Autonomous Robots

, Volume 41, Issue 1, pp 259–272 | Cite as

High-slope terrain locomotion for torque-controlled quadruped robots

  • Michele Focchi
  • Andrea del Prete
  • Ioannis Havoutis
  • Roy Featherstone
  • Darwin G. Caldwell
  • Claudio Semini
Article

Abstract

Research into legged robotics is primarily motivated by the prospects of building machines that are able to navigate in challenging and complex environments that are predominantly non-flat. In this context, control of contact forces is fundamental to ensure stable contacts and equilibrium of the robot. In this paper we propose a planning/control framework for quasi-static walking of quadrupedal robots, implemented for a demanding application in which regulation of ground reaction forces is crucial. Experimental results demonstrate that our 75-kg quadruped robot is able to walk inside two high-slope (\(50^\circ \)) V-shaped walls; an achievement that to the authors’ best knowledge has never been presented before. The robot distributes its weight among the stance legs so as to optimize user-defined criteria. We compute joint torques that result in no foot slippage, fulfillment of the unilateral constraints of the contact forces and minimization of the actuators effort. The presented study is an experimental validation of the effectiveness and robustness of QP-based force distributions methods for quasi-static locomotion on challenging terrain.

Keywords

Whole-body control Multi-contact inter-action Quadruped locomotion Ground Reaction Force optimization Force control 

Notes

Acknowledgments

This research has been funded by the Fondazione Istituto Italiano di Tecnologia.

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Michele Focchi
    • 1
  • Andrea del Prete
    • 2
  • Ioannis Havoutis
    • 3
  • Roy Featherstone
    • 1
  • Darwin G. Caldwell
    • 1
  • Claudio Semini
    • 1
  1. 1.Department of Advanced RoboticsIstituto Italiano di TecnologiaGenoaItaly
  2. 2.LAAS-CNRSToulouseFrance
  3. 3.Robot Learning & Interaction GroupIdiap Research InstituteMartignySwitzerland

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