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 this article, we present a [Formula Omitted]-learning-enabled safe navigation system—S-Nav—that recommends routes in a road network by minimizing traveling through categorically demarcated COVID-19 hotspots. S-Nav takes the source and destination as inputs from the commuters and recommends a safe path for traveling. The S-Nav system dodges hotspots and ensures minimal passage through them in unavoidable situations. This feature of S-Nav reduces the commuter's risk of getting exposed to these contaminated zones and contracting the virus. To achieve this, we formulate the reward function for the reinforcement learning model by imposing zone-based penalties and demonstrate that S-Nav achieves convergence under all conditions. To ensure real-time results, we propose an Internet of Things (IoT)-based architecture by incorporating the cloud and fog computing paradigms. While the cloud is responsible for training on large road networks, the geographically aware fog nodes take the results from the cloud and retrain them based on smaller road networks. Through extensive implementation and experiments, we observe that S-Nav recommends reliable paths in near real time. In contrast to state-of-the-art techniques, S-Nav limits passage through red/orange zones to almost 2% and close to 100% through green zones. However, we observe 18% additional travel distances compared to precarious shortest paths.
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
Unmanned Air Vehicles (UAVs) are becoming popular in real-world scenarios due to current advances in sensor technology and hardware platform development. The applications of UAVs in the medical field are broad and may be shared worldwide. With the recent outbreak of COVID-19, fast diagnostic testing has become one of the challenges due to the lack of test kits. UAVs can help in tackling the COVID-19 by delivering medication to the hospital on time. In this paper, to detect the number of COVID-19 cases in a hospital, we propose a deep convolution neural architecture using transfer learning, classifying the patient into three categories as COVID-19 (positive) and normal (negative), and pneumonia based on given X-ray images. The proposed deep-learning architecture is compared with state-of-the-art models. The results show that the proposed model provides an accuracy of 94.92%. Further to offer time-bounded services to COVID-19 patients, we have proposed a scheme for delivering emergency kits to the hospitals in need using an optimal path planning approach for UAVs in the network.
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
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
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 unmanned logistics and distribution urgently require a large number of unmanned ground vehicles(UGVs) under the influence of the potential spread of the Coronavirus Disease 2019 (COVID-19). The path planning of UGV relies excessively on SLAM map, and has no self-optimization and learning ability for the space containing a large number of unknown obstacles. In this paper, a new dynamic parameter-A* (DP-A*) algorithm is proposed, which is based on the A* algorithm and enables the UGV to continuously optimize the path while performing the same task repeatedly. First, the original evaluation functions of the A* algorithm are modified by Q-Learning to memory the coordinates of unknown obstacle. Then, Q-table is adopted as an auxiliary guidance for recording the characteristics of environmental changes and generating heuristic factor to overcome the shortcoming of the A* algorithm. At last, the DP-A* algorithm can realize path planning in the instantaneous changing environment, record the actual situation of obstacles encountered, and gradually optimize the path in the task that needs multiple explorations. By several simulations with different characteristics, it is shown that our algorithm outperforms Q-learning, Sarsa and A* according to the evaluation criteria such as convergence speed, memory systems consume, Optimization ability of path planning and dynamic learning ability. IEEE
ABSTRACT
In recent years, the logistics sector expanded significantly, leading to the birth of smart warehouses. In this context, a key role is represented by autonomous mobile robots, whose main challenge is to find collision-free paths in their working environment in real-time. Model Predictive Control Algorithms combined with global path planners, such as the A* algorithm, show great potential in providing efficient navigation for collision avoidance problems. This paper proposes a Dual Forward–Backward Algorithm to find the solution to a Model Predictive Control problem in which the task of driving a mobile robotic platform into a bi-dimensional semi-structured environment is formulated in a convex optimisation framework.
