Following Task-Space Paths for Robots - Computational Geometry Meets Surgical Robots

GRAND Seminar Friday, October 5th, 1pm, Room: 4201

Oren Salzman
Postdoctoral Researcher
Robotics Institute
Carnegie Mellon University

Abstract:

In recent years, robots have played an active role in everyday life: medical robots assist in complex surgeries, low-cost commercial robots clean houses and fleets of robots are used to efficiently manage warehouses. A key challenge in these systems is motion planning, where we are interested in planning a collision-free path for a robot in an environment cluttered with obstacles. While the general problem has been studied for several decades now, these new applications introduce an abundance of new challenges.

In this talk I will describe some of these challenges as well as algorithms developed to address them. Specifically, I will concentrate on the problem of following paths in task-space (the space defined by the robot's end effector). Here, the objective is to compute a path in the configuration space (C-space), the space defined by the position and orientation of each of the robot’s joints, “as best as possible”. We are motivated by settings where the taskspace path is provided, but it is not clear if there exists a collision-free C-space path that exactly traces it. One example where such a setting occurs is when robot manipulators operate in household environments performing tasks such as serving a cup of coffee. In order not to spill the coffee, the robot’s end-effector has to stay roughly upright. A second example that motivates our work comes from medical robots such as steerable needles and concentric tube robots. In this application, we envision the surgeon providing the reference path in task space and our algorithm producing the C-space path for either the tip of the tube robot or the bevel edge of the steerable needle that follows the reference path.

This talk assumes no prior knowledge and is based on work done in collaboration with Rachel Holladay, Sherdil Niyaz, Alan Kuntz, Ron Alterovitz and Siddhartha Srinivasa.

Short Bio:

Oren Salzman completed a PhD in the School of Computer Science at Tel Aviv University under the supervision of Prof. Dan Halperin. He is currently a postdoctoral researcher at Carnegie Mellon University working with Siddhartha Srinivasa and Maxim Likhachev. His research focuses on revisiting classical computer science algorithms, tools and paradigms to address the computational challenges that arise when planning motions for robots. Combining techniques from diverse domains such as computational geometry, graph theory and machine learning, he strives to provide efficient algorithms with rigorous analysis for robot systems with many degrees of freedom moving in tight quarters. He earned his BSc with honors from the Technion and his MSc with honors from Tel Aviv University.