Path Planning Of Industrial Park Power Grid Autonomous Inspection Robot

In order to ensure the normal operation of the power grid in the industrial park,the staff will regularly check the equipment in the park,but the manual inspection method has the disadvantages of heavy workload,low efficiency,easy misjudgment and so on.The automation degree of the power grid in the modern industrial park has been improved,but the scale of the power grid has become larger,the number of equipment has become more,and it is difficult for manual inspection to meet the needs of inspection work.Therefore,the industrial park power grid inspection has the need to reduce the number of inspection personnel,reduce labor costs and missed inspection risks.With the development of robot technology,inspection robots are widely used in power grid inspection work,and move towards automation and intelligence.At present,the use of inspection robots to intelligently inspect power grid lines and electrical equipment,improve inspection efficiency,and ensure the safe operation of the power grid,which requires the construction of corresponding path planning algorithms to achieve autonomous inspection of robots.Based on the above analysis of the application scenarios,in order to ensure that the inspection robot can traverse the foothold during the driving process,find the shortest path,or meet the multi-objective optimization conditions,this paper proposes a hybrid path planning method based on binary particle swarm optimization and genetic algorithm.The main research contents include :First,model the scenario.According to the known characteristics of the industrial park environment map,the grid environment model is selected to describe the inspection environment.The reason for choosing grid modeling is that each location area has a unique corresponding coordinate in the model established by the grid method,and uses the same size grid to describe the environment space,which has high efficiency and adjustable accuracy.Secondly,a fusion algorithm based on binary particle swarm optimization combined with genetic algorithm is proposed to compare the advantages and disadvantages of the algorithm.The fusion algorithm is applied to grid path planning to improve the efficiency of path planning and reduce algorithm complexity.This method avoids the problem that the traditional particle swarm optimization algorithm is easy to premature convergence and fall into local optimal solution.By introducing appropriate adaptive adjustment parameters,the adaptability of the algorithm is improved,and the local convergence and global planning ability of the algorithm are adjusted.In the same grid environment,the paths planned by traditional genetic algorithm,binary particle swarm optimization algorithm and improved fusion algorithm are compared.The calculation results show that the fusion algorithm can obtain better optimal paths and has better practicability and adaptability.Finally,according to the characteristics of the industrial park inspection robot to meet the multi-objective task,the original single path shortest function is improved to a multi-objective optimization function.According to the specific needs,a multi-constraint function is established to replace the original fitness function with the shortest path length as a single target.Then,according to different needs,considering the priority of various factors,different functions are used for multiple simulations,and their effects and environmental adaptability are analyzed.Using the path optimization method based on improved binary particle swarm optimization combined with genetic algorithm,the multi-objective optimization fitness function for the actual scene is adopted,and the time,energy consumption and path length information are used together for road planning,so as to deal with the path planning problem of robot autonomous inspection based on multivariate information evaluation.The simulation experiment verifies the feasibility of the fusion algorithm,and obtains the optimal path of the robot under the condition of routine inspection and emergency inspection respectively.The calculation results show that the establishment of multi-constraint function in the fusion algorithm can adapt to the complex and changeable environmental changes and meet the basic requirements of robot inspection.Figure [25] Table [3] Reference [68]...