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Date:  Wed, June 24, 2020
Time:  1:00pm - 2:00pm
Location:  Online via Zoom, contact iychun@hawaii.edu or eeoffice@hawaii.edu for details
Speaker:  Anirban Sinha, PhD Candidate (ME, SUNY Stony Brook)

Abstract
Robotic assembly in tight tolerance situations in the presence of sensing and actuation uncertainties is a long standing and relevant problem to be solved to bring the robots in the factory floor and perform desired tasks successfully. Robot's actuation and sensing uncertainties ultimately result in compromised accuracy of the desired/commanded joint angle vector which in turn affects the end-effector’s positioning accuracy. Usually we have limited control over the hardware inaccuracies but exploiting the redundancy of the kinematic structure of a manipulator we can develop robust inverse kinematics (robust-IK) algorithm which will always ensure end-effector space error be bounded within a certain error margin. In the first part of my talk I will describe the design methodology of robust-IK algorithm and show its efficacy in increased success rate in pre-grasp positioning problem and in pre-insertion positioning problem in peg-in-hole assembly problem. Motion planning is also essential in assembly problems which brings the mating parts towards assembly states.  Traditionally this kind of problem is posed as taskspace trajectory following problem using an impedance controller. Further the motions of the mating parts are highly constrained and generating a reference trajectory is difficult in such situations in general. The second part of my talk addresses this problem while introducing our novel ScLERP based taskspace planner with complementarity based obstacle avoidance (ScComp). The main advantage of using ScComp lies in generating taskspace trajectories with end-effector constraints with an efficient and fast manner. Further the complementarity constraint based obstacle avoidance scheme takes care of the potential contacts of the arm link with environment obstacles while pushing the arm away from the obstacles. I will conclude the talk by discussing some of the future avenues of my research and preliminary results of on the going work.

Bio
Mr. Anirban Sinha is a Ph.D. candidate in the department of Mechanical Engineering of the State University of New York at Stony Brook. His research aim is to endow robot manipulators with self evaluation capability to quantify the confidence level of achieving a given task and executing the task reliably in the presence of sensing and actuation uncertainties. He is minoring in Applied Mathematics and Statistics. Before joining Ph.D. program at Stony Brook University, he worked as a research staff in the department of Aerospace Engineering of the Indian Institute of Science, Bangalore, India. He earned his Master's degree from Jadavpur University, Kolkata, India where he received scholarship from the Ministry of Human Resource development of India.

Host: Il Yong Chun (Assistant Professor of EE, UHM)