Controlling fleets of autonomous mobile robots with reinforcement learning : a brief survey

Enthalten in
Robotics : open access journal 
Volume
11
Issue
5
Article Number
85
Citation
Robotics 11 (5): 85 (2022-10)
Publisher DOI
10.3390/robotics11050085
Scopus ID
2-s2.0-85140922725
Publisher
Multidisciplinary Digital Publishing Institute
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.
DDC Class
004: Informatik
600: Technik
620: Ingenieurwissenschaften