Abstract

The material handling industry is in the middle of a transformation from manual operations to automation due to the rapid growth in e-commerce. Autonomous mobile robots (AMRs) are being widely implemented to replace manually operated forklifts in warehouse systems to fulfil large shipping demand, extend warehouse operating hours, and mitigate safety concerns. Two open questions in AMR management are task assignment and path planning. This dissertation addresses the task assignment and path planning (TAPP) problem for autonomous mobile robots (AMR) in a warehouse environment. The goals are to maximize system productivity by avoiding AMR traffic and reducing travel time. The first topic in this dissertation is the development of a discrete event simulation modeling framework that can be used to evaluate alternative traffic control rules, task assignment methods, and path planning algorithms. The second topic, Risk Interval Path Planning (RIPP), is an algorithm designed to avoid conflicts among AMRs considering uncertainties in robot motion. The third topic is a deep reinforcement learning (DRL) model that is developed to solve task assignment and path planning problems, simultaneously. Experimental results demonstrate the effectiveness of these methods in stochastic warehouse systems.

Publication Date

11-22-2021

Document Type

Dissertation

Student Type

Graduate

Advisor

Michael E. Kuhl

Advisor/Committee Member

Andres Kwasinski

Advisor/Committee Member

Amlan Ganguly

Campus

RIT – Main Campus

Share

COinS