ABSTRACT
A fundamental issue in robotics is the precise localization of mobile robots in uncertain environments. Due to changing environmental patterns and lighting, localization under difficult perceptual conditions remains problematic. This paper presents an approach for locating an outdoor mobile robot using deep learning algorithms merge with 3D Light Detection and Ranging LiDAR data and RGB-D image. This approach is divided into three levels. The first is the training level, which involves scanning the localization area with a 3D LiDAR sensor and then converting the data into a 2.5D image based on the Principal Component Analysis. The testing is the second level in the Intensity Hue Saturation process. Then, the RGB and Depth images are combined to create a 2.5D fusion image. These datasets are trained and tested using Convolution Neural Networks. The K-Nearest Neighbor algorithm is used in the third level is the classification. The results show that the proposed approach is better in terms of accuracy of 97.46% and the Mean error distance is 0.6 meters.
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
In recent years, with the emergence of COVID-19, the shortage of medical resources has become increasingly obvious. However, current environments such as hospital wards still require a large number of medical staff to deliver medicines. In this paper, we propose a mobile robot that can complete medicine grabbing and delivery in a hospital ward scenario. First, a lightweight neural network is built to improve the detection efficiency of Faster R-CNN algorithm for boxed medicine. Then, the pose of the robotic arm grasping the pill box is determined by point cloud matching to control the mechanical grasping of the pill box. Finally, a discomfort function representing the collision risk between the robot and the pedestrian is incorporated into the Risk-RRT algorithm to improve the navigation performance of the algorithm. By building a real experimental platform, the experiments verify the performance of our proposed medicine delivery robot system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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
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
This article presents the design of an Application in Android operating system, with the aim of monitoring movements in real time through a 2D graphic representation system for a group of Mobile Robots with medical assistance capabilities, in a portable and economical way. In addition, to provide information about the robots positioning in Cartesian coordinates, percentage of current battery and state of operation during their navigation. The robots are link to an Android mobile device through wireless communication in MQTT or WiFi protocol, depending on the technical case available. As a basis of application there is in the first instance a Covid hospital, the need arises as a means of medical assistance between the patient with Covid symptoms and the doctor, nurses and support staff in their treatment and isolation, in order to avoid contact, contagion and the additional expense of wearing special clothing to treat the patient. © 2022 IEEE.
ABSTRACT
Mobile robots have been used in warehouses worldwide as a means for distribution of goods and gained demand after the Covid19 labor issue. This paper proposes an Autonomous Mobile Robot (AMR) to navigate in a warehouse environment to its target location using LIDAR. The method used to solve this problem is a deep reinforcement learning algorithm called deep Q-network (DQN) to detect and avoid obstacles and reach the target location. DQN is used as it is desired for solving complex tasks. Training of the DQN algorithm is carried out in ROS Gazebo environment using LIDAR-based robot model. The LIDAR sensor detects the obstacles and the odometer sensor helps to find the distance between the target location are used as inputs for training the algorithm and optimal actions are taken based on the two inputs. A reward policy is awarded when an obstacle is avoided and reaches the target location. The results show that mobile robot can successfully navigate in an unknown environment through simulation and real life. © 2022 IEEE.
