Path Planning Of Substation Inspection Robot Based On Potential Field Ant Colony Algorithm

In recent years,as China's industrialization process continues,the demand for electricity has also increased,and the power industry has developed rapidly.In the power system,both the total social power consumption and the total installed capacity are increasing rapidly.The substation is the hub station of the power system.The safe and stable operation of the substation is vital to the normal operation of the entire power system.In order to ensure the normal operation of the substation,operators will be scheduled to conduct regular inspections of the substation.However,there are many disadvantages.Therefore,in recent years,under the support of artificial intelligence technology,substation inspection robots have emerged at the historic moment.Path planning is one of the most important technologies in the process of robot inspection,and has also developed rapidly in recent years.However,the traditional path planning methods have their own advantages and disadvantages.The selection of the path planning algorithm of the substation inspection robot in this article is based on the actual environment and needs in the substation,and the selected algorithm is improved.The main work is as follows:First,this article analyzes the environmental characteristics of the substation in detail.The premise of path planning is to analyze the environment of the workspace and create a map model of the workspace.This article uses a LiDAR sensor-based robot to perform map modeling experiments on the environment,to "puff" or "fill in" specific shape obstacles in the map,and to simplify the grid map to complete the overall map of the substation environment Modeling provides specific and accurate environmental data for the path planning of inspection robots.Then,this paper uses ant colony algorithm and artificial potential field algorithm to plan the path of the robot,and improves the above algorithms.This article introduces the principles and applicable rules of the ant colony algorithm and artificial potential field algorithm.In view of the "blind search" of the ant colony algorithm inthe initial search stage and the inefficiency in global search,this paper proposes The potential direction of the potential field method is simplified as the direction factor is added to the initial pheromone,so that the algorithm can have a greater probability of moving toward the target point in the initial stage;the transition probability is adjusted,and the ant's transition probability is increased as the number of iterations increases.Make reasonable adjustments;change the one-way search of the ant colony algorithm to two-way search to improve the search efficiency.Through the comparison of the grid environment simulation of 20 × 20 and 30 × 30,it can be seen that the improved algorithm has both the number of iterations and the minimum path.Significantly improved.Finally,in order to solve the problems such as unreachable target and unsmooth path of artificial potential field method,this paper proposes to modify the repulsive force field function method,so that the robot can successfully reach the target point when the target point has large obstacles;At the minimum point,the movement direction of the robot can be set according to the size of the obstacle to help the robot get out of the "trap".The B-spline curve is used to smooth the planned tortuous and non-smooth path.Through the simulation results,a smooth and complete path.