Project Description:

Robots have entered the surgical arena to perform complex maneuvers with life-saving precision. Despite decades of developments, robots are lacking the delicacies of human tactile perception.

Commercially available robot-assisted surgical systems require new human-machine interfaces that can resolve a tradeoff between rigid machine dynamics and resilient human decision-making processes.

A division of soft robotics is employing Compliant Mechanisms (CMs), which are flexible devices that achieve force and motion through elastic body deformation. Using the elasticity of materials, rather than an assembly of stiff-hinged linkages, opens a wide range of sophisticated motions and forces to source tactile perception through kinesthetic and cutaneous force feedback cues.

Help us design intelligent soft mechanisms impeded with learning-based tactile sensing strategies to “invent and make” the next generation of human-machine interfaces for robot-assisted surgical systems. Work with robots at Purdue (da Vinci Surgical Kit) and collaborate with clinical partners (IU) and industrial partners (Stryker Corp.) to transform scientific discoveries into clinical solutions.

Start Date:

June-Aug 2023

Postdoc Qualifications:

Experience with analytical and computation tools for translating theoretical solutions into functional prototypes. Experience with medical devices and/or human factors preferred.

Co-Advisors:

Denny Yu, dennyyu@purdue.edu, Industrial Engineering, https://engineering.purdue.edu/IE/people/ptProfile?resource_id=134078

Milton Aguirre, meaguirr@purdue.edu, Mechanical Engineering Technology, https://polytechnic.purdue.edu/profile/meaguirr

Outside Collaborators:

Richard Voyles, School of Engineering Technology, Purdue University, https://web.ics.purdue.edu/~rvoyles/

Barragan, J. A., Yang, J., Yu, D., & Wachs, J. P. (2022). A neurotechnological aid for semi-autonomous suction in robotic-assisted surgery. Scientific Reports, 12(1), 4504. doi:10.1038/s41598-022-08063-w

Wu, C., Cha, J., Sulek, J., Zhou, T., Sundaram, C. P., Wachs, J., & Yu, D. (2019). Eye-Tracking Metrics Predict Perceived Workload in Robotic Surgical Skills Training. Human factors, 62(8), 1365-1386. doi:10.1177/0018720819874544

Aguirre, M., Steinórsson, Á. T., Horeman, T., and Herder, J. (June 1, 2015). "Technology Demonstrator for Compliant Statically Balanced Surgical Graspers." ASME. J. Med. Devices. June 2015; 9(2): 020926. https://doi.org/10.1115/1.4030131

M. E. Aguirre, K. D. Kommuri, D. J. Isbister and J. A. Gallego, "Multi-Modal Mechanism for Enhancing Haptics and Safety in Handheld Surgical Grasping," 2022 IEEE Haptics Symposium (HAPTICS), 2022, pp. 1-6, doi: 10.1109/HAPTICS52432.2022.9765596.

M.E. Aguirre, T. Horeman, Mechanical End-effector. US patent number: US10881421B2