Inverse models and robust parametric-step neuro-control of a Humanoid Robot

In this paper we present advances on the work done on the development of the AH1N2 humanoid robot at the Automatic Control Department of the Cinvestav. The geometric model of the AH1N2 humanoid robot is defined by kinematic open chains, head, arms, waist and legs, attached to the robot body. We center our work in the inverse geometric model, since from all the models used in robotics, is the most difficult to automate. Its knowledge is needed to control the robot position and attitude in the workspace. We present derivations of the inverse geometric models for the arm and the legs. We also study, in this work, the singularities of the arms and legs because they affect its control in the workspace. We also present the use of kinematic model to built movement constraints that allow us the control of the motion control and specify complex movements. Finally, we use the dynamic models to calculate a Neuro proportional-derivative control and to simulate the robot movement in presence of a destabilising perturbations. The neural network is trained to compensate the effect of gravity.

[1]  Wisama Khalil,et al.  Modeling, Identification and Control of Robots , 2003 .

[2]  R. Paul.  Robot manipulators : mathematics, programming, and control : the computer control of robot manipulators , 1981 .

[3]  A. Davids.  Urban search and rescue robots: from tragedy to technology , 2002 .

[4]  Philippe Dubart,et al.  Novel Nuclear Measurements Technologies for Safety and Security , 2015 .

[5]  Hajime Asama,et al.  Development of open humanoid platform DARwIn-OP , 2011, SICE Annual Conference 2011.

[6]  Kurosh Madani,et al.  Multi-level cognitive machine-learning based concept for human-like "artificial" walking: Application to autonomous stroll of humanoid robots , 2011, Neurocomputing.

[7]  G. Oriolo,et al.  Robotics: Modelling, Planning and Control , 2008 .

[8]  David C. Slaughter,et al.  Autonomous robotic weed control systems: A review , 2008 .

[9]  Juan Manuel Ibarra Zannatha,et al.  Manipulation with the AH1N2 humanoid robot an underactuated/overactuated problem , 2015, CCE.

[10]  Ching-Chang Wong,et al.  Small-Size Humanoid Soccer Robot Design for FIRA HuroSot , 2010 .

[11]  D. Van Buren,et al.  A Robotic Wide‐Angle Hα Survey of the Southern Sky , 2001, astro-ph/0108518.

[12]  Nikolaos G. Tsagarakis,et al.  iCub: the design and realization of an open humanoid platform for cognitive and neuroscience research , 2007, Adv. Robotics.

[13]  Kenichi Ogawa,et al.  Honda humanoid robots development , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[14]  Cristina P. Santos,et al.  Combining central pattern generators and reflexes , 2015, Neurocomputing.

[15]  John J. Craig Zhu,et al.  Introduction to robotics mechanics and control , 1991 .

[16]  S. Harkema,et al.  Retraining the injured spinal cord , 2001, The Journal of physiology.

[17]  José Santos Reyes,et al.  Biped locomotion control with evolved adaptive center-crossing continuous time recurrent neural networks , 2012, Neurocomputing.

[18]  Kikuo Fujimura,et al.  The intelligent ASIMO: system overview and integration , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  Javier de Lope,et al.  A method to learn the inverse kinematics of multi-link robots by evolving neuro-controllers , 2009 .

[20]  Donald Lee Pieper.  The kinematics of manipulators under computer control , 1968 .

[21]  Robin R. Murphy,et al.  Trial by fire [rescue robots] , 2004, IEEE Robotics & Automation Magazine.

[22]  M. Spong,et al.  Robot Modeling and Control , 2005 .

[23]  Angela Davids,et al.  Urban Search and Rescue Robots: From Tragedy to Technology , 2002, IEEE Intell. Syst..

[24]  Qining Wang,et al.  Disturbance rejection of Central Pattern Generator based torque-stiffness-controlled dynamic walking , 2015, Neurocomputing.

[25]  H. JoséAntonioMartín,et al.  A method to learn the inverse kinematics of multi-link robots by evolving neuro-controllers , 2009, Neurocomputing.

[26]  José Blasco,et al.  Machine Vision System for Automatic Quality Grading of Fruit , 2003 .

[27]  Peter Rankin McCullough,et al.  A Robotic Wide-Angle Ha Survey of the Southern Sky , 2001 .

[28]  Nicolas Y. Masse,et al.  Reach and grasp by people with tetraplegia using a neurally controlled robotic arm , 2012, Nature.

[29]  Jacky Baltes,et al.  The Humanoid Leagues in Robot Soccer Competitions , 2009 .

[30]  Y. Ota.  Partner Robots – From Development to Business Implementation , 2012 .

[31]  Fredrik Rehnmark,et al.  Robonaut: NASA's Space Humanoid , 2000, IEEE Intell. Syst..

[32]  H. Hultborn,et al.  Spinal control of locomotion – from cat to man , 2007, Acta physiologica.

[33]  Jorge Angeles,et al.  Fundamentals of Robotic Mechanical Systems: Theory, Methods, and Algorithms , 1995 .

[34]  Suguru Arimoto,et al.  A New Feedback Method for Dynamic Control of Manipulators , 1981 .

[35]  Shunsuke Komizunai,et al.  Implementation of HOAP-2 Humanoid Walking Motion in OpenHRP Simulation , 2015, 2015 International Conference on Computing Communication Control and Automation.

[36]  Alain Liégeois,et al.  A study of multiple manipulator inverse kinematic solutions with applications to trajectory planning and workspace determination , 1986, Proceedings. 1986 IEEE International Conference on Robotics and Automation.

[37]  Sungho Jo,et al.  A neurobiological model of the recovery strategies from perturbed walking , 2007, Biosyst..

[38]  Jeffrey Abouaf,et al.  Trial by Fire: Teleoperated Robot Targets Chernobyl , 1998, IEEE Computer Graphics and Applications.

[39]  B. Roth,et al.  Inverse Kinematics of the General 6R Manipulator and Related Linkages , 1993 .

[40]  Giovanni Muscato,et al.  Volcanic Environments: Robots for Exploration and Measurement , 2012, IEEE Robotics & Automation Magazine.

[41]  David Wettergreen,et al.  Dante II: Technical Description, Results, and Lessons Learned , 1999, Int. J. Robotics Res..