Date of Award

Spring 5-2023

Document Type


Degree Name

Doctor of Philosophy (PhD)


Mechanical & Aerospace Engineering


Mechanical Engineering

Committee Director

Krishnanand Kaipa

Committee Member

Sebastian Bawab

Committee Member

Tian-Bing Xu

Committee Member

Jiang Li


Recent advances in surgical robotics attempt to overcome limitations of manual surgery by augmenting the surgeon’s capabilities while performing suturing, incision, retraction, and retrieval tasks. This dissertation presents novel approaches for spillage-free specimen retrieval in confined spaces, targeted toward the surgical domain of minimally invasive robotic surgery. The retrieval task involves extraction of a resected specimen, residing in the abdominal cavity, completely outside of the patient’s body. A major challenge in this context is the spillage of content being retrieved, which may cause dissemination of malignancy. To address this challenge, this dissertation develops RoboRetrieve, a portable hand-held robot that can be operated by a surgeon to perform spillage-free retrieval tasks. Specifically, RoboRetrieve enables a surgeon to deploy the needed surgical tools—atraumatic forceps and a specimen-retrieval bag—through a single-port into the abdomen and perform grasping and spillage-free retrieval of an excised specimen. Experimental results revealed that RoboRetrieve can manipulate porcine meat samples up to a mass of 100 g and perform spillage-free retrieval of phantom blood up to a volume of 1500 microliters. Further testing of RoboRetrieve is conducted in three experimental regimes—retrieval of a single tissue, multi-tissue retrieval within a single bag deployment, and retrieval in a realistic scenario using a laparoscopic simulator, emulating unconstrained/constrained abdomen environments. The second contribution of this dissertation aims to tackle challenges arising from the limitations of surgeons’ psychomotor skills involving complex maneuvers that are difficult to learn. This is achieved by augmenting RoboRetrieve with the KUKA LBR Med 7 R800 collaborative robot to automate the specimen-retrieval task. The integrated robotic system is tested in two experimental regimes—robotic specimen retrieval within the laparoscopic simulator and human-robot collaboration to achieve the resection and retrieval tasks in tandem. Finally, the imitation learning paradigm is explored through kinesthetic teaching and using dynamic movement primitives (DMPs) as a learning algorithm. The outcomes of these experiments aim to showcase the potential of the integrated system to automate spillage-free specimen retrieval tasks in minimally invasive surgery, leading to enhanced surgical efficiency and a decreased risk of errors.


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