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Adaptive emergency response and dynamic crowd navigation for mobile robot using deep reinforcement learning.

Anusha Alexander1, V N Suchir Vangaveeti2, Kalaichelvi Venkatesan1

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This study introduces a Deep Reinforcement Learning framework for mobile robot navigation in dynamic crowds. The Twin Delayed Deep Deterministic Policy Gradient algorithm demonstrated superior performance in success rate and efficiency.

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crowd navigationdeep Q-networkdeep deterministic policy gradientdeep reinforcement learningmobile robottwin delayed DeepDeterministic policy gradient

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Mobile robots are crucial for dynamic navigation in complex environments like high-density crowds and emergency situations.
  • Existing path planning and reinforcement learning methods struggle with adaptability to real-world uncertainties and dynamic obstacles.

Purpose of the Study:

  • To develop and evaluate a Deep Reinforcement Learning (DRL) framework for enhanced autonomous navigation of mobile robots in dynamic crowd environments.
  • To compare the effectiveness of Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Deep Q-Network (DQN) algorithms for this task.

Main Methods:

  • Implemented a DRL framework using DDPG, TD3, and DQN algorithms.
  • Developed a context-aware state representation integrating LiDAR-based perception, robot kinematics, and goal orientation.
  • Utilized a ROS2 Gazebo simulation with the TurtleBot3 platform for testing in challenging scenarios.

Main Results:

  • The TD3 algorithm significantly outperformed DDPG and DQN in terms of success rate, path efficiency, and collision avoidance.
  • The proposed context-aware state representation improved situational awareness for the navigation system.

Conclusions:

  • The TD3-based DRL framework offers a robust and adaptable solution for real-time, emergency-oriented mobile robot navigation in dynamic crowds.
  • The study provides a reproducible, constraint-aware navigation architecture suitable for practical applications.