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Towards Goal-Directed Navigation Through Combining Learning Based Global and Local Planners.

Xiaomao Zhou1, Yanbin Gao2, Lianwu Guan3

  • 1College of Automation, Harbin Engineering University, Harbin 150001, China. zhouxiaomao@hrbeu.edu.cn.

Sensors (Basel, Switzerland)
|January 10, 2019
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Summary
This summary is machine-generated.

This study introduces a novel robot navigation system combining goal-directed end-to-end learning for global planning and deep reinforcement learning (RL) for local planning. This hybrid approach enhances adaptability and efficiency in robot navigation tasks.

Keywords:
end-to-end learningglobal planninglocal planningreinforcement learningrobot navigation

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Robot navigation is a core challenge in robotics.
  • Learning-based methods are increasingly favored for their efficiency and potential in intelligent systems.
  • Existing methods may struggle with environmental changes.

Purpose of the Study:

  • To develop a goal-directed robot navigation system.
  • To integrate global and local planning for enhanced adaptability.
  • To improve robot navigation in dynamic environments.

Main Methods:

  • A hybrid approach combining goal-directed end-to-end learning for global planning and deep reinforcement learning (DRL) for local planning.
  • Global planner trained via supervised learning to imitate expert navigation.
  • Local planner developed in simulation and transferred to a real-world robot platform.

Main Results:

  • The integrated system effectively navigates robots to goal positions.
  • The DRL-based local planner complements the global planner, handling unseen situations.
  • The system demonstrates generalization capabilities across different environments.
  • Experimental results on a robot platform validate the system's effectiveness.

Conclusions:

  • The proposed navigation system offers a robust solution for goal-directed robot navigation.
  • The combination of end-to-end learning and DRL enhances adaptability to environmental changes.
  • This approach shows significant promise for developing intelligent and versatile navigation systems.