Towards Robotised Palpation for Cancer Detection through Online Tissue Viscoelastic Characterisation with a Collaborative Robotic Arm
<|reference_start|>Towards Robotised Palpation for Cancer Detection through Online Tissue Viscoelastic Characterisation with a Collaborative Robotic Arm: This paper introduces a new method for estimating the penetration of the end effector and the parameters of a soft body using a collaborative robotic arm. This is possible using the dimensionality reduction method that simplifies the Hunt-Crossley model. The parameters can be found without a force sensor thanks to the information of the robotic arm controller. To achieve an online estimation, an extended Kalman filter is employed, which embeds the contact dynamic model. The algorithm is tested with various types of silicone, including samples with hard intrusions to simulate cancerous cells within a soft tissue. The results indicate that this technique can accurately determine the parameters and estimate the penetration of the end effector into the soft body. These promising preliminary results demonstrate the potential for robots to serve as an effective tool for early-stage cancer diagnostics.<|reference_end|>
@article{beber2024towards,
    title={Towards Robotised Palpation for Cancer Detection through Online Tissue
  Viscoelastic Characterisation with a Collaborative Robotic Arm},
    author={Luca Beber, Edoardo Lamon, Giacomo Moretti, Daniele Fontanelli, Matteo
  Saveriano, Luigi Palopoli},
    journal={arXiv preprint arXiv:2404.09542},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.09542},
    primaryClass={cs.RO}
}