Application of Reinforcement Learning in Robotic CNC Path Optimization

Abstract

With the widespread application of industrial robots in the field of high flexibility CNC machining, how to achieve path optimization under complex dynamic constraints has become the key to improving machining performance. Traditional heuristic algorithms and mathematical programming methods often face limitations such as low computational efficiency and strong model dependence when dealing with high-dimensional configuration spaces and nonlinear dynamic features. This article systematically reviews the research progress of reinforcement learning (RL) in robot numerical control path optimization. Firstly, the modeling method of transforming path optimization problems into reinforcement learning Markov decision processes (MDPs) was elaborated, covering state space design, continuous action mapping, and construction of multi-objective reward functions. Secondly, the application performance of deep reinforcement learning algorithms in core scenarios such as feed rate scheduling, multi axis trajectory smoothing, and machining error compensation was analyzed in detail. The research results of this article provide important theoretical support and technical reference for the development of a new generation of intelligent and autonomous robot numerical control systems.