Intelligent Autonomous-Robot Control for Medical Applications

The COVID-19 pandemic has shown that there is a lack of healthcare facilities to cope with a pandemic.This has also underscored the immediate need to rapidly develop hospitals capable of dealing with infectious patients and to rapidly change in supply lines to manufacture the prescription goods(including medicines)that is needed to prevent infection and treatment for infected patients.The COVID-19 has shown the utility of intelligent autonomous robots that assist human efforts to combat a pandemic.The artificial intelligence based on neural networks and deep learning can help to fight COVID-19 in many ways,particularly in the control of autonomous medic robots.Health officials aim to curb the spread of COVID-19 among medical,nursing staff and patients by using intelligent robots.We propose an advanced controller for a service robot to be used in hospitals.This type of robot is deployed to deliver food and dispense medications to individual patients.An autonomous line-follower robot that can sense and follow a line drawn on the floor and drive through the rooms of patients with control of its direction.These criteria were met by using two controllers simultaneously:a deep neural network controller to predict the trajectory of movement and a proportional-integral-derivative(PID)controller for automatic steering and speed control.

Computer Vision-Control-Based CNN-PID for Mobile Robot

With the development of artificial intelligence technology,various sectors of industry have developed.Among them,the autonomous vehicle industry has developed considerably,and research on self-driving control systems using artificial intelligence has been extensively conducted.Studies on the use of image-based deep learning to monitor autonomous driving systems have recently been performed.In this paper,we propose an advanced control for a serving robot.A serving robot acts as an autonomous line-follower vehicle that can detect and follow the line drawn on the floor and move in specified directions.The robot should be able to follow the trajectory with speed control.Two controllers were used simultaneously to achieve this.Convolutional neural networks(CNNs)are used for target tracking and trajectory prediction,and a proportional-integral-derivative controller is designed for automatic steering and speed control.This study makes use of a Raspberry PI,which is responsible for controlling the robot car and performing inference using CNN,based on its current image input.

Deep Learning Control for Autonomous Robot

Several applications of machine learning and artificial intelligence,have acquired importance and come to the fore as a result of recent advances and improvements in these approaches.Autonomous cars are one such application.This is expected to have a significant and revolutionary influence on society.Integration with smart cities,new infrastructure and urban planning with sophisticated cyber-security are some of the current ramifications of self-driving automobiles.The autonomous automobile,often known as selfdriving systems or driverless vehicles,is a vehicle that can perceive its surroundings and navigate predetermined routes without human involvement.Cars are on the verge of evolving into autonomous robots,thanks to significant breakthroughs in artificial intelligence and related technologies,and this will have a wide range of socio-economic implications.However,in order for these automobiles to become a reality,they must be endowed with the perception and cognition necessary to deal with high-pressure real-life events and make proper judgments and take appropriate action.The majority of self-driving car technologies are based on computer systems that automate vehicle control parts.From forward-collision warning and antilock brakes to lane-keeping and adaptive drive control,to fully automated driving,these technological components have a wide range of capabilities.A self-driving car combines a wide range of sensors,actuators,and cameras.Recent researches on computer vision and deep learning are used to control autonomous driving systems.For self-driving automobiles,lane-keeping is crucial.This study presents a deep learning approach to obtain the proper steering angle to maintain the robot in the lane.We propose an advanced control for a selfdriving robot by using two controllers simultaneously.Convolutional neural networks(CNNs)are employed,to predict the car’and a proportionalintegral-derivative(PID)controller is designed for speed and steering control.This study uses a Raspberry PI based camera to control the robot car.

不同类型城市建设用地扩张驱动机制比较研究——以京津冀地区为例

Different types of urban construction land are different in terms of driving factors for their expansion.Most existing studies on driving forces for urban construction land expansion have considered the construction urban land as a whole and have not examined and compared the differentiated driving forces for different types of construction land expansion.This study explored the differentiated driving mechanisms for two types of urban construction land expansion by selecting key driving factors and using spatial econometric regression and geographical detector models.The results show that there are significant differences in the driving forces for expansion between the two types of urban construction land.The driving factors of urban land expansion do not necessarily affect industrial parks.And the factors acting on expansion of both types are different in influence degree.For urban expansion,economic density growth,the value-added growth of tertiary industries,and proximity to urban centers have a negative effect.However,urbanization levels and value-added growth of secondary industries have a positive effect.The explanatory power of these factors is arranged in the following descending order:value-added growth of tertiary industries,value-added change of secondary industries,urban population growth,economic density growth,and proximity to urban centers;road network density has no significant effect.For industrial parks expansion,the value-added growth of secondary industries and road network density has a positive effect,while economic density growth has a negative effect.The explanatory power is arranged in the following descending order:value-added growth of secondary industries,road network density,and economic density growth.The findings can help implement differentiated and refined urban land use management policies.