Human-Aware Robotic Assistant for Collaborative Assembly: Integrating Human Motion Prediction With Planning in Time
Name
2018_Unhelkar_Lasota_Shah_etal_RA_Letters.pdf
Description
Accepted version
Size
3.01 MB
Format
Adobe PDF
Checksum (MD5)
7af15af5c4db24b31fa9cf83b72add0b
Author(s)
Unhelkar, Vaibhav V.
Lasota, Przemyslaw A.
Tyroller, Quirin
Buhai, Rares-Darius
Marceau, Laurie
Deml, Barbara
Shah, Julie A.
Date Issued
March 2018
Journal
IEEE Robotics and Automation Letters
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Unhelkar, Vaibhav V. et al. "Human-Aware Robotic Assistant for Collaborative Assembly: Integrating Human Motion Prediction With Planning in Time." IEEE Robotics and Automation Letters 3, 3 (July 2018): 2394-2401 © 2016 IEEE
Version
Author's final manuscript
Abstract
Introducing mobile robots into the collaborative assembly process poses unique challenges for ensuring efficient and safe human-robot interaction. Current human-robot work cells require the robot to cease operating completely whenever a human enters a shared region of the given cell, and the robots do not explicitly model or adapt to the behavior of the human. In this work, we present a human-aware robotic system with single-axis mobility that incorporates both predictions of human motion and planning in time to execute efficient and safe motions during automotive final assembly. We evaluate our system in simulation against three alternative methods, including a baseline approach emulating the behavior of standard safety systems in factories today. We also assess the system within a factory test environment. Through both live demonstration and results from simulated experiments, we show that our approach produces statistically significant improvements in quantitative measures of safety and fluency of interaction. Keywords: Physical Human-Robot Interaction, Collaborative Robots, Assembly
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/lra.2018.2812906
