Estimating Travel Time for Autonomous Mobile Robots through Long Short-Term Memory

Abstract

Autonomous mobile robots (AMRs) are gaining popularity in various applications such as logistics, manufacturing, and healthcare. One of the key challenges in deploying AMR is estimating their travel time accurately, which is crucial for efficient operation and planning. In this article, we propose a novel approach for estimating travel time for AMR using Long Short-Term Memory (LSTM) networks. Our approach involves training the network using synthetic data generated in a simulation environment using a digital twin of the AMR, which is a virtual representation of the physical robot. The results show that the proposed solution improves the travel time estimation when compared to a baseline, traditional mathematical model. While the baseline method has an error of 6.12%, the LSTM approach has only 2.13%.