Bilevel Optimization for Just-in-Time Robotic Kitting and Delivery via Adaptive Task Segmentation and Scheduling

TL;DR

This paper introduces a bilevel optimization framework for adaptive, just-in-time robotic kitting and delivery, improving efficiency and resilience in manufacturing by dynamically segmenting tasks and scheduling deliveries.

Contribution

It presents a novel bilevel optimization approach for adaptive task segmentation and scheduling in robotic kitting, addressing variability and delays in manufacturing environments.

Findings

System is more efficient than baseline methods.

System is resilient to upstream delays.

Participants preferred the adaptive system.

Abstract

Kitting refers to the task of preparing and grouping necessary parts and tools (or "kits") for assembly in a manufacturing environment. Automating this process simplifies the assembly task for human workers and improves efficiency. Existing automated kitting systems adhere to scripted instructions and predefined heuristics. However, given variability in the availability of parts and logistic delays, the inflexibility of existing systems can limit the overall efficiency of an assembly line. In this paper, we propose a bilevel optimization framework to enable a robot to perform task segmentation-based part selection, kit arrangement, and delivery scheduling to provide custom-tailored kits just in time - i.e., right when they are needed. We evaluate the proposed approach both through a human subjects study (n=18) involving the construction of a flat-pack furniture table and shop-flow simulation based on the data from the study. Our results show that the just-in-time kitting system is objectively more efficient, resilient to upstream shop flow delays, and subjectively more preferable as compared to baseline approaches of using kits defined by rigid task segmentation boundaries defined by the task graph itself or a single kit that includes all parts necessary to assemble a single unit.