3D Operation of Autonomous Excavator based on Reinforcement Learning through Independent Reward for Individual Joints

TL;DR

This paper introduces a reinforcement learning control algorithm for autonomous excavators that independently trains each joint with separate rewards, enabling precise 3D operation without prior physical knowledge.

Contribution

It presents a novel RL-based control method that expands excavator workspace to 3D and trains joints independently, improving adaptability and precision in construction environments.

Findings

Successfully expanded excavator workspace into 3D space.

Achieved independent joint training with separate rewards.

Enhanced control precision without prior physical knowledge.

Abstract

In this paper, we propose a control algorithm based on reinforcement learning, employing independent rewards for each joint to control excavators in a 3D space. The aim of this research is to address the challenges associated with achieving precise control of excavators, which are extensively utilized in construction sites but prove challenging to control with precision due to their hydraulic structures. Traditional methods relied on operator expertise for precise excavator operation, occasionally resulting in safety accidents. Therefore, there have been endeavors to attain precise excavator control through equation-based control algorithms. However, these methods had the limitation of necessitating prior information related to physical values of the excavator, rendering them unsuitable for the diverse range of excavators used in the field. To overcome these limitations, we have explored reinforcement learning-based control methods that do not demand prior knowledge of specific equipment but instead utilize data to train models. Nevertheless, existing reinforcement learning-based methods overlooked cabin swing rotation and confined the bucket's workspace to a 2D plane. Control confined within such a limited area diminishes the applicability of the algorithm in construction sites. We address this issue by expanding the previous 2D plane workspace of the bucket operation into a 3D space, incorporating cabin swing rotation. By expanding the workspace into 3D, excavators can execute continuous operations without requiring human intervention. To accomplish this objective, distinct targets were established for each joint, facilitating the training of action values for each joint independently, regardless of the progress of other joint learning.