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
Immune Plasma algorithm (IP algorithm or IPA) that models the implementation details of a medical method popularized with the COVID-19 pandemic again known as the immune or convalescent plasma has been introduced recently and used successfully for solving different engineering optimization problems. In this study, incremental donor (ID) approach was first developed for controlling how many donor individuals will be chosen before the treatment of receivers representing the poor solutions of the population and then a promising IPA variant called ID-IPA was developed as a new path planner. For analyzing the contribution of the ID approach on the solving capabilities of the IPA, a set of experimental studies was carried out and results of the ID-IPA were compared with different well-known meta-heuristic algorithms. Comparative studies showed that controlling the incrementation of donor individuals as described in the ID approach increases the qualities of the final solutions and improves the stability of the IP algorithm. © 2022 IEEE.
ABSTRACT
During an emergency, timely and effective distribution of emergency supplies is critical in rescue. In the context of Covid-19, given the difficulties in distributing supplies to communities due to super infectious viruses, unmanned vehicle distribution is studied by taking into account the priority and satisfaction of communities to improve distribution safety and effectiveness of supplies. Furthermore, the influence of distribution time on the overall efficiency is also taken into account, thus ultimately establishing an unmanned distribution model with the shortest distribution time while meeting community satisfaction. The improved whale algorithm is used to solve the dual-objective model and compared with the basic whale optimization algorithm. The results show that the improved whale algorithm demonstrates better convergence, searchability, and stability. The constructed model can scientifically distribute daily necessities to communities while considering their priority and satisfaction. © 2022 IEEE.
ABSTRACT
In the Internet of Things (IoT) era, telepresence robots (TRs) are increasingly a part of healthcare, academia, and industry due to their enormous benefits. IoT provides a sensor-based environment in which robots receive more precise information about their surroundings. The researchers work day and night to reduce cost, duration, and complexity in all application areas. It provides tremendous benefits, such as sustainability, welfare improvement, cost-effectiveness, user-friendliness, and adaptability. However, it faces many challenges in making critical decisions during motion, which requires a long training period and intelligent motion planning. These include obstacle avoidance during movement, intelligent control in hazardous situations, and ensuring the right measurements. Following up on these issues requires a sophisticated control design and a secure communication link. This paper proposes a control design to normalize the integration process and offer an auto-MERLIN robot with cognitive and sustainable architecture. A control design is proposed through system identification and modeling of the robot. The robot control design was evaluated, and a prototype was prepared for testing in a hazardous environment. The robot was tested by considering various parameters: driving straight ahead, turning right, self-localizing, and receiving commands from a remote location. The maneuverability, controllability, and stability results show that the proposed design is well-developed and cost-efficient, with a fast response time. The experimental results show that the proposed method significantly minimizes the obstacle collisions. The results confirm the employability and sustainability of the proposed design and demonstrate auto-MERLIN's capabilities as a sustainable robot ready to be deployed in highly interactive scenarios.
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
People's failure to maintain a social distance is causing the COVID19 virus to spread. We have used the drone thermal images for a maximum of 10 km of coverage to detect temperature and reduce virus spread areas. The part of the work is based on utilizing disinfectant spraying drones, disinfectant testing with the guidance of doctors, setting the path planning of drones for surveying the temperature of people, and monitoring the infected place using GPS. When the thermal camera of the drone detects the temperature values using remote sensing images, the drone covers crowded places like hospitals, cinemas, and temples using remote sensing images. One drone model is designed to provide present results using thermal images. The Proposed drone can cover an affected area of up to 16,000 square meters per hour for capturing remote sensing images. It predicts affected areas using faster CNN algorithms with 2100 thermal images. Thermal mapping is used to monitor the social distance between people, alert people that a virus is spreading, and reduce the risk factor of people's movement. In this paper, remote sensing images are analysed and detect higher temperature areas using thermal mapping (Messina and Modica in Remote Sensing 12:1491, 2020). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The development of artificial intelligence technology has proudly enhanced the quality of life and education of students. The outbreak of COVID-19 in early 2020 dealt a huge blow to the world economy and workplace environment, therefore planning a career path before graduation is a primary and core task for undergraduate students to succeed in this era. This paper introduces the framework design of an intelligent career recommendation system, which is based on the analysis of the required career ability and students' individual ability to achieve accurate career recommendations. © 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
Featured ApplicationTelepresence robot is useful for remote applications, healthcare and remote sensing.Background: The development of telepresence robots is getting much attention in various areas of human–robot interaction, healthcare systems and military applications because of multiple advantages such as safety improvement, lower energy and fuel consumption, exploitation of road networks, reduced traffic congestion and greater mobility. Methods: In the critical decision-making process during the motion of a robot, intelligent motion planning takes an important and challenging role. It includes obstacle avoidance, searching for the safest path to follow, generating appropriate behavior and comfortable trajectory generation by optimization while keeping road boundaries and traffic rules as important concerns. Results: This paper presents a state machine algorithm for avoiding obstacles and speed control design to a cognitive architecture named auto-MERLIN. This research empirically tested the proposed solutions by providing implementation details and diagrams for establishing the path planning and obstacle tests. Conclusions: The results validate the usability of our approach and show auto-MERLIN as a ready robot for short- and long-term tasks, showing better results than using a default system, particularly when deployed in highly interactive scenarios. The stable speed control of the auto-MERLIN in case of detecting any obstacle was shown.
