Controlling Fleets of Autonomous Mobile Robots with Reinforcement Learning: A Brief Survey

Author:

Wesselhöft MikeORCID,Hinckeldeyn JohannesORCID,Kreutzfeldt Jochen

Abstract

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.

Publisher

MDPI AG

Subject

Artificial Intelligence,Control and Optimization,Mechanical Engineering

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Native Support of AI Applications in 6G Mobile Networks Via an Intelligent User Plane;2024 IEEE Wireless Communications and Networking Conference (WCNC);2024-04-21

2. Simulation Analysis of a Reinforcement-Learning-Based Warehouse Dispatching Method Considering due Date and Travel Distance;2023 Winter Simulation Conference (WSC);2023-12-10

3. Designing Heterogeneous Robot Fleets for Task Allocation and Sequencing;2023 International Symposium on Multi-Robot and Multi-Agent Systems (MRS);2023-12-04

4. Lessons learned: Symbiotic autonomous robot ecosystem for nuclear environments;IET Cyber-Systems and Robotics;2023-12

5. Addressing Non-Intervention Challenges via Resilient Robotics Utilizing a Digital Twin;2023 IEEE International Conference on Omni-layer Intelligent Systems (COINS);2023-07-23

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