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Learn to Steer through Deep Reinforcement Learning.

Keyu Wu1, Mahdi Abolfazli Esfahani2, Shenghai Yuan3

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This study introduces a deep reinforcement learning algorithm for safer robot navigation in complex environments. The model efficiently learns steering commands from depth images, demonstrating superior performance and real-world adaptability.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Autonomous robot navigation in complex environments is challenging.
  • Deep reinforcement learning (DRL) offers improved generalization over conventional methods.
  • DRL enables robots to learn from experience for enhanced situational awareness.

Purpose of the Study:

  • To develop an end-to-end DRL algorithm for improved autonomous steering performance.
  • To enable robots to derive steering commands directly from raw depth images.
  • To enhance the efficiency and effectiveness of robot learning frameworks.

Main Methods:

  • An end-to-end deep reinforcement learning algorithm utilizing a branching noisy dueling architecture.
  • Convolutional neural networks (CNNs) for feature extraction from depth images.
  • Simultaneous mapping of features to linear and angular velocity commands via separate network streams.

Main Results:

  • The proposed model achieved superior performance compared to baseline methods in virtual environments.
  • Demonstrated high learning efficiency, success rates, and reduced computational time.
  • Successfully transferred from simulation to real-world deployment without fine-tuning, adapting to static and dynamic obstacles.

Conclusions:

  • The developed DRL algorithm significantly enhances autonomous steering in complex environments.
  • The system's ability to use depth images ensures seamless transition from virtual to real-world applications.
  • The approach shows high adaptability and robustness in cluttered and dynamic obstacle-rich scenarios.