Research On Path Planning Algorithm Of Substation Inspection Robot

The substation is the hub of the power grid system,which has the function of collecting and distributing electric energy,and plays an important role in the power grid system.In order to ensure the safety of the substation,the substation staff will regularly inspect the power equipment in the substation.With the continuous expansion of the scale of the substation and the continuous enhancement of the voltage level,the traditional manual inspection method shows many disadvantages.In recent years,with the support of artificial intelligence technology,substation inspection robots have emerged.Path planning is one of the key technologies for inspection robots in order to ensure that the equipment in the substation can be inspected on time.In this paper,the path planning algorithm is analyzed according to different inspection tasks of the substation,and the selected algorithm is improved.The main research work is as follows:Firstly,the substation environment is analyzed in detail and the actual environmental features of the substation are simplified,and the Grid Method is selected for the creation of the substation map model to provide environmental data for the path planning of the inspection robot.Then the path planning task of the substation is divided into two ways: local path planning and global path planning.The research is carried out for local path planning using the Artificial Potential Field Algorithm,and the potential field function is improved for the target unreachability problem of the traditional Artificial Potential Field Algorithm,so that the inspection robot can reach the target point smoothly when it encounters dense obstacles near the target point;when the starting point,obstacles and the target point are in a straight line,the motion direction of the robot is set to help the inspection robot get out of the When the starting point,the obstacles and the target point are in a straight line,the motion direction of the robot is set to help the inspection robot get out of the "stagnant" state.The cubic B-spline curve is used to smooth the path processing to get a more anthropomorphic path.Finally,I simulate and analyze three global path planning algorithms: Genetic Algorithm,Ant Colony Algorithm and Particle Swarm Algorithm,compare the performance of the three algorithms,and choose the Ant Colony Algorithm as the base algorithm of the global path planning algorithm;The evaluation function for the Jump Point Search Algorithm is introduced and a Fusion Algorithm is proposed for the problems of "blindness" and low efficiency of the Ant Colony Algorithm;The Fusion Algorithm is improved to address the problem that the turning angle of the Fusion Algorithm is too small when turning at the device,and finally the Ant Colony-JPS* Algorithm is proposed.Through experimental comparison,the Ant Colony-JPS* Algorithm reduces the number of turns in the multi-objective node path planning problem by 53.6% compared with the traditional Ant Colony Algorithm;the path distance is reduced by 21.1%;the total time consumed is reduced by 66.1%,which greatly improves the efficiency of path planning.In the single target node path planning problem,the average number of turns is reduced by 58.8%;the path distance is reduced by 7.7%;and the total time consumed is reduced by 65.6%.Then use the cubic B-spline curve to smooth the path to make the path more anthropomorphic.