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.

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.

Decision making framework for autonomous vehicles driving behavior in complex scenarios via hierarchical state machine

With the development of autonomous car,a vehicle is capable to sense its environment more precisely.That allows improved drving behavior decision strategy to be used for more safety and effectiveness in complex scenarios.In this paper,a decision making framework based on hierarchical state machine is proposed with a top-down structure of three-layer finite state machine decision system.The upper layer classifies the driving scenario based on relative position of the vehicle and its surrounding vehicles.The middle layer judges the optimal driving behavior according to the improved energy efficiency function targeted at multiple criteria including driving efficiency,safety and the grid-based lane vacancy rate.The lower layer constructs the state transition matrix combined with the calculation results of the previous layer to predict the optimal pass way in the region.The simulation results show that the proposed driving strategy can integrate multiple criteria to evaluate the energy efficiency value of vehicle behavior in real time,and realize the selection of optimal vehicle driving strategy.With popularity of automatic vehicles in future,the driving strategy can be used as a reference to provide assistance for human drive or even the real-time decision-making of autonomous driving.