Recent advances in surgical robotics attempt to overcome some of the limitations of manual surgery by augmenting the surgeon’s capabilities while performing suturing, incision, and retrieval tasks. In this regard, a primary research direction is focused on developing hand-held robots that replace manual surgical tools through motorized operations. Another complementary research direction addresses challenges arising from the limitations of the motor skills of surgeons. For example, surgeons need psychomotor skills to perform surgical tasks like laparoscopy, involving complex maneuvers requiring precision and dexterity. These skills are highly difficult to learn. The problem is compounded as surgeons must typically operate in a three-dimensional space, while guided by two-dimensional images. Robotic assisted surgery (RAS) has the potential to overcome these problems. Although current RAS technology takes more operative time than manual surgery, it offers benefits in other vital aspects: RAS enhances precision, flexibility, and control during the operation, offers reduced blood loss and shorter hospital stays, less postoperative pain, and lower risks of infection. Current RAS still depends on the surgeon’s skills to a large extent, and the outcome is highly variable because of the human factor. Autonomous robotic surgery can reduce human error, increase efficiency, and result in stable outcomes.
1. Hand-held Robotics for Specimen Retrieval Tasks in Laparoscopy
In this research, we explore novel approaches for specimen retrieval, a minimally invasive surgical procedure performed to diagnose and treat a myriad of medical pathologies in fields ranging from gynecology to oncology. The retrieval task typically involves extraction of a resected/excised 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 disease, infection, or malignancy. To address this challenge, we have developed a hand-held robot that achieves spillage-free retrieval of operative specimens, typically performed after their resection during laparoscopic surgery. Details on several aspects of the hand-held robot development including the design choices made, kinematics, actuation and drive-transmission mechanisms, end-effector mechanism, CAD modeling and 3D-printing of the mechanical structure, controller electronics, and PCB design/fabrication are described. Physical validation experiments were conducted to verify the functionality of different mechanisms of the robot. Further, specimen retrieval experiments were conducted with porcine meat samples to test the feasibility of the proposed design. Experimental results revealed that the robot was capable of retrieving specimens of masses ranging from 1 gram to 30 grams.
2. Autonomous Robotics for Minimally Invasive Surgery
In this research, we explore novel approaches for specimen retrieval, a minimally invasive surgical procedure performed to diagnose and treat a myriad of medical pathologies in fields ranging from gynecology to oncology. The retrieval task typically involves extraction of a resected/excised 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 disease, infection, or malignancy. To address this challenge, we have developed a hand-held robot that achieves spillage-free retrieval of operative specimens, typically performed after their resection during laparoscopic surgery. Details on several aspects of the hand-held robot development including the design choices made, kinematics, actuation and drive-transmission mechanisms, end-effector mechanism, CAD modeling and 3D-printing of the mechanical structure, controller electronics, and PCB design/fabrication are described. Physical validation experiments were conducted to verify the functionality of different mechanisms of the robot. Further, specimen retrieval experiments were conducted with porcine meat samples to test the feasibility of the proposed design. Experimental results revealed that the robot was capable of retrieving specimens of masses ranging from 1 gram to 30 grams.
In this research, we explore novel approaches for specimen retrieval, a minimally invasive surgical procedure performed to diagnose and treat a myriad of
Summary: Nature abounds in examples of swarming, a form of intelligent group behavior found in insect and animal societies.The swarm intelligence mechanisms found in the natural world can inspire synthetic algorithms that can be applied to diverse fields such as optimization, multi-agent decision making, and robotics.
1. Glowworm Swarm Optimization: Theory, Algorithms, and Applications
Glowworm swarm optimization (GSO) is a swarm intelligence algorithm introduced by Kaipa and Ghose in 2005. GSO is inspired by swarming behaviors in glowworms and originally developed for optimization and swarm robotics problems involving simultaneous capture of multiple optima of multimodal landscapes. The generality of the GSO algorithm is evident in its application to diverse problems ranging from engineering optimization and clustering to mobile sensor networks and distributed search using robotic swarms. The overwhelming reception of the algorithm by the research community propelled the authors to publish a book on the theory, algorithms, and applications of GSO, which embodies a valuable resource for researchers as well as graduate and undergraduate students working in the area of computational intelligence.
2. Data-driven Modeling to Enhance Search Efficiency of Glowworm Robot Swarm
In time-sensitive search and rescue applications, a team of multiple robots broadens the scope of operational capabilities. Scaling multi-robot systems (< 10 agents) to larger robot teams (10 – 100 agents) becomes challenging mainly in terms of online computational tractability. One solution to this problem is inspired by swarm intelligence principles found in nature, offering benefits of decentralized control, fault tolerance to individual failures, and self-organizing adaptability. Among various swarm algorithms, glowworm swarm optimization (GSO) is unique as it focuses on searching for multiple targets simultaneously. This research leverages Gaussian Process Models based data-driven modeling approaches to improve the search efficiency of GSO-based robotic swarms. We are focused on implementations in underwater robotic swarms for applications like pipeline inspection and search for plane crash sites, etc.
