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Updated: Jul 6, 2025
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Published on: August 26, 2018
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Modular hierarchical reinforcement learning for multi-destination navigation in hybrid crowds
Wen Ou1, Biao Luo1, Bingchuan Wang1
1School of Automation, Central South University, Changsha 410083, China.
Summary
This study introduces a modular hierarchical reinforcement learning (MHRL) method for robot navigation in crowded environments. MHRL effectively handles dynamic and static crowds, outperforming existing methods.
Area of Science:
- Robotics
- Artificial Intelligence
- Machine Learning
Background:
- Real-world robot navigation often involves multiple destinations.
- Crowded environments present challenges due to dynamic and static obstacles.
- Existing methods struggle with complex crowd interactions.
Purpose of the Study:
- To develop a novel method for robot navigation in complex, crowded environments.
- To address the challenges posed by dynamic and static crowds.
- To improve navigation efficiency and performance.
Main Methods:
- A modular hierarchical reinforcement learning (MHRL) approach was developed.
- MHRL comprises destination evaluation, policy switch, and motion network modules.
- Modules are designed to address distinct phases of the navigation problem.
Main Results:
- MHRL effectively handles hybrid crowds (dynamic and static).
- The method demonstrates superior performance compared to state-of-the-art techniques.
- Extensive simulations validate the effectiveness of MHRL.
Conclusions:
- MHRL offers a robust solution for robot navigation in challenging environments.
- The modular hierarchical structure enhances adaptability to crowd dynamics.
- This approach paves the way for more sophisticated autonomous navigation systems.
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