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Related Experiment Video

Updated: Feb 14, 2026

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DSAC-ICM: A Distributional Reinforcement Learning Framework for Path Planning in 3D Uneven Terrains.

Yixin Zhou1, Fan Liu2, Zhixiao Liu3

  • 1School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

Sensors (Basel, Switzerland)
|February 13, 2026
PubMed
Summary

This study introduces DSAC-ICM, a novel Deep Reinforcement Learning approach for autonomous mobile robot navigation in complex 3D terrains. It enhances path planning by mitigating value overestimation and improving exploration for faster, more efficient robot operations.

Keywords:
3D uneven terraindistributional reinforcement learningglobal path planningintrinsic curiosity modulesoft actor–critic

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Path Planning

Background:

  • Autonomous mobile robots are vital for public safety and national defense.
  • Global path planning in 3D uneven terrains presents significant challenges for traditional methods.
  • Deep Reinforcement Learning (DRL) faces issues with value overestimation and inefficient exploration.

Purpose of the Study:

  • To develop an advanced DRL framework for robust navigation of ground robots in challenging 3D environments.
  • To address the limitations of existing DRL methods, specifically value overestimation and sparse rewards.

Main Methods:

  • Proposed DSAC-ICM: a Distributional Soft Actor-Critic framework combined with an Intrinsic Curiosity Module (ICM).
  • Learned the full probability distribution of state-action returns to mitigate value overestimation.
  • Integrated ICM to generate intrinsic rewards for enhanced exploration of novel states.

Main Results:

  • DSAC-ICM enabled effective navigation capabilities in realistic 3D uneven-terrain environments.
  • Achieved a superior balance between path quality and computational cost compared to traditional algorithms.
  • Demonstrated significantly faster convergence and higher returns than other DRL baselines.

Conclusions:

  • DSAC-ICM offers a robust and efficient solution for autonomous robot path planning in complex 3D terrains.
  • The proposed method overcomes key DRL challenges, paving the way for more capable ground robots.