ABSTRACT
In the Covid-19 epidemic, the automated guided vehicles (AGVs) could be use in material handling which prevent interpersonal contact and be a factor in reducing the epidemic. As the consequence of the labor shortage, the recruitment of labor on material handling tends to be scarce and the labor cost is also higher. Therefore, the solution is to use AGVs in the material handling in the production line. The benefit of reducing the human labor is the ability to reduce the cycle time and human error in the operation. However, at present, the cost of the AGVs from the manufacturer or the dealer is high. This paper introduces the development process of an automatic transport vehicle by using Mecanum wheels which can carry payload up to 20 kg. The hardware and software of material handling vehicle was designed such as the material of the structure, sensors, electrical circuit, controller, and computer algorithm. The autonomous material handling vehicle was tested in the transportation of small parts in factories within limited workspaces where conventional wheeled vehicles cannot operate. The RP-LiDAR S1 was used to acquire main sensing signal to find the appropriate escape route. The experimental results showed that the intelligent nagavative system enhance the ability of obstacle avoidance to the material handling vehicles in the real situation in the transportation of small parts in a factory. © 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
In this paper, an autonomous mobile robot (AMR) that delivers food to patients in hospitals during COVID-19 pandemic situation was proposed. AMRs are one of the tools that can minimize direct physical contact between healthcare worker and patients to prevent infection. As some of the hospital logistic tasks are handled by the AMRs, more manpower can be distributed to more important tasks. This is a capstone project developed by a group of students from the Faculty of Engineering, at UCSI University. The project was completed with a concept design, a virtual prototype, and computer simulation of the robot. © 2021 IEEE.
ABSTRACT
The COVID-19 pandemic made robot manufacturers explore the idea of combining mobile robotics with UV-C light to automate the disinfection processes. But performing this process in an optimum way introduces some challenges: on the one hand, it is necessary to guarantee that all surfaces receive the radiation level to ensure the disinfection; at the same time, it is necessary to minimize the radiation dose to avoid the damage of the environment. In this work, both challenges are addressed with the design of a complete coverage path planning (CCPP) algorithm. To do it, a novel architecture that combines the glasius bio-inspired neural network (GBNN), a motion strategy, an UV-C estimator, a speed controller, and a pure pursuit controller have been designed. One of the main issues in CCPP is the deadlocks. In this application they may cause a loss of the operation, lack of regularity and high peaks in the radiation dose map, and in the worst case, they can make the robot to get stuck and not complete the disinfection process. To tackle this problem, in this work we propose a preventive deadlock processing algorithm (PDPA) and an escape route generator algorithm (ERGA). Simulation results show how the application of PDPA and the ERGA allow to complete complex maps in an efficient way where the application of GBNN is not enough. Indeed, a 58% more of covered surface is observed. Furthermore, two different motion strategies have been compared: boustrophedon and spiral motion, to check its influence on the performance of the robot navigation.
ABSTRACT
Since 2019, coronavirus has impacted all aspects of humans and become one of the most serious pandemics in history. To prevent the spread of virus transmission in public, cleaning and disinfection with the support of technology are effective ways worldwide. The paper focuses on the design and development of a multifunctional autonomous mobile disinfection robot, namely MEM-Bot. The MEM-Bot integrates robust and well-developed technologies such as remote control, auto-navigation in a mobile platform with a biology/chemical solution to sanitization and disinfection effectively. The MEM-Bot consists of 8 UCV lamps with 240 W radiation with no restriction emitting area, a 360o spinning detergent system, a pair of retractable arms equipped with mist nozzles to cover the lower area of spraying, and a floor-cleaning module. The overall disinfection zone is cylindrical with a radius of 1 meter and a height of 3 meters. Fabricated at a much lower cost than commercialized robots, MEM-Bot has been rapidly deployed and tested at the Phenikaa University campus zone for months. The MEM-Bot is a potential solution for developing countries against the COVID-19 © 2022 Int. J. Mech. Eng. Rob. Res
ABSTRACT
The latest spinoffs in the field of Autonomous Vehicles have paved way for a revolution in mobility and transportation;particularly in the warehousing and distribution sector. AMRs, Autonomous Mobile Robots, are being deployed to assist in warehousing activities as they present multiple advantages. In this paper, an AMR coupled with image processing and deep learning is introduced as a novel approach to solve a two-fold problem: surveillance and disinfection. Deep learning will make use of real-time data collected by the AMR's camera as a smart surveillance method for abnormal event detection. YOLOv4 is used to train a custom dataset for object detection on five different classes. The latter obtained a 74.40% accuracy. The vehicle will also be used to diffuse disinfecting agents as a mean to sanitize the stores and stocks against Covid-19. Moreover, autonomous navigation of the AMR will be based on image processing techniques for path track detection. © 2022 IST-Africa Institute and Authors.
