College - Author 1
College of Engineering
Department - Author 1
Computer Science Department
Advisor
Ka Yaw Teo, College of Engineering, Computer Science and Software Engineering
Funding Source
This research was funded by Cal Poly's Computer Science and Software Engineering Department
Date
10-2025
Abstract/Summary
Slender, multi-link, highly articulated, and extensible robots designed for minimally invasive surgeries have the potential to significantly transform the performance of common medical procedures. These advanced robots can reduce uncertainties and risks associated with surgeries, leading to shorter patient recovery times, accelerated healing, and minimized scarring. Made possible by their numerous mechanical linkages and concentric mechanisms, these multi-link articulated robots can navigate along non-linear paths, a capability that traditional straight probes lack. This flexibility allows surgeons to perform minimally invasive procedures on clinically significant targets that were previously difficult or impossible to access while avoiding vital anatomical structures. Beyond their applications in medicine, the potential uses of these robots span various fields that may require non-linear trajectories and navigation to reach difficult-to-access targets. However, manually controlling such devices can be quite unintuitive due to the various kinematic constraints involved, highlighting the need for automatic planning methods. To fully harness the capabilities of these multi-link robots, automation is key. For any automated medical procedure to gain acceptance in clinical settings, it is crucial -- from the perspectives of patient care, safety, and regulatory compliance -- to certify the accuracy and effectiveness of the motion-planning algorithms used in the automation process. In this context, our proposed study will focus on developing accurate and efficient motion planners specifically for extensible snake-like robots. We will model the articulated motion of these robots using a recursively defined trajectory consisting of: i) Base step: Inserting a link into the workspace. ii) Recursive step: Extending the end link by a given distance and rotating the new extension. The goal is to guide the endpoint of the last link to a target point within the workspace while avoiding obstacles. Our study will center on two main research questions: 1) (Completeness) Can we devise a time- and space-efficient deterministic algorithm for computing a feasible trajectory when one exists? 2) (Optimality) Can we also ensure that the computed trajectory is optimal with respect to specific objectives, such as minimizing trajectory length and maximizing clearance from obstacles? Our goal is to create a deterministic motion planner that provides formal guarantees of both completeness and optimality. This planner should be capable of finding a solution in a finite number of steps for any given scenario while ensuring that the solution meets a desired cost metric.
October 1, 2025.
Included in
URL: https://digitalcommons.calpoly.edu/ceng_surp/169