Publication

Shared task representation for human–robot collaborative navigation: The collaborative search case

Journal Article (2024)

Journal

International Journal of Social Robotics

Pages

145-171

Volume

16

Doc link

https://doi.org/10.1007/s12369-023-01067-0

File

Download the digital copy of the doc pdf document

Abstract

Recent research in Human Robot Collaboration (HRC) has spread and specialised in many sub-fields. Many show considerable advances, but the human–robot collaborative navigation (HRCN) field seems to be stuck focusing on implicit collaboration settings, on hypothetical or simulated task allocation problems, on shared autonomy or on having the human as a manager. This work takes a step forward by presenting an end-to-end system capable of handling real-world human–robot collaborative navigation tasks. This system makes use of the Social Reward Sources model (SRS), a knowledge representation to simultaneously tackle task allocation and path planning, proposes a multi-agent Monte Carlo Tree Search (MCTS) planner for human–robot teams, presents the collaborative search as a testbed for HRCN and studies the usage of smartphones for communication in this setting. The detailed experiments prove the viability of the approach, explore collaboration roles adopted by the human–robot team and test the acceptability and utility of different communication interface designs.

Categories

automation.

Author keywords

Human–robot collaboration · Human–robot collaborative navigation · Human–robot interaction · Multi-agent planning · Motion planning · Task representation · Object search

Scientific reference

M. Dalmasso, J.E. Domínguez, I.J. Torres, P. Jiménez, A. Garrell Zulueta and A. Sanfeliu. Shared task representation for human–robot collaborative navigation: The collaborative search case. International Journal of Social Robotics, 16: 145-171, 2024.