仿人肌骨结构机器人通过数据驱动学习提升抗干扰能力
Robustness study of the bio-inspired musculoskeletal arm robot based on the data-driven iterative learning algorithm
- 用数据驱动迭代学习算法优化15个肌腱执行器的控制信号
- 仿真中可抗20%负载干扰，实验中达15%仍能精准跟踪轨迹
- 适合研究仿生机器人、柔性控制与高鲁棒性系统的设计者
人类手臂兼具爆发力与精度，展现出在非结构化环境中的灵巧性、顺应性和鲁棒性。本研究设计了一种新型轻量化腱驱动肌骨臂（LTDM-Arm），包含7自由度骨骼关节系统和由15个执行器组成的模块化人工肌系统（MAMS）。采用Hilly型肌肉模型，并结合数据驱动迭代学习控制（DDILC）算法，在有限时间内学习并优化重复任务的激活信号。通过仿真与实验验证了该肌骨系统的抗干扰能力。结果表明，即使在仿真中承受20%负载扰动、实验中承受15%负载扰动的情况下，该系统仍能有效完成期望轨迹跟踪任务。本研究为实现类人操作性能的先进机器人系统奠定了基础。
原文摘要 · Abstract (English)
The human arm exhibits remarkable capabilities, including both explosive power and precision, which demonstrate dexterity, compliance, and robustness in unstructured environments. Developing robotic systems that emulate human-like operational characteristics through musculoskeletal structures has long been a research focus. In this study, we designed a novel lightweight tendon-driven musculoskeletal arm (LTDM-Arm), featuring a seven degree-of-freedom (DOF) skeletal joint system and a modularized artificial muscular system (MAMS) with 15 actuators. Additionally, we employed a Hilly-type muscle model and data-driven iterative learning control (DDILC) to learn and refine activation signals for repetitive tasks within a finite time frame. We validated the anti-interference capabilities of the musculoskeletal system through both simulations and experiments. The results show that the LTDM-Arm system can effectively achieve desired trajectory tracking tasks, even under load disturbances of 20 % in simulation and 15 % in experiments. This research lays the foundation for developing advanced robotic systems with human-like operational performance.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。