被动人工肌腱型双足机器人多地形适应性行走控制

To optimize bipedal robot structural design and enhance motion control stability, this study proposes a “passive tendon-active control” synergistic design strategy. Centered on human biomechanical data, the research integrates structural generation with control algorithm optimization to bridge the gap between biological efficiency and robotic robustness. On the structural design level, a passive tendon transmission scheme featuring constant-stiffness springs and cross-joint cable-driven mechanisms is implemented. This design replicates the essential biomechanical characteristics of the human Achilles tendon: it stores impact energy through elastic deformation during the touchdown phase and releases it to facilitate forward propulsion. This mechanism not only significantly improves energy efficiency but also endows the foot with an inherent passive compliance capability, allowing for natural adaptation to unstructured environments without complex sensory feedback. On the control algorithm level, the mechanical parameters of the passive tendons are integrated into a variable-stiffness spring-loaded inverted pendulum model and a whole-body dynamics framework. A “passive buffering + active regulation” synergistic mechanism is thus constructed. Specifically, during the single-support phase, human lower-limb biomechanical data are utilized to calibrate support-leg stiffness, ensuring the robot’s motion aligns with human-like characteristics. During the double-support phase, motion trajectories are planned to simulate muscle-tendon functions, effectively mitigating foot-ground impacts during gait transitions. Furthermore, tendon force constraints are incorporated into the foot-ground friction cone and joint torque boundary conditions to guarantee closed-loop stability. Experimental results demonstrate that the robot’s plantar force characteristics closely match human kinetic data. The calf stretching tendons provide effective passive compliance on rugged terrain and slippery surfaces, while the control algorithm performs real-time gait adjustments to maintain balance. This study validates the potential of bio-inspired structural design in enhancing bipedal adaptability, providing a theoretical and technical foundation for stable locomotion in complex, real-world environments.