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SLP-Improved DDPG Path-Planning Algorithm for Mobile Robot in Large-Scale Dynamic Environment.

Yinliang Chen1, Liang Liang2

  • 1School of Computer Science, Wuhan University, Wuhan 430072, China.

Sensors (Basel, Switzerland)
|April 13, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an enhanced path planning algorithm for robots navigating dynamic environments. The SLP-enhanced DDPG method improves obstacle avoidance and success rates in large-scale settings.

Keywords:
deep neural networkdeep reinforcement learningmobile robotpath planning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Science

Background:

  • Robotic navigation in large-scale environments with dynamic obstacles presents significant challenges.
  • Traditional Deep Deterministic Policy Gradient (DDPG) algorithms struggle with stability and real-time performance in such complex scenarios.

Purpose of the Study:

  • To enhance the stability and efficiency of DDPG algorithms for robot path planning.
  • To achieve a superior balance between stability and real-time performance in dynamic environments.

Main Methods:

  • Proposes an improved Deep Deterministic Policy Gradient (DDPG) path planning algorithm.
  • Incorporates Sequential Linear Path Planning (SLP) to generate sub-goals.
  • Utilizes DDPG to follow these sub-goals for navigation and obstacle avoidance.

Main Results:

  • The SLP-enhanced DDPG algorithm demonstrates superior performance in large-scale dynamic environments.
  • Achieved a 12.33% improvement in success rate compared to traditional DDPG.
  • Showed a 29.67% improvement compared to the A*+DDPG algorithm.

Conclusions:

  • The proposed SLP-enhanced DDPG algorithm effectively navigates robots in complex dynamic environments.
  • Offers improved stability, efficiency, and success rates for robotic path planning.