Gait planning for biped locomotion on slippery terrain

Martim Brandão, Kenji Hashimoto, José Santos-Victor, Atsuo Takanishi

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

10 Citations (Scopus)


We propose a new biped locomotion planning method that optimizes locomotion speed subject to friction constraints. For this purpose we use approximate models of required coefficient of friction (RCOF) as a function of gait. The methodology is inspired by findings in human gait analysis, where subjects have been shown to adapt spatial and temporal variables of gait in order to reduce RCOF in slippery environments. Here we solve the friction problem similarly, by planning on gait parameter space: namely foot step placement, step swing time, double support time and height of the center of mass (COM). We first used simulations of a 48 degrees-of-freedom robot to estimate a model of how RCOF varies with these gait parameters. Then we developed a locomotion planning algorithm that minimizes the time the robot takes to reach a goal while keeping acceptable RCOF levels. Our physics simulation results show that RCOF-aware planning can drastically reduce slippage amount while still maximizing efficiency in terms of locomotion speed. Also, according to our experiments human-like stretched-knees walking can reduce slippage amount more than bent-knees (i.e. crouch) walking for the same speed.

Original languageEnglish
Title of host publication2014 IEEE-RAS International Conference on Humanoid Robots, Humanoids 2014
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)9781479971749
Publication statusPublished - 2015 Feb 12
Event2014 14th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2014 - Madrid, Spain
Duration: 2014 Nov 182014 Nov 20

Publication series

NameIEEE-RAS International Conference on Humanoid Robots
ISSN (Print)2164-0572
ISSN (Electronic)2164-0580


Other2014 14th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2014

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Human-Computer Interaction
  • Electrical and Electronic Engineering


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