Standard-Operational optimization of fleet management of Autonomous Mobile Robots

In smart factories, the seamless operation of operations heavily relies on efficient fleet management of
Autonomous Mobile Robots (AMRs). While fleet management algorithms play a crucial role in ensuring
smooth operations, they may encounter difficulties in effectively handling conflicts and tracking the
performance of AMRs. To address these limitations and develop an enhanced fleet management system
that boosts the availability of AMRs, it is imperative to delve deeper into Conflict Management and
Performance Monitoring approaches. This research aims to investigate the primary challenges faced by
AMRs fleet when utilizing the mentioned approaches. By identifying the specific pain points and
shortcomings, we can devise a comprehensive design specification that optimizes the performance of
AMRs fleet. Through this study, we seek to propose novel strategies and techniques that effectively
address conflict management issues and enable efficient performance monitoring of AMRs in real-time.

Faculty Supervisor:

Sousso Kélouwani

Student:

Partner:

Noovelia

Discipline:

Engineering

Sector:

Transportation and warehousing; Utilities

University:

Université du Québec à Trois-Rivières

Program: