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<p>This paper presents a closed-loop, proprioception-driven adaptive locomotion strategy for a quadruped robot with an actuated spinal joint, aiming to enhance locomotion stability, terrain adaptability, and energy efficiency on complex rigid terrains. Unlike conventional rigid-body quadruped platforms, the proposed system incorporates an active spine-limb coupling mechanism. We establish a full-body kinematic model incorporating the spinal degree of freedom and develop a hierarchical central pattern generator (CPG) network based on Hopf oscillators to coordinate rhythmic spinal and limb motions. To address perception limitations in complex environments, we design a heuristic, threshold-based proprioceptive terrain classification framework that fuses kinematic data with contact states to classify terrain features exclusively for rigid terrain scenarios. A bio-inspired reflex mechanism is synthesized with the CPG to dynamically regulate joint equilibrium positions, body posture, and foot trajectories, ensuring adaptive stability on slopes and rugged terrains. Both simulations and prototype experiments validate the effectiveness of the proposed strategy, demonstrating significant improvements in stability, adaptability, and energy efficiency.</p>

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Proprioception-driven adaptive locomotion and posture control for spine joint quadruped robot on complex terrain

  • 期刊论文

  • 发表日期：

    2026年7月13日

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  • Guozheng Song,
  • Qinglin Ai,
  • Gangjiang Liu,
  • Bo Fu,
  • Xiaohang Shan,
  • Jianguo Yang,
  • Gang Li

摘要

This paper presents a closed-loop, proprioception-driven adaptive locomotion strategy for a quadruped robot with an actuated spinal joint, aiming to enhance locomotion stability, terrain adaptability, and energy efficiency on complex rigid terrains. Unlike conventional rigid-body quadruped platforms, the proposed system incorporates an active spine-limb coupling mechanism. We establish a full-body kinematic model incorporating the spinal degree of freedom and develop a hierarchical central pattern generator (CPG) network based on Hopf oscillators to coordinate rhythmic spinal and limb motions. To address perception limitations in complex environments, we design a heuristic, threshold-based proprioceptive terrain classification framework that fuses kinematic data with contact states to classify terrain features exclusively for rigid terrain scenarios. A bio-inspired reflex mechanism is synthesized with the CPG to dynamically regulate joint equilibrium positions, body posture, and foot trajectories, ensuring adaptive stability on slopes and rugged terrains. Both simulations and prototype experiments validate the effectiveness of the proposed strategy, demonstrating significant improvements in stability, adaptability, and energy efficiency.