Robustness study of the bio-inspired musculoskeletal arm robot based on the data-driven iterative learning algorithm

TL;DR

This paper presents a lightweight, tendon-driven musculoskeletal arm robot that uses data-driven iterative learning control to achieve robust trajectory tracking under disturbances, mimicking human arm capabilities.

Contribution

It introduces a novel LTDM-Arm with modular muscular system and applies DDILC for improved robustness and precision in robotic arm control.

Findings

Effective trajectory tracking under load disturbances of 20% in simulation

Successful validation of anti-interference capabilities through experiments

Demonstrates potential for human-like dexterity in robotic systems

Abstract

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.