Foresee and Act Ahead: Task Prediction and Pre-Scheduling Enabled Efficient Robotic Warehousing

TL;DR

This paper introduces a pre-scheduling framework for robotic warehousing that predicts task flow and allocates tasks proactively, significantly improving efficiency and reducing robot idle time in real-world settings.

Contribution

It presents a novel spatio-temporal task prediction method and a hybrid task allocation approach that considers uncertainty and sector efficiency, validated on real datasets and in a live warehouse.

Findings

Achieved state-of-the-art task prediction accuracy.

Reduced robot empty running rate by over 50%.

Validated system effectiveness through long-term real-world deployment.

Abstract

In warehousing systems, to enhance logistical efficiency amid surging demand volumes, much focus is placed on how to reasonably allocate tasks to robots. However, the robots labor is still inevitably wasted to some extent. In response to this, we propose a pre-scheduling enhanced warehousing framework that predicts task flow and acts in advance. It consists of task flow prediction and hybrid tasks allocation. For task prediction, we notice that it is possible to provide a spatio-temporal representation of task flow, so we introduce a periodicity-decoupled mechanism tailored for the generation patterns of aggregated orders, and then further extract spatial features of task distribution with novel combination of graph structures. In hybrid tasks allocation, we consider the known tasks and predicted future tasks simultaneously and optimize the allocation dynamically. In addition, we consider factors such as predicted task uncertainty and sector-level efficiency evaluation in warehousing to realize more balanced and rational allocations. We validate our task prediction model across actual datasets derived from real factories, achieving SOTA performance. Furthermore, we implement our compelte scheduling system in a real-world robotic warehouse for months of lifelong validation, demonstrating large improvements in key metrics of warehousing, such as empty running rate, by more than 50%.