Trajectory clustering for motion prediction

Cynthia Sung, Dan Feldman, Daniela Rus

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


We investigate a data-driven approach to robotic path planning and analyze its performance in the context of interception tasks. Trajectories of moving objects often contain repeated patterns of motion, and learning those patterns can yield interception paths that succeed more often. We therefore propose an original trajectory clustering algorithm for extracting motion patterns from trajectory data and demonstrate its effectiveness over the more common clustering approach of using k-means. We use the results to build a Hidden Markov Model of a target's motion and predict movement. Our simulations show that these predictions lead to more effective interception. The results of this work have potential applications in coordination of multi-robot systems, tracking and surveillance tasks, and dynamic obstacle avoidance.

Original languageEnglish
Title of host publication2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2012
Number of pages6
StatePublished - 2012
Externally publishedYes
Event25th IEEE/RSJ International Conference on Robotics and Intelligent Systems, IROS 2012 - Vilamoura, Algarve, Portugal
Duration: 7 Oct 201212 Oct 2012

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866


Conference25th IEEE/RSJ International Conference on Robotics and Intelligent Systems, IROS 2012
CityVilamoura, Algarve

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications


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