When Mining Electric Locomotives Meet Reinforcement Learning

TL;DR

This paper explores applying reinforcement learning to autonomously control mining electric locomotives in complex coal mine environments, enhancing safety and responsiveness through an improved epsilon-greedy algorithm tested in a co-simulation platform.

Contribution

It introduces a novel reinforcement learning approach with an improved epsilon-greedy algorithm for autonomous locomotive control in coal mines, validated via a co-simulation platform.

Findings

The method ensures safe following distances in complex environments.

Locomotives respond promptly to sudden obstacles.

The approach improves safety and control accuracy.

Abstract

As the most important auxiliary transportation equipment in coal mines, mining electric locomotives are mostly operated manually at present. However, due to the complex and ever-changing coal mine environment, electric locomotive safety accidents occur frequently these years. A mining electric locomotive control method that can adapt to different complex mining environments is needed. Reinforcement Learning (RL) is concerned with how artificial agents ought to take actions in an environment so as to maximize reward, which can help achieve automatic control of mining electric locomotive. In this paper, we present how to apply RL to the autonomous control of mining electric locomotives. To achieve more precise control, we further propose an improved epsilon-greedy (IEG) algorithm which can better balance the exploration and exploitation. To verify the effectiveness of this method, a co-simulation platform for autonomous control of mining electric locomotives is built which can complete closed-loop simulation of the vehicles. The simulation results show that this method ensures the locomotives following the front vehicle safely and responding promptly in the event of sudden obstacles on the road when the vehicle in complex and uncertain coal mine environments.