A Human-Robot Skill Transfer Framework of Mobile Medical Robots for Autonomous Motion with Teaching by DemonstrationShow others and affiliations
2020 (English)In: Proceedings ICARM 2020 - 2020 5th IEEE International Conference on Advanced Robotics and Mechatronics, Institute of Electrical and Electronics Engineers (IEEE) , 2020, p. 209-213Conference paper, Published paper (Refereed)
Abstract [en]
The accuracy of autonomous motion is the main challenge for mobile medical robots, especially running in narrow space conditions to transport the casualties in the hospital. This article mainly centers on the human-robot transmission of the mobile medical robot for narrow space autonomous motion. In order to enable mobile medical robots to autonomously and smoothly transport hospital casualties through narrow aisles, in this paper, we present a robot skill transfer technology, namely a human-robot skill transfer framework of mobile medical robots for autonomous motion with teaching by demonstration, which is that a smooth optimal path is planned through the human motion, and then the Kinect sensor is used to detect human bone movement to learn its motion trajectory so as to achieve trajectory tracking control. Meanwhile, using the Remote Center of Motion (RCM) constraint to minimize the error between the actual trajectory of the medical robot and the tracking reference path to control the motion trajectory accurately, human-computer interactive mobile medical robots can smoothly pass through narrow channels. Through research and analysis, it is demonstrated that the mobile medical robot proposed in this paper has great feasibility, which is of great reference value in the field of medical rescue.
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2020. p. 209-213
Keywords [en]
Agricultural robots, Hospitals, Motion tracking, Robot programming, Robotics, Robots, Trajectories, Kinect sensors, Motion trajectories, Reference path, Reference values, Remote center of motions, Research and analysis, Teaching by demonstration, Trajectory tracking control, Medical robotics
National Category
Computer graphics and computer vision
Identifiers
URN: urn:nbn:se:kth:diva-313541DOI: 10.1109/ICARM49381.2020.9195398ISI: 000728183800039Scopus ID: 2-s2.0-85092611912OAI: oai:DiVA.org:kth-313541DiVA, id: diva2:1669359
Conference
5th International Conference on Advanced Robotics and Mechatronics, ICARM 2020, Shenzhen, China, December 18-21, 2020
Note
Part of ISBN 9781728164793
QC 20220614
2022-06-142022-06-142025-02-07Bibliographically approved