Investigation of motion guidance with scooter cobot and collaborative learning

Eng Seng Boy*, Etienne Burdet, Chee Leong Teo, James Edward Colgate

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

This paper investigates how collaborative robots (cobots) can assist a human by mechanically constraining motion to software-defined guide paths, and introduces simple and efficient tools to design ergonomic paths. Analysis of the movements of seven subjects with the Scooter cobot reveals significant differences between guided movements (GM) and free movements (FM). While FM requires learning for each novel task, movements in GM are satisfying from the first trial, require little effort, are faster, smoother, and with fewer back and forth corrections than in FM. Operators rely on path guidance to rotate the Scooter and direct it along curved trajectories. While these advantages demonstrate the strength of the cobot concept, they do not show how guide paths should be defined. We introduce tools to enable the cobot and its operator to collaboratively learn ergonomic guide paths and adapt to changes in the environment. By relying on the haptic sensing, vision, and planning capabilities of the human operator, we can avoid equipping the cobot with complex sensor processing. Experiments with human subjects demonstrate the efficiency and complementarity of these guide paths design tools.

Original languageEnglish (US)
Pages (from-to)245-255
Number of pages11
JournalIEEE Transactions on Robotics
Volume23
Issue number2
DOIs
StatePublished - Apr 2007

Keywords

  • Assistive devices
  • Effort
  • Ergonomics
  • Haptics
  • Human-machine interaction
  • Motion guidance

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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