Research On Motion Trajectory Planning Of Catenary Insulator Cleaning Robot

Due to the environmental impact,dust and haze accumulate on the surface of insulators for catenary system in electrical railway exposed to the outside,which weakens their electrical characteristics,causing pollution flashover accidents and seriously endangering the safety of train operation.Therefore,it is indispensable to clean the catenary insulators.Traditional cleaning methods are time-consuming and laborious,and can not meet the needs of modern railway transportation.Research on robot automatic cleaning technology is imperative.The automatic cleaning motion trajectory planning for the catenary insulators in electrical railway is the key technology for robot research.Ant Colony Optimization(ACO)is a bionic intelligent algorithm,which studies the interaction of strategies among related individuals.It has the characteristics of positive feedback,distributed computing and heuristic search.Applying it to path planning algorithm has become a research hotspot.However,the traditional ACO is prone to the phenomenon of many iterations and slow convergence in search path,and the algorithm is prone to stagnation,thus falling into local optimum,unable to find the global optimal solution.Aiming at the above problems,this paper presents and designs an improved trajectory planning algorithm for robot cleaning the catenary insulators.Through simulation analysis,the rationality and efficiency of the algorithm are demonstrated.The environment model of the robot’s path planning is built,based on the grid method which is easy to handle by computer.The D-H method is used to simulate and analyze the rod structure of the six-degree-of-freedom cleaning robot.The forward and inverse kinematics equations of the cleaning robot are deduced based on the set D-H parameters,and they are verified.By introducing the guidance function and adding the weight influence factor,the ant state transition strategy is improved,which makes the individual ant more oriented in the process of optimization,so that the algorithm quickly finds the optimal path and converges to the target point.A updating method of the adaptive pheromone decay factor is proposed,and in the updating of the path pheromone of ants,the optimal or sub-optimal ant strategy is adopted to add additional pheromones to the path that they passed through,so as to avoid ants falling into local optimum solution,which makes the proposed algorithm can quickly realize the global optimum of path planning.The parameters of the improved ACO are optimized and selected,including the number of ants m,information heuristic factor?,expectation heuristic factor ?,guidance heuristic factor ? and pheromone intensity coefficient Q.The path planning of improved ACO is simulated and validated on MATLAB platform.Under the simple and complex environment model in two-dimensional space,the influence ofant number m,heuristic factors ? 、 ? 、 ? on the iteration times and path distance of the algorithm are analyzed.At the same time,in the three-dimensional space environment,the heuristic function of height is introduced.The simulation results show that the improved ACO is better than the basic ACO.By comparing and analyzing the path selection between the proposed and the traditional algorithm,the efficiency of the improved ACO is proved.