ABSTRACT
This paper describes the evolution of the Assistant Personal Robot (APR) project developed at the Robotics Laboratory of the University of Lleida, Spain. This paper describes the first APR-01 prototype developed, the basic hardware improvement, the specific anthropomorphic improvements, and the preference surveys conducted with engineering students from the same university in order to maximize the perceived affinity with the final APR-02 mobile robot prototype. The anthropomorphic improvements have covered the design of the arms, the implementation of the arm and symbolic hand, the selection of a face for the mobile robot, the selection of a neutral facial expression, the selection of an animation for the mouth, the application of proximity feedback, the application of gaze feedback, the use of arm gestures, the selection of the motion planning strategy, and the selection of the nominal translational velocity. The final conclusion is that the development of preference surveys during the implementation of the APR-02 prototype has greatly influenced its evolution and has contributed to increase the perceived affinity and social acceptability of the prototype, which is now ready to develop assistance applications in dynamic workspaces.
ABSTRACT
In today's world, technology has drastically increased in all sectors of industries and businesses. Automated machines demands have increased rapidly. Most small and medium scale businesses are trying to use this technology to increase the speed and reliability. One of the booming technologies is robotics, which helps businesses in so many ways. This paper discusses the approach and experience in the design, simulation, modelling, testing, and deployment of a Low-Cost food delivery robot in hotels, restaurants etc. These food delivery robots give an enhanced experience for the customers and benefits the restaurant business financially by bringing attention to visitors and act as a publicity. The restaurant industry is also experiencing a downturn because of the COVID-19 breakout. With this method, food can be delivered directly from the kitchen to the customer's table while maintaining all norms and sanitary guidelines. The robot uses microcontroller mounted with DC motors. Ultrasonic sensors and IR sensors are used for mapping and localization of destination tables, motor drivers, obstacle detection, collision avoidance, path detection. The robot performed as per the test and achieved the desired result. © 2022 IEEE.
ABSTRACT
The COVID-19 pandemic has demonstrated the need for a more effective and efficient disinfection approach to combat infectious diseases. Ultraviolet germicidal irradiation (UVGI) is a proven mean for disinfection and sterilization and has been integrated into handheld devices and autonomous mobile robots. Existing UVGI robots which are commonly equipped with uncovered lamps that emit intense ultraviolet radiation suffer from: inability to be used in human presence, shadowing of objects, and long disinfection time. These robots also have a high operational cost. This paper introduces a cost effective germicidal system that utilizes UVGI to disinfect pathogens, such as viruses, bacteria, and fungi, on high contact surfaces (e.g. doors and tables). This system is composed of a team of 5-DOF mobile manipulators with end-effectors that are equipped with far-UVC excimer lamps. The design of the system is discussed with emphasis on path planning, coverage planning, and scene understanding. Evaluations of the UVGI system using simulations and irradiance models are also included. Please see the project's website for videos and simulations of the robot.1 © 2022 IEEE.
ABSTRACT
The emergence of COVID-19 pandemic led to an increase in establishing methods of sterilization and prevention, and also in the searching for sterilization methods at the lowest cost and most effective in eliminating viruses. Robots are widely used in many fields including the sterilization to reduce the risk to human life. This work presents a design and implementation of robot for automatic surface disinfecting using Ultraviolet (UV) lights. Arduino UNO R3 is used as micro controller to control the movement of the mobile robot and three ultrasonic sensor which used to avoid robot collision with obstacles. UV lights are used in the sterilization processes of surfaces, air and water, as it ruptures the DNA of bacteria or viruses and thus prevents it from reproduce. Eight UV lights are used in this research work which are fixed around the mobile robot. The results showed the low cost with robot using for surface disinfecting can be obtained with using simple sensors and actuators components and UV lights, as well as being safer for humans than using chemical disinfectants. © 2022 IEEE.
