Research And Application Of Safe Path Planning In Complex Industrial Environment

The rapid development of science and technology has caused tremendous changes in people's lives,and intelligent life is affecting human lives all the time.Industrialized production also uses intelligence as the background to improve production efficiency and obtain more benefits.With the expansion of the field and scope of intelligent applications,it brings greater safety challenges to special industrial environments,such as coal production,petroleum,gas exploration,etc.,all of which have extremely high requirements for safety.Therefore,in a special industrial environment,when using robots to carry out cargo transportation,detection and construction operations,one of the important issues that need to be studied is the path planning of the robot,so that the robot can avoid the dangerous sources in the complex environment and safely transport the goods to the destination.To study one or more paths suitable for robots to avoid dangerous sources and quickly reach their destinations in a complex industrial environment is the current research focus at home and abroad.This thesis focuses on simulating the establishment of two-dimensional terrain environment and three-dimensional terrain environment,and simulating obstacles or hazards in the process of robot traveling and elaborates the entire research process and experimental results of path planning algorithm.This thesis first studies several classic algorithms in the initial stage of the path planning algorithm development process,and builds models and obstacles,and compares their advantages;based on the research background of this article,it is necessary to have an algorithm can perfectly handle the edge points of obstacles in the path planning to avoid danger in the complex industrial environment.Therefore,the emergence of the flow function method solves the problem well.It is based on the mechanical conditions,and the path obtained is streamlined,which can solve the edge points of the obstacles.Then,the two-dimensional terrain environment and the three-dimensional under the terrain environment,the improved A-Star algorithm and the improved continuous Hopfield neural network algorithm are proposed and corresponding experiments are carried out.Finally,the experiment concludes that these two improved algorithms can handle the edge points of obstacles well.So that the robot can quickly and safely travel from the starting point to the target point,improving production efficiency and saving resources.