Benchmarking the utility of maps of dynamics for human-aware motion planning

Chittaranjan Srinivas Swaminathan, Tomasz Piotr Kucner, Martin Magnusson, Luigi Palmieri, Sergi Molina, Anna Mannucci, Federico Pecora, Achim J. Lilienthal

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Robots operating with humans in highly dynamic environments need not only react to moving persons and objects but also to anticipate and adhere to patterns of motion of dynamic agents in their environment. Currently, robotic systems use information about dynamics locally, through tracking and predicting motion within their direct perceptual range. This limits robots to reactive response to observed motion and to short-term predictions in their immediate vicinity. In this paper, we explore how maps of dynamics (MoDs) that provide information about motion patterns outside of the direct perceptual range of the robot can be used in motion planning to improve the behaviour of a robot in a dynamic environment. We formulate cost functions for four MoD representations to be used in any optimizing motion planning framework. Further, to evaluate the performance gain through using MoDs in motion planning, we design objective metrics, and we introduce a simulation framework for rapid benchmarking. We find that planners that utilize MoDs waste less time waiting for pedestrians, compared to planners that use geometric information alone. In particular, planners utilizing both intensity (proportion of observations at a grid cell where a dynamic entity was detected) and direction information have better task execution efficiency.

Original languageEnglish
Article number916153
JournalFrontiers Robotics AI
Volume9
DOIs
StatePublished - 2 Nov 2022
Externally publishedYes

Keywords

  • ATC
  • benchmarking
  • dynamic environments
  • human-aware motion planning
  • human-populated environments
  • maps of dynamics

Fingerprint

Dive into the research topics of 'Benchmarking the utility of maps of dynamics for human-aware motion planning'. Together they form a unique fingerprint.

Cite this