[1] K. N. Kaipa and D. Ghose. Glowworm Swarm Optimization: Theory, Algorithms, and Applications, Studies in Computational Intelligence, Vol. 698, Springer-Verlag,2017. ISBN: 978-3-319-51594-6 (Print) 978-3-319-51595-3 (Online).
[2] K. N. Kaipa and D. Ghose (2009). Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions. Swarm Intelligence, 3(2): 87―124.
[3] K. N. Kaipa and D. Ghose. Detection of multiple source locations using a glowworm metaphor with applications to collective robotics. IEEE Swarm Intelligence Symposium, Pasadena, California, USA, June 2005, pp. 84―91.
Summary
1. Glowworm Swarm Optimization: Theory, Algorithms, and Applications
Glowworm swarm optimization (GSO) is a swarm intelligence algorithm introduced by Kaipa and Ghose in 2005. GSO is inspired by swarming behaviors in glowworms and originally developed for optimization and swarm robotics problems involving simultaneous capture of multiple optima of multimodal landscapes. The generality of the GSO algorithm is evident in its application to diverse problems ranging from engineering optimization and clustering to mobile sensor networks and distributed search using robotic swarms. The overwhelming reception of the algorithm by the research community propelled the authors to publish a book on the theory, algorithms, and applications of GSO, which embodies a valuable resource for researchers as well as graduate and undergraduate students working in the area of computational intelligence.
1. A. Kondapalli, P. Nandi, and K. Kaipa, “Development of a three-dimensional simulator for integrated testing of path-planners and controllers for autonomous underwater vehicles,” In IEEE OES OCEANS Conference & Exposition, October 17-20, 2022.
2. M. J. Kuhlman, P. Svec, K. N. Kaipa, D. Sofge, and S. K. Gupta. Physics-aware informative coverage planning for autonomous vehicles. IEEE International Conference on Robotics and Automation (ICRA 2014), Hong Kong, China, May 31-June 7, 2014.
3. K. N. Kaipa and D. Ghose (2008). Theoretical foundations for rendezvous of glowworm-inspired agent swarms at multiple locations. Robotics and Autonomous Systems, 56(7): 549―569.
4. K. N. Kaipa, P. Amruth, M.H. Guruprasad, S. V. Bidargaddi, and D. Ghose. Glowworm-inspired robot swarm for simultaneous taxis towards multiple radiation sources. IEEE International Conference on Robotics and Automation (ICRA 2006), Orlando, Florida, USA, May 2006, pp. 958―963.
5. K. N. Kaipa and D. Ghose (2005). Formations of minimalist mobile robots using local-templates and spatially distributed interactions. Robotics and Autonomous Systems, 53(3―4): 194―213.
In this research, we explore novel methods that enable human-safe robots like KUKA and Sawyer to share workspace with other robots and/or humans and perform tasks collaboratively. Our focus is on non-repetitive and contact-based tasks like micro-drilling [case paper, michael’s thesis, TTR paper], automated fiber placement [SAMPE paper], finishing [], and bin-picking []. Other collaborative robotic tasks of current focus include robotic graphene spray and dynamic pouring [].
1. Robotic Micro-drilling
2. Robotic Automated Fiber Placement
3. Robotic Graphene Spray
4. Finishing
5. Bin-picking
6. Pouring
[1] K. N. Kaipa, A. S. Kankanhalli-Nagendra, N. B. Kumbla, S. Shriyam, S. S. Thevendria-Karthic, J. A. Marvel, and S. K. Gupta (2016). Addressing perception uncertainty induced failure modes in robotic bin-picking. Robotics and Computer Integrated Manufacturing 42(1), 17-38.
[2] A. Kabir, K. N. Kaipa, J. Marvel, and S. K. Gupta. Automated planning for robotic cleaning using multiple setups and oscillatory tool motions. IEEE Transactions on Automation Science and Engineering vol.PP, no.99, pp.1-14. doi: 10.1109/TASE.2017.2665460.
[3] J. D. Langsfeld, K. N. Kaipa, and S. K. Gupta. Selection of trajectory parameters for dynamic pouring tasks based on exploitation-driven updates of local metamodels, robotica. (Accepted).
[4] C. W. Morato, K. N. Kaipa, and S. K. Gupta. Toward safe human robot collaboration by using multiple Kinects based real-time human tracking (2014). ASME Journal of Computing and Information Science in Engineering, 14(1): 011006.
Autonomous Underwater robot
Jumping robot
Autonomous ground vehicle
Autonomous Drone Swarm
Balancing cube