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  4. Computing forward statics from tendon-length in flexible-joint hyper-redundant manipulators
 
conference paper

Computing forward statics from tendon-length in flexible-joint hyper-redundant manipulators

Feng, Weiting
•
Walker, Kyle L.  
•
Yang, Yunjie
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October 19, 2025
2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Hyper-redundant tendon-driven manipulators offer greater flexibility and compliance over traditional manipulators. 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 conditions. 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.

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Type
conference paper
DOI
10.1109/iros60139.2025.11247715
Author(s)
Feng, Weiting
Walker, Kyle L.  

École Polytechnique Fédérale de Lausanne

Yang, Yunjie
Giorgio-Serchi, Francesco
Date Issued

2025-10-19

Publisher

IEEE

Published in
2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
DOI of the book
https://doi.org/10.1109/IROS60139.2025
ISBN of the book

979-8-3315-4393-8

Start page

16287

End page

16294

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
CREATE-LAB  
Event nameEvent acronymEvent placeEvent date
2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

IROS 2025

Hangzhou, China

2025-10-19 - 2025-10-25

Available on Infoscience
December 1, 2025
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/256519
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