ABSTRACT
Under the background of the continuous spread of covid-19, fresh food delivery platforms need to make decisions on how to incorporate epidemic factors into their delivery strategies. In this paper, considering the factors of large activity range, long path, low efficiency and high risk of delivery staff in reservation-type fresh food delivery, combined with the perspective of delivery platform, a path planning model is constructed. we apply the ALNS algorithm to the proposed model and compares it with other classical heuristic algorithms. The results show that our proposed model can effectively reduce risks and improve delivery efficiency. © 2022 SPIE.
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In response to the current problem of highly contagious new coronavirus and repeated epidemics, which cause great threat and inconvenience to people's production and life, In this paper, a multifunctional intelligent epidemic prevention robot control system based on a single chip microcomputer is designed to realize the intelligent management of community epidemic prevention and control. Stm32 microcontroller is used as the control core. In order to improve the efficiency of prevention and control management and reduce contact, the Jetson Nano controller is designed to provide map reproduction, positioning navigation, and path planning functions. It is used to summarize patient status information quickly and efficiently, the design provides face recognition and remote monitoring functions to realize real-time uploading of accurate data to cell phone console APP and computer terminal integrated monitoring platform. Through the map reconstruction and positioning simulation test, an optimal path is selected to ensure the stable movement of the epidemic prevention robot. The face_recognition algorithm's error reception rate, error rejection rate, and accuracy rate are 0.35%, 11.12%, and 88.53%, respectively, which are better than the face-net algorithm in three aspects and can well meet the needs of small communities. The face recognition needs of small community areas can be well met. This epidemic prevention and control system can realize efficient community epidemic prevention and control management, reduce contact transmission, and lower the difficulty of epidemic prevention and control. © 2022 IEEE.
ABSTRACT
Under the background of the continuous spread of covid-19, fresh food delivery platforms need to make decisions on how to incorporate epidemic factors into their delivery strategies. In this paper, considering the factors of large activity range, long path, low efficiency and high risk of delivery staff in reservation-type fresh food delivery, combined with the perspective of delivery platform, a path planning model is constructed. we apply the ALNS algorithm to the proposed model and compares it with other classical heuristic algorithms. The results show that our proposed model can effectively reduce risks and improve delivery efficiency. © 2022 SPIE.
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
Nowadays, the COVID-19 epidemic continues to repeat, and the novel coronaviruses are highly contagious. In order to solve the difficulties of information collection and cargo transportation in the process of epidemic prevention and control, and reduce the work intensity of epidemic prevention personnel. In this paper, a multi-functional intelligent epidemic prevention vehicle control system based on single-chip microcomputer is proposed to realize the goal of replacing manual control with intelligent vehicles. This design uses the stm32 single-chip microcomputer as the control core, for the work demand of the epidemic prevention site. First of all, the design realizes the intelligent vehicle tracking navigation and path planning function to realize the contactless distribution. Secondly, the function of face recognition and intelligent temperature measurement is completed, which can upload information remotely, process images, measure the temperature for diagnostics, and strengthen better interaction with the doctor terminal. At last, by wireless transmission, human-computer interaction is realized. The intelligent epidemic prevention vehicle control system can basically complete the designed function through many simulation experiments. Among them, the path planning algorithm can be completed within 5 s, and the success rate of face recognition is as high as 97 %, which achieves a good simulation effect and has a certain value in the current environment. © 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
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
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.