mobile robots in the medical field has gradually demonstrated their unique advantages. Therefore, we focus on the application of mobile robots inwards. By collating and summarizing some of the most popular existing path planning algorithms, this paper illustrates that different algorithms can produce varying outcomes depending on different environments and hardware used. MATLAB is used in this study to simulate four algorithms: To determine the most efficient path, A∗, RRT, RRT∗, and PRM in a specific hospital map are compared, as well as parameters including path length, average execution time, and resource consumption. Modelling a single-layer hospital map makes it possible for mobile robots in the medical field to execute tasks more efficiently between entry and ward in the COVID-19 hospital environment. Based on a comparison and comprehensive consideration of the data derived from the simulations, it is found that the A∗algorithm is superior in terms of optimality, completeness, time complexity, and spatial complexity. Therefore, the A∗algorithm is more valuable in finding the best path for a mobile robot in a hospital environment. © 2022 ACM.
ABSTRACT
The nasopharyngeal swab is the standardized method of collecting specimens for diagnosing COVID-19, among numerous other respiratory illnesses. While there has been interest from the robotics community in the design of robots and manipulators for performing swab collections, detailed simulation and planning for swab insertion trajectories through the nasal cavity is less studied. In this work, we propose a simulation environment with the swab modelled as an Euler-Bernoulli beam, subject to linear elastic collisions coming from the nasal cavity. We evaluate the impact of inserting the swab with different amounts of force. We also leverage the simulation environment to pose an optimization problem that finds trajectories that minimize strain on the swab during the insertion. We find that the optimized trajectories adhere to qualitative clinical advice. © 2022 IEEE.
ABSTRACT
Since the outbreak of the COVID-19, comprehensive and thorough environmental disinfection is a very important issue. In order to reduce personnel contact and reduce the risk of cross-infection, this paper designs an indoor disinfecting intelligent robot that can realize large-scale combined disinfection of disinfectant and ultraviolet. The whole system comprises of five main parts: control center, running control module, disinfection module, information processing module, and power module. The control center mainly adopts ESP32micro-controller to achieve the connection and control of all parts of the system. The running control module mainly controls the forward, backward, and rotation of the device and ensures that the system follows the expected path during the disinfection. The disinfection module uses liquid disinfectant and ultraviolet irradiation to inhibit the bacteria and kill COVID-19. Information processing module is responsible for the information interaction between the system and the data center. The proposed system transmits data through Wi-Fi and MQTT protocol, and realizes basic functions such as positioning, path planning, and disinfection. The proposed system can effectively solve the problem of personal contact and infection in the process of manual disinfection and have nice application value. © 2022 IEEE.
ABSTRACT
In the time of Covid-19, when social distancing is one of the important solution for avoiding virus infection, human cleaner become the one of the major spreaders of the virus. Also day-by-day human cleaner cost and the number of senior citizens are increasing, autonomous surface cleaner is in demand. It is also useful for the industry places, offices, and other public and private places. Commercial surface cleaners are available in the market with having limited functionality of covering the entire surface area. In addition, these surface cleaners demands a good amount of human efforts. In this paper author proposed design of an efficient autonomous surface cleaner using deep learning and embedded technology. It is having complete area coverage planning and dynamic obstacles avoidance strategies. The prototype of the proposed design is developed and tested in a room area of the domestic environment. It covers the significant surface areas and clean the surface efficiently with minimal human efforts. © 2022 IEEE.
ABSTRACT
The pedagogy of a first-year engineering course in manufacturing is presented. This course entitled Manufacturing and Society involves collaboration with social science, is based on industrial robots as the central theme to attract students’ interests and utilizes the flipped classroom approach for delivery. We hypothesize that, in one semester, recent high school graduates will be able to gain knowledge in manufacturing by learning the computer-aided engineering (CAD) software, applying CAD to design a penholder, fabricating the penholder using additive manufacturing and computer-aided manufacturing (CAM) software, programming the robot to create a toolpath for the pen, drawing using the pen on the penholder guided by a robot, and elaborating on impacts of robotic painting on society from a social science perspective. This course is designed to give students, regardless of their intended major in engineering, broad knowledge in manufacturing via 10 engineering, 3 social science, and 10 technical communication lectures;8 labs;and 4 projects. The social science lectures and discussions focus on how knowledge about society can be used to inform design and manufacturing decisions, social science research methods for understanding how engineers and technology can impact people's lives, and changing trends in work, the workplace, and the future workforce as it relates to manufacturing. This course aimed to give undergraduate first-year engineering students a positive view of advanced manufacturing and its impact on society. Student evaluations and comments were positive and affirmed the learning objective of teaching manufacturing to the first-year engineering students. The flipped classroom approach was demonstrated to be ideal during the COVID-19 pandemic with limited capacity for in-person lectures and labs. The use of flipped classrooms allowed students to learn at their own pace, review and reinforce knowledge, have a closer interaction with instructors, and reduce the number of technical errors using simulation tools. This course with the support of flipped classroom pedagogy can be successfully implemented in the post-pandemic era, devoting the time of the class to answer questions, expand upon the class content and have a closer in-person interaction with students. © 2022
ABSTRACT
