Parameterized Grinding Planning of Robotic Arms Based on Optimization Experiences Generalization for Tubular Workpieces

In order to make industrial robotic arms adaptively accomplish the uniform grinding tasks for tubular workpieces of different sizes, spatial orientations and surface characteristics, a parameterized planning framework is proposed in this paper. In this framework, Moving-Circle-Determination(MCD)-based geometric parameter calculation is designed for two types of tubular workpieces (both elbow and tee tube). Then the Adaptive Uniform Grouping (AUG) mechanism is designed and applied in path cost calculation module to make all points evenly grouped, and results in a minimal computational complexity of optimization algorithm in the framework. And then Two-stage Progressive Optimization (TPO) algorithm, containing both Experience Generalization Ant Colony System (EGACS) and Double Deep Q-Network (DDQN), is designed to calculate the optimal grinding order in the maximal convergence rate, where optimization capabilities of both EGACS and DDQN are enhanced with accumulation of uniform grinding task based on optimization experiences generalization mechanism applied in TPO algorithm. Finally Parameterized Velocity Vector Summation (PVVS) is designed and applied in the path planning and tracking control module to drive robotic arms to accomplish uniform grinding tasks. Compared to the state-of-the-art methods and algorithms, performance advantages of the planning framework proposed are at least 16.94%, 21.60%, 28.78%, 4.59% and 48.68% in terms of size estimation, spatial orientation estimation, convergence rate, solution quality and computational complexity respectively.