Computing forward statics from tendon-length in flexible-joint hyper-redundant manipulators
Weiting Feng, Kyle L. Walker, Yunjie Yang, Francesco Giorgio-Serchi
- Year
- 2025
- Access
- Open access
Abstract
Hyper-redundant tendon-driven manipulators of- fer greater flexibility and compliance over traditional manipu- lators. A common way of controlling such manipulators relies on adjusting tendon lengths, which is an accessible control parameter. This approach works well when the kinematic configuration is representative of the real operational con- ditions. However, when dealing with manipulators of larger size subject to gravity, it becomes necessary to solve a static force problem, using tendon force as the input and employing a mapping from the configuration space to retrieve tendon length. Alternatively, measurements of the manipulator posture can be used to iteratively adjust tendon lengths to achieve a desired posture. Hence, either tension measurement or state estimation of the manipulator are required, both of which are not always accurately available. Here, we propose a solution by reconciling cables tension and length as the input for the solution of the system forward statics. We develop a screw-based formulation for a tendon-driven, multi-segment, hyper-redundant manipulator with elastic joints and introduce a forward statics iterative solution method that equivalently makes use of either tendon length or tension as the input. This strategy is experimentally validated using a traditional tension input first, subsequently showing the efficacy of the method when exclusively tendon lengths are used. The results confirm the possibility to perform open-loop control in static conditions using a kinematic input only, thus bypassing some of the practical problems with tension measurement and state estimation of hyper-redundant systems.
Keywords
Related papers
State-of-the-art in mobile robot-assisted grinding technologies for large-scale complex components
Yusen Li, Ziwei Wang, Xiangye Zhu +9 more
Robotics and Computer-Integrated Manufacturing · 2026
A fusion prediction model of tool wear based on physical information and machine learning in five-axis milling TC4 titanium alloy
Shaoqing Qin, Lida Zhu, Yanpeng Hao +7 more
Robotics and Computer-Integrated Manufacturing · 2026
A domain-informed learning framework for seam extraction in robotic welding: Generalizing to unseen seam topologies from unstructured workpiece types
Xianzhong Zhao, Haotian Liu, Zhaoqi Huang +1 more
Robotics and Computer-Integrated Manufacturing · 2026
A novel method of suppressing low-frequency chatter in robotic milling using magnetically-induced nonlinear broadband multidirectional passive vibration absorber
Hao Li, Yuhui Yu, Rui Fu +3 more
Robotics and Computer-Integrated Manufacturing · 2026