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Related Experiment Videos

FTDRL-HGSO: an efficient terrain-adaptive DRL-driven gait and path control framework for hexapod robots.

B Prabha1, Anshuman Das2, Tiago Zonta3

  • 1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India. prabha.b@vit.ac.in.

Scientific Reports
|July 6, 2026
PubMed
Summary

This study introduces FTDRL-HGSO for hexapod robots, enhancing adaptive gait control and path optimization on varied terrains. The hybrid model improves energy efficiency and stability, outperforming existing methods.

Keywords:
Convergence efficiencyFusion terrain aware Deep reinforcement LearningHenry Gas Solubility OptimizationHexapod robotsTerrain adaptability

Related Experiment Videos

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Hexapod robots require robust locomotion strategies for navigating complex, varied terrains.
  • Existing methods often struggle with dynamic adaptation and energy efficiency in unpredictable environments.
  • Deep Reinforcement Learning (DRL) and metaheuristic optimization show promise but require integration for enhanced performance.

Purpose of the Study:

  • To introduce the FTDRL-HGSO methodology for adaptive gait control and path optimization in hexapod robots.
  • To enhance locomotion strategies by integrating multi-terrain analysis, dynamic gait adaptation, and energy-efficient movement.
  • To improve the exploration-exploitation balance in DRL through hybrid learning-optimization for faster convergence.

Main Methods:

  • Developed FTDRL-HGSO by coupling a DRL policy network with the Henry Gas Solubility Optimization (HGSO) metaheuristic.
  • Implemented a multi-terrain analysis module to extract surface characteristics for dynamic gait adaptation.
  • Conducted simulations in MATLAB-Simulink on sand, clay, and rock terrains, comparing against PSO, TGPSO, GWO, and NGO.

Main Results:

  • The FTDRL-HGSO model demonstrated enhanced convergence, improved stability, and reduced energy consumption compared to benchmark models.
  • Achieved a low fitness function value of 0.165 on clay terrain and a high stability index of 0.69.
  • Outperformed existing models in convergence efficiency and adaptability across diverse terrains like rock, sand, and clay.

Conclusions:

  • The FTDRL-HGSO methodology offers superior performance for hexapod robot locomotion in varied terrains.
  • The hybrid DRL-HGSO approach effectively balances exploration and exploitation, leading to optimal locomotion strategies.
  • The model provides a significant advancement in robotic navigation, stability, and energy efficiency.