This paper introduces a new method for online estimating the penetration of the end-effector and the viscoelastic properties of a soft body, through palpation exams using a collaborative robotic arm. The estimator is based on the dimensionality reduction method that simplifies the nonlinear Hunt-Crossley model. In addition, in our algorithm, the model parameters can be found without a force sensor, leveraging only the robotic arm controller data. An extended Kalman filter is employed to achieve online estimation, which embeds the dynamic contact model. The algorithm is tested with various types of silicone, a material that resembles biological tissues, including samples with hard intrusions to simulate cancerous cells within a softer tissue. The results indicate that this technique can accurately determine the model parameters and estimate the penetration of the end-effector into the soft body. These promising preliminary results demonstrate robots' potential to be an effective tool fo...

Towards Robotised Palpation for Cancer Detection through Online Tissue Viscoelastic Characterisation with a Collaborative Robotic Arm / Beber, Luca; Lamon, Edoardo; Moretti, Giacomo; Fontanelli, Daniele; Saveriano, Matteo; Palopoli, Luigi. - (2024), pp. 2380-2386. ( 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2024 Abu Dhabi, UAE 14th-19th October 2024) [10.1109/IROS58592.2024.10802582].

Towards Robotised Palpation for Cancer Detection through Online Tissue Viscoelastic Characterisation with a Collaborative Robotic Arm

Beber, Luca;Lamon, Edoardo
Co-primo
;
Moretti, Giacomo;Fontanelli, Daniele;Saveriano, Matteo;Palopoli, Luigi
2024-01-01

Abstract

This paper introduces a new method for online estimating the penetration of the end-effector and the viscoelastic properties of a soft body, through palpation exams using a collaborative robotic arm. The estimator is based on the dimensionality reduction method that simplifies the nonlinear Hunt-Crossley model. In addition, in our algorithm, the model parameters can be found without a force sensor, leveraging only the robotic arm controller data. An extended Kalman filter is employed to achieve online estimation, which embeds the dynamic contact model. The algorithm is tested with various types of silicone, a material that resembles biological tissues, including samples with hard intrusions to simulate cancerous cells within a softer tissue. The results indicate that this technique can accurately determine the model parameters and estimate the penetration of the end-effector into the soft body. These promising preliminary results demonstrate robots' potential to be an effective tool fo...
2024
2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
345 E 47TH ST, NEW YORK, NY 10017 USA
Institute of Electrical and Electronics Engineers Inc.
9798350377705
Beber, Luca; Lamon, Edoardo; Moretti, Giacomo; Fontanelli, Daniele; Saveriano, Matteo; Palopoli, Luigi
Towards Robotised Palpation for Cancer Detection through Online Tissue Viscoelastic Characterisation with a Collaborative Robotic Arm / Beber, Luca; Lamon, Edoardo; Moretti, Giacomo; Fontanelli, Daniele; Saveriano, Matteo; Palopoli, Luigi. - (2024), pp. 2380-2386. ( 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2024 Abu Dhabi, UAE 14th-19th October 2024) [10.1109/IROS58592.2024.10802582].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/434611
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