Dynamic Zoning of Industrial Environments with Autonomous Mobile Robots
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Authors
Keith, Russell Wood
Issue Date
2024
Type
Thesis
Language
en_US
Keywords
Autonomous Mobile Robots , Manufacturing , Scheduling , Warehouse
Alternative Title
Abstract
With the increasing adoption of autonomous mobile robots (AMRs) in manufacturing and warehouse environments, efficient task scheduling and resource allocation are critical for optimizing performance. This thesis presents two scheduling algorithms that divide a manufacturing/warehouse floor into zones that an AMR will occupy and complete part pick-up and drop-off tasks. Each zone is balanced so that every AMR will share each task equally. These zones change over time to accommodate fluctuations in production and to avoid overloading an AMR with tasks. To find the optimal zone design, we first present a re-engineered version of a simulated annealing (SA) algorithm using a genetic algorithm (GA). Then a Decentralized Dynamic Zoning (DDZ) algorithm is introduced, eliminating the possibility of single-point failure from a centralized unit. An experiment was conducted comparing the adaptability of each dynamic zoning system in a simulated industrial environment. Results show that the SA and GA methods share a similar throughput, but both DDZ and GA can achieve a better distribution of tasks. This could be useful for real-world applications by making it easier to design charging and maintenance schedules without much downtime. These algorithms provide a general strategy for both manufacturing and warehouse environments as they are designed to function in unpredictable scenarios where robots can only respond to the current demands of the system.