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Area-Based Total Length Estimation for Position Control in Soft Growing Robots

EasyChair Preprint no. 10446

2 pagesDate: June 28, 2023


Eversion-based mechanism has become increasingly popular in the field of robotics. This mechanism enable objects to turn inside out, similar to flipping a sock, allowing them to move through narrow spaces without making direct force on the environment. This type of movement can be useful for medical devices such as catheters and endoscopes, as it enables them to navigate through tight and curved spaces more safely. Feedback control can further enhance the accuracy and efficiency of the examination process.

In this study, an area-based method was introduced for calculating the total length of the eversion portion, which is typically controlled by a reel mechanism. The reel mechanism consists of a spool wrapped tightly with plastic tubing and connected to a motor. However, the diameter of the reel mechanism is varied by the number of plastic layers around the spool, making it challenging to calculate the total length using the standard roller model. Our method calculates the total length based on area and was validated using an optical tracking camera. The results show that our proposed method can predict the total length with a Root Mean Square Error of around 2 times, which is about 1.5% of the total length.

In conclusion, our study presents a promising solution for calculating the total length of eversion-based movements in robotics. The proposed method can reduce errors from the model and improve accuracy in medical examinations.

Keyphrases: Endoscopes & Catheters, Everting Mechanisms, Modelling and Control, Soft Growing robot

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Korn Borvorntanajanya and Shen Treratanakulchai and Enrico Franco and Ferdinando Rodriguez Y Baena},
  title = {Area-Based Total Length Estimation for Position Control in Soft Growing Robots},
  howpublished = {EasyChair Preprint no. 10446},

  year = {EasyChair, 2023}}
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