Survey on robotic systems for internal logistics

article

Bernardo, Rodrigo
Sousa, J.M.C.
Gonçalves, P.J.S.

Elsevier

BERNARDO, Rodrigo ; SOUSA, João M.C. ; GONÇALVES, Paulo J.S. (2022) - Survey on robotic systems for internal logistics. Journal of Manufacturing Systems. Vol 65, p. 339-350. DOI 10.1016/j.jmsy.2022.09.014.

10.1016/j.jmsy.2022.09.014

Survey on robotic systems for internal logistics

Internal logistic
Logistic systems
Robotics
Logistics planning
Mobile robotics

FCT — Foundation for Science and Technology, through IDMEC, under LAETA, project UIDB/50022/2020
PhD Scholarship BD\6841\2020 from FCT
European Union’s Horizon 2020 programme Grant Agreement No.: 951972

2022-12-05T09:15:32Z
2022-12-05T09:15:32Z
2022

The evolution of production systems has established major challenges in internal logistics. In order to overcome these challenges, new automation solutions have been developed and implemented. This paper is a literature review and analysis of selected scientific studies, which has as the main focus the existing solutions in robotics for internal logistics. The review aims to provide a broad perspective of the existing robotic systems for internal logistics to determine which research paths have been followed to date and highlight the current and future research directions. The survey has been subdivided into the following topics: localisation and path planning; task planning; optimisation and knowledge representation in robotic systems; and applications. The analysis of the works developed until the date of this review highlights the appearance of strategies in the different disciplines based on meta-heuristics. These are replacing the classical and heuristic approaches due to their limitations in dealing with a large amount of information in internal logistic systems. Due to the increase of information that robotic agents have to process, strategies based on semantic knowledge have been gaining prominence to make the domain knowledge explicit and eliminate ambiguities, allowing agents to reason and facilitate knowledge sharing between robotic agents and humans.
info:eu-repo/semantics/publishedVersion

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eng