ABSTRACT
The recourse to Mobile Robots (MRs) in fighting against Coronavirus pandemic (COVID-19) has today become a necessity in almost all hospitals worldwide. Indeed, for example, the Ultraviolet Disinfection (UVD) robot has been very useful, since COVID-19 pandemic began, to destroy viruses in Wuhan hospitals. However, MRs are equipped with a locomotion system, which should be capable of navigating through, generally, an unknown work environment. In such situation, MR should also have learning mechanism. In this paper, we propose a low-cost solution to deal with the problem of autonomous navigation and the trajectory optimization for MR in Field Hospitals (FHs). The architecture of these latter may be varying from one to another according to where it should be installed. Thus, a rapidly adapting to the new trajectory is then necessary to help save lives in pandemic situation. So, to conduct a successful autonomous navigation of MR particularly in COVID-19 FHs, a practical low-cost solution must then be found. To do so, a MR equipped with TCS230 Color Sensor, is used to read colored sticky notes fixed on the ground, to identify room number of the FH that serves for learning and correcting robot trajectory keypoints. Furthermore, an Android application is also developed in this work to remotely control, via a Bluetooth wireless connection, an Arduino-based MR in some specific situation. Some experiments were carried out to verify the performance of the proposed autonomous navigation approach of MR type ELEGOO Smart Robot Car V3.0. Moreover, the simulation results have confirmed that the robot can reach the goal with optimal trajectory by only exploiting the popular Q-Learning algorithm with the use of a low-cost color sensor, and sticky notes which could be easily deployed according to the architecture of the new installed FH.
ABSTRACT
As telecommunications technology progresses, telehealth frameworks are becoming more widely adopted in the context of long-term care (LTC) for older adults, both in care facilities and in homes. Today, robots could assist healthcare workers when they provide care to elderly patients, who constitute a particularly vulnerable population during the COVID-19 pandemic. Previous work on user-centered design of assistive technologies in LTC facilities for seniors has identified positive impacts. The need to deal with the effects of the COVID-19 pandemic emphasizes the benefits of this approach, but also highlights some new challenges for which robots could be interesting solutions to be deployed in LTC facilities. This requires customization of telecommunication and audio/video/data processing to address specific clinical requirements and needs. This paper presents OpenTera, an open source telehealth framework, aiming to facilitate prototyping of such solutions by software and robotic designers. Designed as a microservice-oriented platform, OpenTera is an end-to-end solution that employs a series of independent modules for tasks such as data and session management, telehealth, daily assistive tasks/actions, together with smart devices and environments, all connected through the framework. After explaining the framework, we illustrate how OpenTera can be used to implement robotic solutions for different applications identified in LTC facilities and homes, and we describe how we plan to validate them through field trials.
ABSTRACT
Currently, much medical personnel died because of being infected by COVID-19 and because of low personal protective facilities and the duties of medical personnel that must carry out to deliver the logistics to patients and make many contacts between the medical personnel and patients of COVID-19. Mobile robots are considered the right solution to complete this problem. With mobile robots, hospitals or the place of isolation can minimize contact between medical personnel and patients of COVID-19 by carrying out the logistic delivery task. To deliver the logistic, a mobile robot must have low-level control, and the mechanism to carry out the workpiece also have the mechanism to open the door. The mechanism to carry out the workpiece is a system to pick up and place the rack of logistics from one place to another. In this study, the low-level control was applied using a PID control with the parameter's value k(p)=500, t(i)=0.001, and t(d)=0.001 and teleoperation to control the mobile robot manually, so the mobile robot was able to move and carry out the load with the maximum value is 13 kg also open the door. Based on the results of the tests that have been carried out, the mobile robot with the proposed low-level control and the object management system can do the delivery task to reduce contact between medical personnel and patients of COVID-19, also the mobile robot can be controlled manually.