GEMS (Girls in Engineering, Math, and Science) is a free STEAM and programming summer camp and after-school robotics club that focuses on educating girls about the fields of STEM. GEMS is divided into two main programs: miniGEMS for rising fifth through eighth middle school students and megaGEMS for rising ninth through twelfth-grade high school students. This paper will provide an overview of a new program within megaGEMS called megaGEMS AEOP (Army Education Outreach Program) for rising eleventh and twelfth-grade high school girls. The camp was initially piloted in the Summer of 2020 during the COVID-19 pandemic as a virtual four-week research camp. For Summer 2021, megaGEMS hosted the inaugural eight-week in-person Apprenticeship Research Camp from June 7-August 6, 2021, for eight rising juniors or seniors. This Apprenticeship Research Camp was held at the Autonomous Vehicle Systems (AVS) Research Laboratories located at the University of the Incarnate Word provided the students with an experiential research camp mentored by both faculty and graduate students in the science of autonomy. The camp was funded through two grants provided by the Army Education Outreach Program. Examples of projects included brain-computer interfacing, virtual reality, and Infrared and LIDAR sensor collection. One apprentice was able to obtain her FAA Part 107 UAS Operator license to collect images using a drone. The camp provided opportunities to expand soft skills, explore college-level research, and community outreach. The apprenticeship curriculum was implemented by undergraduate and graduate students which included: daily Python coding classes, developing quality research skills, improving public speaking, and introducing careers in STEAM. Local female STEM leaders were guest speakers and provided career advice. The program concluded with a research symposium where they presented their research in poster and presentation format. This paper will provide details about recruiting, lessons learned working with students and parents under COVID-19 restrictions and developing research agendas for high school students. © American Society for Engineering Education, 2022.
ABSTRACT
This paper presents design, implementation, and evaluation of a novel virtual-physical summer Robotics camp for 7th-12th grade students offered by the Manufacturing and Technology Resource Consortium (MTRC) at Stony Brook University during the COVID-19 pandemic*. The MTRC is New York state's Empire State Development's Regional Manufacturing Extension Partnership (MEP) center for the Long Island region. While this program had been offered in person in 2018 and 2019, in 2020 and 2021 due to the pandemic, it was offered online only using a virtual-physical robotics platform. The modality of this platform consisted of a novel hardware kit, which was shipped to students in advance, a web-based robot motion design software, and a curriculum which brought the hardware and software together. This paper presents a study on the feasibility and accessibility of this program and its effectiveness in engaging students and exposing them to key robotics concepts while helping them make suitable career decisions. The pre- and post-program surveys indicated that the students' interest in a STEM field increased as a result of this camp, helped them understand that robotics is much more than just programming, and taught them mechanical design, practical electronics, and microcontroller programming in a flipped and experiential learning format. Moreover, survey results also indicated an attitudinal shift in their decision making based on the knowledge, skills, and capabilities that they acquired in the camp. © American Society for Engineering Education, 2022
ABSTRACT
As the hands-on engineering education got severely affected by the lockdown due to the COVID-19 pandemic, the present study discusses how to adopt the Project-Based Learning (PBL) approach to teach complex engineering concepts in this tough time. In this paper, we have discussed the design of a gamified problem statement (using a robotic simulation environment called CoppeliaSim) which was used to teach complex engineering concepts like image processing, control systems, path planning, etc to undergraduate students by a pioneering initiative in engineering education. The study was implemented on 469 teams (1876 students) and explores how the use of a simulation environment impacts the overall performance of teams in completing the assigned problem statement. In addition to this, we have demonstrated the use of a leaderboard to increase learner engagement and motivation in completing the problem statement. Our work is useful to anyone seeking to use PBL to teach and/or learn complex engineering concepts. © 2022 IEEE.
ABSTRACT
In order to save manpower, improve the management of COVID-19 prevention and prevent the spread of the epidemic, this paper proposes and designs a medical robot based on a one-chip computer. The single-chip STC89C52 is used as the main control core. Obstacles are detected by infrared sensors. And the robot uses the tracking module to determine the path. The working states of the two DC motors are then changed by the IO-port control L298N drive template, thereby changing the motion state of the robot through the speed difference between the motors on both sides. In the intelligent tracking module, the robot first uses a genetic algorithm to find the best path forward inspection and then enters the ward. After disinfection, the robot uses STM32F4 to drive the OV2640 camera to collect data and detect the mask using the yolov5s algorithm. Finally, it sends the collected information to the computer to realize the real-time monitoring of the patient's condition. The simulation results show that the medical robot can effectively and accurately realize the requirements of path planning, facial mask recognition, and wireless communication. This will significantly improve the efficiency and safety of medical staff. © 2022 IEEE.
ABSTRACT
This is a paper on disease prediction using machine learning through a python graphical user interface application. The motivation behind this application is the pandemic (Covid- Situation) faced by the whole world and also the idea to robotize the current manual framework of initial diagnosis by the assistance of mechanized supplies and undeniable PC programming so that their important information/data can be put away for a more drawn out period and also for a more useful purpose. This paper introduces the field of diseases prediction, the treatment for the disease, and consulting with the doctors nearby through efficient programming using machine learning. It describes the need for a system of an online artificial doctor, which will not only help them in predicting and understanding the diseases, but it will also advise them of certain medicines that are necessary for controlling or curing those diseases. © 2022 IEEE.