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Reinforcement-Learning-Based Route Generation for Heavy-Traffic Autonomous Mobile Robot Systems.

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Summary

This study introduces a reinforcement learning method for autonomous mobile robot (AMR) route planning in intralogistics. The approach enhances system throughput and reliability compared to traditional shortest-path methods, especially with many robots.

Keywords:
autonomous mobile robotsintralogisticsmulti-robot cooperationreinforcement learningroute planning

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Area of Science:

  • Robotics and Automation
  • Operations Research
  • Artificial Intelligence

Background:

  • Intralogistics systems increasingly rely on autonomous mobile robots (AMRs).
  • Optimizing multi-robot cooperation is crucial for enhancing performance and managing complexity.
  • Existing methods struggle with efficient operation in crowded environments.

Purpose of the Study:

  • To develop an advanced method for off-line route planning and on-line route execution for AMRs.
  • To improve the operational efficiency and reliability of multiple AMRs in shared intralogistics spaces.
  • To address conflict avoidance and enhance throughput in dense AMR deployments.

Main Methods:

  • A reinforcement learning (RL) approach was employed for route generation.
  • Routes were pre-computed for frequent pick-up/drop-off points to minimize conflicts.
  • An RL agent was trained on a given layout, optimizing for system performance criteria.

Main Results:

  • The proposed RL-based route planning significantly improved throughput and reliability.
  • Performance gains were observed particularly with a high density of AMRs.
  • The method outperformed traditional shortest-path algorithms in simulated scenarios.

Conclusions:

  • The RL approach offers a superior solution for AMR route planning in demanding intralogistics environments.
  • This method is recommended for scenarios requiring high throughput with numerous AMRs in confined spaces.
  • Enhanced robot cooperation through intelligent routing is key to modern intralogistics efficiency.