Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.4594
Publisher DOI: 10.3390/robotics11050085
Title: Controlling fleets of autonomous mobile robots with reinforcement learning : a brief survey
Language: English
Authors: Wesselhöft, Mike  
Hinckeldeyn, Johannes  
Kreutzfeldt, Jochen  
Issue Date: 30-Aug-2022
Publisher: Multidisciplinary Digital Publishing Institute
Source: Robotics 11 (5): 85 (2022)
Abstract (english): 
Controlling a fleet of autonomous mobile robots (AMR) is a complex problem of optimization. Many approached have been conducted for solving this problem. They range from heuristics, which usually do not find an optimum, to mathematical models, which are limited due to their high computational effort. Machine Learning (ML) methods offer another potential trajectory for solving such complex problems. The focus of this brief survey is on Reinforcement Learning (RL) as a particular type of ML. Due to the reward-based optimization, RL offers a good basis for the control of fleets of AMR. In the context of this survey, different control approaches are investigated and the aspects of fleet control of AMR with respect to RL are evaluated. As a result, six fundamental key problems should be put on the current research agenda to enable a broader application in industry: (1) overcoming the “sim-to-real gap”, (2) increasing the robustness of algorithms, (3) improving data efficiency, (4) integrating different fields of application, (5) enabling heterogeneous fleets with different types of AMR and (6) handling of deadlocks.
URI: http://hdl.handle.net/11420/13625
DOI: 10.15480/882.4594
ISSN: 2218-6581
Journal: Robotics : open access journal 
Other Identifiers: doi: 10.3390/robotics11050085
Institute: Technische Logistik W-6 
Document Type: Article
Project: Open-Access-Publikationskosten / 2022-2024 / Technische Universität Hamburg (TUHH) 
Funded by: Deutsche Forschungsgemeinschaft (DFG) 
License: CC BY 4.0 (Attribution) CC BY 4.0 (Attribution)
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