Task-Level Time-Optimal Machining Path Planning for Grinding Manipulators

A computational framework that can plan the task-level time-optimal machining path (TOMP) of grinding manipulators is constructed based on an improved simulated annealing (SA) algorithm. When there are multiple discrete non-interconnected grinding areas on the workpiece surface that need to be polished by the grinding manipulators, the planning of TOMP for a given grinding task is important. Although many existing algorithms can be used to solve the problem, the planning of task-level TOMP is more time-consuming when the number of processing path points on the machining path increases. This paper proposes a task-level TOMP planning framework to planning a task-level time-optimal machining path for grinding manipulators. Its key point is to preprocess the weighted distance between any two processing path points. Its innovation lies in that the SA algorithm updates the current optimal global solution in real time based on parallel subchain interaction method(PSIM). The experimental results show that the planning framework can effectively solve the task-level TOMP planning problem with multiple grinding areas, and the process of planning the task-level TOMP is significantly less time-consuming without degrading the quality of the solution.