Microassembly path planning using reinforcement learning for improving positioning accuracy of a 1 cm³ omni-directional mobile microrobot

Abstract

This paper introduces the path planning of a 1 cm³ mobile microrobot that is designed for microassembly in a microfactory. Since the conventional path planning method can not achieve high microassembly positioning accuracy, a supervised learning assisted reinforcement learning (SL-RL) method has been developed. In this mixed learning method, the reinforcement learning (RL) is used to search a movement path in the normal learning area. But when the microrobot moves into the buffer area, the supervised learning (SL) is employed to prevent it from moving out of the boundary. The SL-RL uses a gradient descent algorithm based on uniform grid tile coding under SARSA(λ) to handle the large learning state space. In addition to the uniform grid tile model, two irregular tile models called an uneven grid tile model and a cobweb tile model are designed to partition the microrobot state space. The main conclusions demonstrated by simulations are as follows: First, the SL-RL method achieves higher positioning accuracy than the conventional path planning method; second, the SL-RL method achieves higher positioning accuracy and learning efficiency than the single RL method; and third, the irregular tile models show higher learning efficiency than the uniform tile model. The cobweb tile model performs especially well.