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Updated: Sep 10, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Research on Robot Obstacle Avoidance and Generalization Methods Based on Fusion Policy Transfer Learning.

Suyu Wang1,2, Zhenlei Xu1, Peihong Qiao1

  • 1School of Mechanical and Electrical Engineering, China University of Mining and Technology, Beijing 100083, China.

Biomimetics (Basel, Switzerland)
|August 27, 2025
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Summary
This summary is machine-generated.

This study enhances mobile robot path planning using deep reinforcement learning (DRL) and a novel Soft Actor-Critic (SAC) framework. The approach improves adaptability and obstacle avoidance in complex environments through bio-inspired perception and policy fusion.

Keywords:
bio-inspiredbio-inspired radar perception featuresineffective behavior recognitionpolicy fusion networkstransfer learning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Bio-inspired Computing

Background:

  • Organisms integrate sensory data and experience for efficient navigation in dynamic environments.
  • Deep reinforcement learning (DRL) enables mobile robots to learn autonomous navigation strategies in unknown scenarios.
  • Adapting robot navigation to complex, uncertain environments remains a challenge.

Purpose of the Study:

  • To develop an enhanced deep reinforcement learning framework for mobile robot path planning.
  • To improve adaptability, efficiency, and obstacle avoidance in complex environments.
  • To leverage bio-inspired mechanisms for better perception and policy transfer.

Main Methods:

  • Utilized the Soft Actor-Critic (SAC) algorithm as the core DRL framework.
  • Introduced an action-level fusion mechanism for dynamic integration of prior and current policies.
  • Implemented a bio-inspired radar perception optimization method for enhanced sensory input processing.
  • Designed a reward function based on ineffective behavior recognition to optimize training.

Main Results:

  • The proposed method demonstrated faster convergence in path planning tasks.
  • Achieved superior obstacle avoidance performance compared to existing methods.
  • Exhibited strong transferability and generalization across diverse obstacle configurations.
  • Validated effectiveness in both simulation (Gazebo) and real-world scenarios.

Conclusions:

  • The developed SAC-based framework significantly enhances mobile robot adaptability and path planning.
  • Bio-inspired perception and policy fusion contribute to more robust and efficient navigation.
  • The approach offers a promising solution for autonomous navigation in complex and uncertain environments.