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
Robots are being increasingly used in the fight against highly-infectious diseases such as the Novel Coronavirus (SARS-CoV-2). By using robots in place of human health care workers in disinfection tasks, we can reduce the exposure of these workers to the virus and, as a result, often dramatically reduce their risk of infection. Since healthcare workers are often disproportionately affected by large-scale infectious disease outbreaks, this risk reduction can profoundly affect our ability to fight these outbreaks. Many robots currently available for disinfection, however, are little more than mobile platforms for ultraviolet lights, do not allow fine-grained control over how the disinfection is performed, and do not allow verification that it was done as the human supervisor intended. In this paper, we present a semi-autonomous system, originally designed for the disinfection of surfaces in the context of Ebola Virus Disease (EVD) that allows a human supervisor to direct an autonomous robot to disinfect contaminated surfaces to a desired level, and to subsequently verify that this disinfection has taken place. We describe the overall system, the user interface, how our calibration and modeling allows for reliable disinfection, and offer directions for future work to address open space disinfection tasks. © 2022 by the authors.
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
Autonomous flight of an unmanned aerial vehicle (UAV) or its weaponized variant named unmanned combat aerial vehicle (UCAV) requires a route or path determined carefully by considering the optimization objectives about the enemy threats and fuel consumption of the system being operated. Immune Plasma algorithm (IP algorithm or IPA) is one of the most recent optimization techniques and directly models the fundamental steps of a medical method also used for the COVID-19 disease and known as convalescent or immune plasma treatment. In this study, IP algorithm for which a promising performance has already been validated with a single population was first extended to a multi-population domain supported by a migration schema. Moreover, the usage of the donor as a source of plasma for the treatment operations of a receiver was remodeled. The new variant of the IPA empowered with the multi-population and modified donor usage approach was called Multi-IP algorithm or MULIPA. For investigating the solving capabilities of the MULIPA as a UCAV path planner, different battlefield scenarios and algorithm specific parameter configurations were used. The results obtained by the MULIPA were compared with the results of other meta-heuristic based path planners. The comparative studies between MULIPA and other techniques showed that newly proposed IPA variant is capable of finding more secure and fuel efficient paths for a UCAV system. © 2022 Elsevier Ltd
ABSTRACT
Under the influence of COVID-19, intercity ride-sharing has become more and more popular due to its relatively little contact and low price and has gradually become one of the important ways of intercity transportation. The ride-sharing platform provides functions of information interaction among passengers and drivers, allocating the transportation tasks and recommending the optimal route planning. Existing ride-sharing platforms fail to take user's personalized needs into account when assigning tasks, and users have low satisfaction with the planned routes. This paper designs an allocation algorithm (Allocation Algorithm 4 Inter-city Carpool) for intercity carpool and proposes a pricing function related to the detour distance and user's satisfaction, so as to ensure the optimal benefits for ride-sharing platforms and drivers, as well as the optimal passenger satisfaction. The AA4IC algorithm is proved to be incentive compatible and budget balanced theoretically, and the effectiveness of allocation scheme generation and path planning is verified by experiments. When the algorithm is iterated 1000 times, the time is less than 200 s, and the task assignment under the optimal user satisfaction can be achieved. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
In the context of the decrease in the number of industrial workers and the increase in labor costs, industrial robots have developed rapidly due to their many advantages. Especially after the COVID-19 epidemics, enterprises have accelerated the upgrade of robots intellectually. The quantity of china's industrial robots grew by 20% in 2020. The 2021 year's growth will reach 21%. At the same time, in the face of the global energy crisis, power rationing, energy conservation & emission reduction, the energy savings of robots are also inevitable. Under the same starting point and ending point, the energy consumption of different motion trajectory planning is very different. Based on the current mainstream industrial robot trajectory planning methods, this paper gives the trajectory algorithm formulas and optimizes them, and then combines the Lagrangian-Euler dynamics formula to derive the energy consumption formula. By simulating mainstream 6DOF manipulator robots, set the same starting point and ending point in the MatLab environment, testing different trajectories of various methods, planning and computing time-consuming, and energy consumption of the entire trajectory. The experimental results demonstrate that the energy consumption of the shortest path method is 1.4 times that of the quartic polynomial method, and the planning time is more than 800 times that of the quartic polynomial method. The energy consumption of the cubic Bezier curve method is 8.08 times that of the quartic polynomial method, and the planning time is 781 times that of the quartic polynomial method. The energy consumption of the seventh-degree polynomial method is 1.6 times that of the fourth-degree polynomial method, and the planning time is 1.28 times that of the quartic polynomial method. The time and energy consumption of the quartic polynomial and quantic polynomial methods are almost the same. Relatively speaking, the quartic polynomial interpolation method is better than the quintic polynomial. © 2022 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 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.