Autonomous Warehouse Robot using Deep Q-Learning

TL;DR

This paper presents a deep reinforcement learning approach for autonomous warehouse robots to navigate, avoid obstacles, and optimize space utilization, tested in simulation for single and multi-robot scenarios.

Contribution

It introduces a novel application of Deep Q-Learning and multi-agent Q-learning for warehouse robot navigation and space optimization.

Findings

Effective in simulation for single robot navigation.

Successful extension to multi-robot coordination.

Improved space utilization in warehouse simulations.

Abstract

In warehouses, specialized agents need to navigate, avoid obstacles and maximize the use of space in the warehouse environment. Due to the unpredictability of these environments, reinforcement learning approaches can be applied to complete these tasks. In this paper, we propose using Deep Reinforcement Learning (DRL) to address the robot navigation and obstacle avoidance problem and traditional Q-learning with minor variations to maximize the use of space for product placement. We first investigate the problem for the single robot case. Next, based on the single robot model, we extend our system to the multi-robot case. We use a strategic variation of Q-tables to perform multi-agent Q-learning. We successfully test the performance of our model in a 2D simulation environment for both the single and multi-robot cases.