ABSTRACT
We aimed to research the design and path-planning methods of an intelligent disinfection-vehicle system. A ROS (robot operating system) system was utilized as the control platform, and SLAM (simultaneous localization and mapping) technology was used to establish an indoor scene map. On this basis, a new path-planning method combining the A* algorithm and the Floyd algorithm is proposed to ensure the safety, efficiency, and stability of the path. Simulation results show that with the average shortest distance between obstacles and paths of 0.463, this algorithm reduces the average numbers of redundant nodes and turns in the path by 70.43% and 31.1%, respectively, compared to the traditional A* algorithm. The algorithm has superior performance in terms of safety distance, path length, and redundant nodes and turns. Additionally, a mask recognition and pedestrian detection algorithm is utilized to ensure public safety. The results of the study indicate that the method has satisfactory performance. The intelligent disinfection-vehicle system operates stably, meets the indoor mapping requirements, and can recognize pedestrians and masks.
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
The home health-care industry is under growing pressure to deliver services more effectively to meet the increasing demand from care recipients, particularly the elderly population. It is estimated that U.S. home health-care expenditures will rise from US[Formula Omitted]108.8 billion in 2019 to US$186.8 billion in 2027 [1] . A simultaneous ongoing shortage of physicians, registered nurses, certified nursing assistants, and social workers has created a major service delivery gap in the home health-care industry, especially in rural areas where timely access to quality health-care services is very limited [2] . The recent COVID-19 pandemic exacerbated this problem as it isolated many care recipients from their caregivers or friends.
ABSTRACT
Detecting protective measures (e.g., masks, goggles and protective clothing) is a momentous step in the fight against COVID-19. The detection mode of unmanned devices based on Simultaneous localization and mapping (SLAM) and fusion technology is more efficient, economical and safe than the traditional manual detection. In this paper, a tightly-coupled nonlinear optimization approach is used to augment the visual feature extraction of SLAM by the gyroscope of the IMU to obtain a high-precision visual inertial system for joint position and pose estimation. Based on the VINS-Mono frame, first, an LSD algorithm based on a conditional selection strategy is proposed to extract line features efficiently. Then, we propose recovering missing point features from line features. Moreover, we propose a strategy to recover vanishing point features from line features, and add residuals to the SLAM cost function based on optimization, which optimizes point-line features in real time to promote the tracking and matching accuracy. Second, the wavelet threshold denoising method based on the 3σ criterion is used to carry out real-time online denoising for gyroscope to improve the output precision. Our WD-PL-VINS was measured on publicly available EuRoC datasets, TUM VI datasets and evaluated and validated in lab testing with a unmanned vehicle (UV) based on the NVIDIA Jetson-TX2 development board. The results show that our method's APE and RPE on MH-03-easy sequences are improved by 69.28% and 97.66%, respectively, compared with VINS-Mono. © 2001-2012 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
With the rapid development of robotics and in-depth research of automatic navigation technology, mobile robots have been applied in a variety of fields. Map construction is one of the core research focuses of mobile robot development. In this paper, we propose an autonomous map calibration method using visible light positioning (VLP) landmarks and Simultaneous Localization and Mapping (SLAM). A layout map of the environment to be perceived is calibrated by a robot tracking at least two landmarks mounted in the venue. At the same time, the robot’s position on the occupancy grid map generated by SLAM is recorded. The two sequences of positions are synchronized by their time stamps and the occupancy grid map is saved as a sensor map. A map transformation method is then performed to align the orientation of the two maps and to calibrate the scale of the layout map to agree with that of the sensor map. After the calibration, the semantic information on the layout map remains and the accuracy is improved. Experiments are performed in the robot operating system (ROS) to verify the proposed map calibration method. We evaluate the performance on two layout maps: one with high accuracy and the other with rough accuracy of the structures and scale. The results show that the navigation accuracy is improved by 24.6 cm on the high-accuracy map and 22.6 cm on the rough-accuracy map, respectively.
ABSTRACT
Detecting protective measures (e.g., masks, goggles and protective clothing) is a momentous step in the fight against COVID-19. The detection mode of unmanned devices based on Simultaneous localization and mapping (SLAM) and fusion technology is more efficient, economical and safe than the traditional manual detection. In this paper, a tightly-coupled nonlinear optimization approach is used to augment the visual feature extraction of SLAM by the gyroscope of the IMU to obtain a high-precision visual inertial system for joint position and pose estimation. Based on the VINS-Mono frame, first, an LSD algorithm based on a conditional selection strategy is proposed to extract line features efficiently. Then, we propose recovering missing point features from line features. Moreover, we propose a strategy to recover vanishing point features from line features, and add residuals to the SLAM cost function based on optimization, which optimizes point-line features in real time to promote the tracking and matching accuracy. Second, the wavelet threshold denoising method based on the 3σcriterion is used to carry out real-time online denoising for gyroscope to improve the output precision. Our WD-PL-VINS was measured on publicly available EuRoC datasets, TUM VI datasets and evaluated and validated in lab testing with a unmanned vehicle (UV) based on the NVIDIA Jetson-TX2 development board. The results show that our method’s APE and RPE on MH 03 easy sequences are improved by 69.28% and 97.66%, respectively, compared with VINS-Mono. IEEE
ABSTRACT
With the further development of online shopping and the impact of the COVID-19 pandemic, the logistics industry has further increased the demand for unmanned, automated warehousing and logistics handling. To realize intelligent warehousing and logistics handling, reliable positioning navigation technology is indispensable. Therefore, this paper designs a Dual-lidar high-precision natural navigation system based on the ROS (Robot Operating System) platform, which can fulfill the basic warehousing and logistics requirements. The natural navigation system uses the Lidar-SLAM method based on graph optimization to construct the 2D environment map, the PF (Particle Filter) algorithm in MRPT (Mobile Robot Programming Toolkit) is used for system positioning, and the real-time correction algorithm is used for motion control. On the built hardware platform, the navigation system completed the fixed-point cruise navigation task, and finally achieved a navigation accuracy of 4 cm and an average repeatable navigation accuracy of 6 mm. The designed navigation system has reference significance for multi-sensor fusion navigation. In reality, it can be applied to the transportation of warehousing and logistics, and it is expected to be mass-produced in the future.
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.
ABSTRACT
Given the prospect of low birth rate and aging population among developed countries, which further resulted in the shortage of workforces, service robots have been gradually applied to real-life from past academic research. This paper introduces the development of a service robot that is designed for food service in fast-food restaurants with the innovative improvement of mapping, localization, and navigation. Moreover, this research took the initiative of integrating 3D point cloud map and 2D occupancy grid map (OGM) in order to build a PC-OGM. In another word, using the sensory fusion method allows the service robot to adapt to a more complicated environment as well as enhance its positioning. In terms of its navigation function, the adaptive motion controller is refined so the service robot could navigate through narrow aisles smoothly. Finally, friendly contact-free food service robots were evaluated at fast-food restaurants in order to gain feedback from diners and waiters. Their feedback was broken down into 3 categories, availability, reliability, and satisfaction, for further analysis. As COVID-19 assails the world, we also look into future possibilities of food service robot deployment among restaurants to keep food and surface free from the virus. Author
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.