Prediction of a Ball Trajectory for the Humanoid Robots: A Friction-Based Study

Behnam Yazdankhoo, Mohammad Navid Shahsavari, Soroush Sadeghnejad*, Jacky Baltes

*Corresponding author for this work

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

1 Citation (Scopus)


Recent advances in robotics have made it necessary for robots to be able to predict actions like humans. This problem is well presented in international RoboCup competition leagues, especially for humanoid robots in challenges such as Goal-Kick from Moving Ball. In this paper, we proposed double exponential smoothing (DES), autoregressive (AR) and quadratic prediction (QP) as online methods and self-perturbing recursive least squares (SPRLS) as an offline method for prediction of the ball trajectory on ground. These prediction methods are compared in two scenarios by applying LuGre friction model. We simulated our proposed methods by Simmechanics library of MATLAB’s Simulink. By comparing results using root-mean-square error and normalized root-mean-square error, we could deduce that methods that were based on predefined models such as QP performed poorly when the friction deviated from the presumed model. Whereas numerical methods such as AR could adapt themselves to variation much better, depending on the friction force variation with time. Also offline methods such as SPRLS are good replacements for online ones when pre-training is possible.

Original languageEnglish
Title of host publicationRoboCup 2018
Subtitle of host publicationRobot World Cup XXII
EditorsDirk Holz, Katie Genter, Maarouf Saad, Oskar von Stryk
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783030275433
Publication statusPublished - 2019
Event22nd RoboCup International Competition and Symposium, RoboCup 2018 - Montréal, Canada
Duration: 2018 Jun 182018 Jun 22

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11374 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd RoboCup International Competition and Symposium, RoboCup 2018


  • Autoregressive
  • Ball trajectory prediction
  • Exponential smoothing
  • Goal-Kick from Moving Ball
  • Humanoid robots
  • LuGre model

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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