During suturing tasks performed with minimally invasive surgical robots, configuration singularities and joint limits often force surgeons to interrupt the task and re-grasp the needle using dual-arm movements. This yields an increased operator's cognitive load, time-to-completion and performance degradation. In this paper, we propose a haptic-guided shared control method for grasping the needle with the Patient Side Manipulator (PSM) of the da Vinci robot avoiding such issues. We suggest a cost function consisting of (i) the distance from robot joint limits and (ii) the task-oriented manipulability along the suturing trajectory. Evaluating the cost and its gradient on the needle grasping manifold allows us to obtain the optimal grasping pose for joint-limit and singularity free robot movements during suturing. We compute force cues and display them through the Master Tool Manipulator (MTM) to guide the surgeon towards the optimal grasp. As such, our system helps the operator to choose a grasping configuration that allows the robot to avoid joint limits and singularities during post-grasp suturing movements. We show the effectiveness of the proposed haptic-guided shared control method during suturing using both simulated and real experiments. The results illustrate that our approach significantly improves the performance in terms of needle re-grasping.
Ghalamzan et al. 2019
Below, you can see some of them:
Parsa et al. 2022, 'The impact of motion scaling and haptic guidance on operator's workload', 2022 The ACM Conference on Human Factors in Computing Systems (CHI)-- ACM CHI is the premier international conference of Human-Computer Interaction (HCI).See some of the reltated (our) papers below:
Contact: Dr Amir Ghalamzan
@inproceedings{Parsal2022,
title={The Impact of Motion Scaling and Haptic Guidance on Operators’ Workload and Performance in Teleoperation},
author=Parsa, Soran and Maior, Horia and Thumwood, Alex and Wilson, Max and Ghalamzan E., Amir,
booktitle=CHI Conference on Human Factors in Computing Systems,
year={2022},
url={https://arxiv.org/pdf/2202.09265.pdf}
}
@inproceedings{Parsa2020,
doi = {10.1109/case48305.2020.9216789},
url = {https://doi.org/10.1109/case48305.2020.9216789},
year = {2020},
month = aug,
publisher = {{IEEE}},
author = {Soran Parsa and Disha Kamale and Sariah Mghames and Kiyanoush Nazari and Tommaso Pardi and Aravinda R. Srinivasan and Gerhard Neumann and Marc Hanheide and Ghalamzan E. Amir},
title = {Haptic-guided shared control grasping: collision-free manipulation},
booktitle = {2020 {IEEE} 16th International Conference on Automation Science and Engineering ({CASE})}
}
@inproceedings{Selvaggio2019,
doi = {10.1109/iros40897.2019.8968109},
url = {https://doi.org/10.1109/iros40897.2019.8968109},
year = {2019},
month = nov,
publisher = {{IEEE}},
author = {Mario Selvaggio and Amir M. Ghalamzan E and Rocco Moccia and Fanny Ficuciello and Bruno Siciliano},
title = {Haptic-guided shared control for needle grasping optimization in minimally invasive robotic surgery},
booktitle = {2019 {IEEE}/{RSJ} International Conference on Intelligent Robots and Systems ({IROS})}
}
@article{GhalamzanE2018,
doi = {10.1016/j.robot.2017.12.001},
url = {https://doi.org/10.1016/j.robot.2017.12.001},
year = {2018},
month = mar,
publisher = {Elsevier {BV}},
volume = {101},
pages = {45--56},
author = {Amir M. Ghalamzan E. and Matteo Ragaglia},
title = {Robot learning from demonstrations: Emulation learning in environments with moving obstacles},
journal = {Robotics and Autonomous Systems}
}
@inproceedings{Ghalamzan2017,
doi = {10.1109/iros.2017.8206178},
url = {https://doi.org/10.1109/iros.2017.8206178},
year = {2017},
month = sep,
publisher = {{IEEE}},
author = {E. Amir M. Ghalamzan and Firas Abi-Farraj and Paolo Robuffo Giordano and Rustam Stolkin},
title = {Human-in-the-loop optimisation: Mixed initiative grasping for optimally facilitating post-grasp manipulative actions},
booktitle = {2017 {IEEE}/{RSJ} International Conference on Intelligent Robots and Systems ({IROS})}
}
@inproceedings{Talha2016,
doi = {10.1109/ssrr.2016.7784294},
url = {https://doi.org/10.1109/ssrr.2016.7784294},
year = {2016},
month = oct,
publisher = {{IEEE}},
author = {M. Talha and E. A. M. Ghalamzan and C. Takahashi and J. Kuo and W. Ingamells and R. Stolkin},
title = {Towards robotic decommissioning of legacy nuclear plant: Results of human-factors experiments with tele-robotic manipulation, and a discussion of challenges and approaches for decommissioning},
booktitle = {2016 {IEEE} International Symposium on Safety, Security, and Rescue Robotics ({SSRR})}
}
@inproceedings{GhalamzanE2015,
doi = {10.1109/icra.2015.7139467},
url = {https://doi.org/10.1109/icra.2015.7139467},
year = {2015},
month = may,
publisher = {{IEEE}},
author = {Amir M. Ghalamzan E. and Luca Bascetta and Marcello Restelli and Paolo Rocco},
title = {Estimating a Mean-Path from a set of 2-D curves},
booktitle = {2015 {IEEE} International Conference on Robotics and Automation ({ICRA})}
}
@inproceedings{GhalamzanE2015,
doi = {10.1109/icra.2015.7139985},
url = {https://doi.org/10.1109/icra.2015.7139985},
year = {2015},
month = may,
publisher = {{IEEE}},
author = {Amir M. Ghalamzan E and Chris Paxton and Gregory D. Hager and Luca Bascetta},
title = {An incremental approach to learning generalizable robot tasks from human demonstration},
booktitle = {2015 {IEEE} International Conference on Robotics and Automation ({ICRA})}
}
@article{GhalamzanE2021,
doi = {10.1016/j.simpa.2021.100087},
url = {https://doi.org/10.1016/j.simpa.2021.100087},
year = {2021},
month = aug,
publisher = {Elsevier {BV}},
volume = {9},
pages = {100087},
author = {Amir Ghalamzan E. and Kiyanoush Nazari and Hamidreza Hashempour and Fangxun Zhong},
title = {Deep-{LfD}: Deep robot learning from demonstrations},
journal = {Software Impacts}
}