Qualitative Constraints for Human-aware Robot Navigation using Velocity Costmaps

Christian Dondrup, Marc Hanheide

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)
53 Downloads (Pure)

Abstract

In this work, we propose the combination of a state-of-the-art sampling-based local planner with so-called Velocity Costmaps to achieve human-aware robot navigation. Instead of introducing humans as "special obstacles" into the representation of the environment, we restrict the sample space of a "Dynamic Window Approach" local planner to only allow trajectories based on a qualitative description of the future unfolding of the encounter.
To achieve this, we use a Bayesian temporal model based on a Qualitative Trajectory Calculus to represent the mutual navigation intent of human and robot, and translate these descriptors into sample space constraints for trajectory generation. We show how to learn these models from demonstration and evaluate our approach against standard Gaussian cost models in simulation and in real-world using a non-holonomic mobile robot. Our experiments show that our approach exceeds the performance and safety of the Gaussian models in pass-by and path crossing situations.
Original languageEnglish
Title of host publication25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)
PublisherIEEE
Pages586-592
Number of pages7
ISBN (Print)9781509039296
DOIs
Publication statusPublished - 17 Nov 2016
Event25th IEEE International Symposium on Robot and Human Interactive Communication 2016 - New York, United States
Duration: 26 Aug 201631 Aug 2016

Publication series

NameIEEE International Symposium on Robot and Human Interactive Communication
PublisherIEEE
ISSN (Print)1944-9437

Conference

Conference25th IEEE International Symposium on Robot and Human Interactive Communication 2016
Abbreviated titleRO-MAN 2016
CountryUnited States
CityNew York
Period26/08/1631/08/16

Keywords

  • Human-Aware Navigation
  • Qualitative Spatial Reasoning
  • Learning from Demonstration
  • Mobile robot
  • Navigation

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