Weight-adaptive walking of the passenger-carrying biped robot, HUBO FX-1

Jung Hoon Kim, Jung Yup Kim, Jun Ho Oh

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

3 Citations (Scopus)

Abstract

In a passenger-carrying walking humanoid robot, it is preferable to use the walking pattern generation and balance control method that can accommodate variable passenger weights. This is because the range of possible payloads for passenger weight is relatively wide, from zero to approximately one hundred kgf. If the variable weight is not considered, the walking performance and walking stability of the robot decrease when the passenger's weight was much heavier or lighter than the predefined passenger weight. Therefore, in this paper, the walking pattern generation and ZMP control methods are developed to adaptively cope with variable passenger weights. The walking pattern generation method using the convolution sum can effectively calculate the walking pattern for variable passenger weights since it contains the analytic function of variable mass. The ZMP controller that performs the real-time sensor feedback control is designed as state space forms where the gains can be easily changed for variable passenger weights. The experiments show that the walking performance of the proposed method is well maintained and superior to those of the non-adaptive controllers for three different payload levels.

Original languageEnglish
Title of host publication2010 10th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2010
Pages33-39
Number of pages7
DOIs
Publication statusPublished - 2010
Event2010 10th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2010 - Nashville, TN, United States
Duration: 2010 Dec 62010 Dec 8

Publication series

Name2010 10th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2010

Other

Other2010 10th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2010
CountryUnited States
CityNashville, TN
Period10/12/610/12/8

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Hardware and Architecture
  • Human-Computer Interaction

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