提出能量等效建模法，精准模拟软硬混合机器人运动
EquiMus: Energy-Equivalent Dynamic Modeling and Simulation of Musculoskeletal Robots Driven by Linear Elastic Actuators
- 用能量等效原理简化软体肌腱驱动的混合机器人动力学建模
- 在仿生腿上验证，仿真与实测动态特性高度一致
- 适用于控制器设计与学习型控制，支持复杂运动模式
动态建模与控制对释放软体机器人潜力至关重要，但因其复杂的本构行为和实际运行条件而面临挑战。仿生肌骨骼机器人将刚性骨架与柔性执行器结合，兼具高承载能力与内在柔韧性。尽管执行器动力学已有实验研究和代理模型，但对于具有连续分布质量、运动闭环及多样运动模式的大规模软硬混合机器人，精确高效的建模与仿真仍是一大难题。为此，本文提出EquiMus——一种基于能量等效原理的动态建模框架，并构建了基于MuJoCo的仿真系统，用于线性弹性执行器驱动的肌骨骼刚-软混合机器人。通过仿真实验与真实仿生腿测试，验证了该方法在等效性与有效性上的优势。EquiMus还展现出在控制器设计与基于学习的控制策略等下游任务中的实用性。
原文摘要 · Abstract (English)
Dynamic modeling and control are critical for unleashing soft robots' potential, yet remain challenging due to their complex constitutive behaviors and real-world operating conditions. Bio-inspired musculoskeletal robots, which integrate rigid skeletons with soft actuators, combine high load-bearing capacity with inherent flexibility. Although actuation dynamics have been studied through experimental methods and surrogate models, accurate and effective modeling and simulation remain a significant challenge, especially for large-scale hybrid rigid--soft robots with continuously distributed mass, kinematic loops, and diverse motion modes. To address these challenges, we propose EquiMus, an energy-equivalent dynamic modeling framework and MuJoCo-based simulation for musculoskeletal rigid--soft hybrid robots with linear elastic actuators. The equivalence and effectiveness of the proposed approach are validated and examined through both simulations and real-world experiments on a bionic robotic leg. EquiMus further demonstrates its utility for downstream tasks, including controller design and learning-based control strategies.
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