Trajectory clustering for motion prediction
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traj.pdf
Description
Accepted version
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741.6 KB
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e9d631ac881d98878189bc1684030405
Author(s)
Sung, Cynthia Rueyi
Feldman, Dan
Rus, Daniela L
Date Issued
October 2012
Publisher
IEEE
Citation
Sung, Cynthia, Feldman, Dan and Rus, Daniela. 2012. "Trajectory clustering for motion prediction."
Version
Author's final manuscript
Abstract
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. © 2012 IEEE.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/iros.2012.6386017
