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Efficient Path Planning for Mobile Robot Based on Deep Deterministic Policy Gradient.

Hui Gong1, Peng Wang1,2, Cui Ni1

  • 1Information Science and Electrical Engineering, Shandong Jiao Tong University, Jinan 250357, China.

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Summary
This summary is machine-generated.

This study enhances mobile robot path planning by integrating Long Short-Term Memory (LSTM) with Deep Deterministic Policy Gradient (DDPG) algorithms. The improved method accelerates convergence and boosts planning efficiency and success rates.

Keywords:
DDPGLSTMmixed noisepath planningreward function

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Traditional Deep Deterministic Policy Gradient (DDPG) algorithms exhibit low training efficiency and slow convergence for mobile robot path planning due to limited environmental observation.
  • Mobile robots require efficient and reliable path planning strategies to navigate complex environments.

Purpose of the Study:

  • To enhance the training efficiency and convergence speed of DDPG for mobile robot path planning.
  • To improve the generalization ability and success rate of path planning models.

Main Methods:

  • Introduced Long Short-Term Memory (LSTM) into the DDPG network to utilize historical and current states.
  • Incorporated a Batch Normalization (Batch Norm) layer in the Actor network and optimized the reward function.
  • Applied normalization techniques to robot-target distance and angle inputs, and used mixed Gaussian and Ornstein-Uhlenbeck (OU) noise for action output.
  • Validated the algorithm using a simulation environment built with ROS and Gazebo.

Main Results:

  • The proposed algorithm significantly accelerates the convergence speed of the DDPG training process.
  • Demonstrated improved generalization capabilities of the path planning model.
  • Achieved higher efficiency and success rates in mobile robot path planning tasks.

Conclusions:

  • Integrating LSTM with DDPG, along with network and reward function enhancements, effectively addresses the limitations of traditional DDPG in mobile robot path planning.
  • The optimized approach offers a more efficient, faster, and reliable solution for autonomous navigation.