n order to solve the problem of robot path planning in large task monitoring, an algorithm of robot path planning based on regional monitoring was proposed. Model for large task monitoring was established for simulate the real task environment.The improved grey wolf optimizer(IGWO) algorithm was used to solve the problem that the traditional grey wolf optimizer(GWO) algorithm has poor global search ability and is easy to fall into local optimal solution.Logistic chaotic mapping was introduced to enhance the diversity of initial population.An adaptive adjustment strategy of control parameters was introduced to balance the exploration and exploitation capabilities of grey wolf optimizer algorithm.Static weighted average strategy was introduced to update population position and accelerate convergence speed. Taking robot's electric load and the short path length as constraint, K-means algorithm was introduced to cluster tasks, and IGWO algorithm is used to solve the model offline, to plan the route, and to automatically convert large task monitoring operations into time-division and step-by-step operations. Experiments show that the convergence speed, search accuracy and stability of IGWO algorithm are improved significantly by testing six international benchmark functions. The algorithm simulation of robot path planning model is carried out at 50 and 100 task points, which verifies the validity of the algorithm.The larger the task, the better the superiority of the model and the higher the proportion of path shortening.