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Mapless Path Planning for Mobile Robot Based on Improved Deep Deterministic Policy Gradient Algorithm.

Shuzhen Zhang1, Wei Tang1, Panpan Li1

  • 1School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730000, China.

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|September 14, 2024
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Summary
This summary is machine-generated.

This study enhances the Deep Deterministic Policy Gradient (DDPG) algorithm for mobile robot path planning in mapless environments, improving learning efficiency and navigation robustness against obstacles.

Keywords:
artificial potential fielddeep deterministic policy gradientmapless path planningmobile robotmulti-step update

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Traditional Deep Deterministic Policy Gradient (DDPG) algorithms face limitations in learning efficiency and navigation performance for mobile robots in mapless environments.
  • Challenges include adaptability and robustness to both static and dynamic obstacles.

Purpose of the Study:

  • To propose an improved algorithm framework for mobile robot path planning in mapless environments.
  • Enhance learning efficiency, navigation performance, adaptability, and robustness against obstacles.

Main Methods:

  • Redesigned state and action spaces within the DDPG algorithm.
  • Introduced a multi-step update strategy.
  • Incorporated a dual-noise mechanism to refine the reward function.

Main Results:

  • Achieved a 20% increase in navigation success rate stability with static obstacles.
  • Reduced pathfinding steps by 25%, leading to smoother paths.
  • Demonstrated a 45% improvement in success rate in environments with dynamic obstacles.
  • Validated through real-world mobile robot tests.

Conclusions:

  • The improved DDPG algorithm framework significantly enhances learning efficiency and navigation performance for mobile robots.
  • The proposed enhancements provide greater adaptability and robustness in complex, mapless environments with static and dynamic obstacles.
  • The algorithm is feasible and effective for real-world applications.