Reinforcement Learning Based Pallet Loading Algorithm and its Application to a Real Manipulator System

Seong Woo Kang, Ye Rin Min, Kyuwon Choi, Woo Jin Ahn, Sang Ryul Baek, Dae Woo Choi, Myo Taeg Lim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Manufacturers pallet loading problem (MPLP) aims to fit the maximum number of boxes into a fixed-size pallet capacity. Solving MPLP can be time-consuming due to its complexity, leading to the use of heuristic methods which may not produce optimal results. This paper proposes a pallet loading algorithm using reinforcement learning to find the optimal solution. Simulation results indicate that the proposed method utilizes the given pallet space more efficiently than the existing heuristic methods. In addition, we introduce a real-life automatic pallet loading system and demonstrate the effectiveness of the proposed algorithm.

Original languageEnglish
Title of host publication2023 20th International Conference on Ubiquitous Robots, UR 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages115-118
Number of pages4
ISBN (Electronic)9798350335170
DOIs
Publication statusPublished - 2023
Event20th International Conference on Ubiquitous Robots, UR 2023 - Honolulu, United States
Duration: 2023 Jun 252023 Jun 28

Publication series

Name2023 20th International Conference on Ubiquitous Robots, UR 2023

Conference

Conference20th International Conference on Ubiquitous Robots, UR 2023
Country/TerritoryUnited States
CityHonolulu
Period23/6/2523/6/28

Bibliographical note

Funding Information:
This work was supported by Seoul R&BD Program (CY220081).

Publisher Copyright:
© 2023 IEEE.

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Control and Optimization
  • Modelling and Simulation

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