QP-based adaptive-gains compliance control in humanoid falls

We address the problem of humanoid falling with a decoupled strategy consisting of a pre-impact and a postimpact stage. In the pre-impact stage, geometrical reasoning allows the robot to choose appropriate impact points in the surrounding environment and to adopt a posture to reach them while avoiding impact-singularities and preparing for the postimpact. The surrounding environment can be unstructured and may contain cluttered obstacles. The post-impact stage uses a quadratic program controller that adapts on-line the joint proportional-derivative (PD) gains to make the robot compliant-to absorb impact and post-impact dynamics, which lowers possible damage risks. This is done by a new approach incorporating the stiffness and damping gains directly as decision variables in the QP along with the usually-considered variables of joint accelerations and contact forces. Constraints of the QP prevent the motors from reaching their torque limits during the fall. Several experiments on the humanoid robot HRP-4 in a full-dynamics simulator are presented and discussed.

[1]  Eiichi Yoshida,et al.  Multi-contact vertical ladder climbing with an HRP-2 humanoid , 2016, Auton. Robots.

[2]  Nikolaos G. Tsagarakis,et al.  An active compliant impact protection system for humanoids: Application to WALK-MAN hands , 2016, 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids).

[3]  Shuuji Kajita,et al.  Towards an Optimal Falling Motion for a Humanoid Robot , 2006, 2006 6th IEEE-RAS International Conference on Humanoid Robots.

[4]  Abderrahmane Kheddar,et al.  Multi-Character Physical and Behavioral Interactions Controller , 2017, IEEE Transactions on Visualization and Computer Graphics.

[5]  Sung-Hee Lee,et al.  Fall on Backpack: Damage Minimization of Humanoid Robots by Falling on Targeted Body Segments , 2013 .

[6]  Kazuhito Yokoi,et al.  UKEMI: falling motion control to minimize damage to biped humanoid robot , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[7]  Roy Featherstone,et al.  Rigid Body Dynamics Algorithms , 2007 .

[8]  M. McCall,et al.  Rigid Body Dynamics , 2008 .

[9]  Paul H. J. Kelly,et al.  Dense planar SLAM , 2014, 2014 IEEE International Symposium on Mixed and Augmented Reality (ISMAR).

[10]  Sehoon Ha,et al.  Multiple contact planning for minimizing damage of humanoid falls , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[11]  Yasuo Kuniyoshi,et al.  Real-time selection and generation of fall damage reduction actions for humanoid robots , 2008, Humanoids 2008 - 8th IEEE-RAS International Conference on Humanoid Robots.

[12]  Abderrahmane Kheddar,et al.  Falls control using posture reshaping and active compliance , 2015, 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids).

[13]  Shuuji Kajita,et al.  Safe knee landing of a human-size humanoid robot while falling forward , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[14]  Yasuo Kuniyoshi,et al.  Falling motion control for humanoid robots while walking , 2007, 2007 7th IEEE-RAS International Conference on Humanoid Robots.

[15]  Umashankar Nagarajan,et al.  Direction-changing fall control of humanoid robots: theory and experiments , 2014, Auton. Robots.

[16]  Kaneko Kenji,et al.  Impact acceleration of falling humanoid robot with an airbag , 2016 .

[17]  Ambarish Goswami,et al.  Tripod fall: Concept and experiments of a novel approach to humanoid robot fall damage reduction , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[18]  Shuuji Kajita,et al.  An optimal planning of falling motions of a humanoid robot , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.