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 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
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
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
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
In this article, a mobile robot for item delivery with tele-operation capability is developed and used in the field hospital. The user is able to control the robot and communicate with the patients via an web-App on a cloud server. The robots are deployed and tested on-site in a large size field hospital, while the workload of the robot is studied and planned by using simulation approach. However, the difficulties on actual implementation have arise due to the working condition and risk of infection. These take into account in the development and deployment phases of the system. © 2021 IEEE.