Generation of the whole-body motion for humanoid robots with the complete dynamics Oscar Efrain Ramos Ponce To cite this version: Oscar Efrain Ramos Ponce. Generation of the whole-body motion for humanoid robots with the complete dynamics. Robotics [cs.RO]. Universite Toulouse III Paul Sabatier, 2014. English. tel- 01134313 HAL Id: tel-01134313 https://tel.archives-ouvertes.fr/tel-01134313 Submitted on 23 Mar 2015 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Christine CHEVALLEREAU: Directeur de Recherche, École Centrale de Nantes, France Francesco NORI: Researcher, Italian Institute of Technology, Italy Patrick DANÈS: Professeur des Universités, Université de Toulouse III, France Ludovic RIGHETTI: Researcher, Max-Plank-Institute for Intelligent Systems, Germany Nicolas MANSARD: Chargé de Recherche, LAAS-CNRS, France Philippe SOUÈRES: Directeur de recherche, LAAS-CNRS, France Yuval TASSA: Researcher, University of Washington, USA Abstract This thesis aims at providing a solution to the problem of motion generation for humanoid robots. The proposed framework generates whole-body motion using the complete robot dy- namics in the task space satisfying contact constraints. This approach is known as operational- space inverse-dynamics control. The specification of the movements is done through objectives in the task space, and the high redundancy of the system is handled with a prioritized stack of tasks where lower priority tasks are only achieved if they do not interfere with higher priority ones. To this end, a hierarchical quadratic program is used, with the advantage of being able to specify tasks as equalities or inequalities at any level of the hierarchy. Motions where the robot sits down in an armchair and climbs a ladder show the capability to handle multiple non-coplanar contacts. The generic motion generation framework is then applied to some case studies using HRP-2 and Romeo. Complex and human-like movements are achieved using human motion imita- tion where the acquired motion passes through a kinematic and then dynamic retargeting processes. To deal with the instantaneous nature of inverse dynamics, a walking pattern gen- erator is used as an input for the stack of tasks which makes a local correction of the feet position based on the contact points allowing to walk on non-planar surfaces. Visual feedback is also introduced to aid in the walking process. Alternatively, for a fast balance recovery, the capture point is introduced in the framework as a task and it is controlled within a desired region of space. Also, motion generation is presented for CHIMP which is a robot that needs a particular treatment. Résumé Cette thèse propose une solution au problème de la génération de mouvements pour les robots humanoïdes. Le cadre qui est proposé dans cette thèse génère des mouvements corps-complet en utilisant la dynamique inverse avec l’espace des tâches et en satisfaisant toutes les con- traintes de contact. La spécification des mouvements se fait à travers objectifs dans l’espace des tâches et la grande redondance du système est gérée avec une pile de tâches où les tâches moins prioritaires sont atteintes seulement si elles n’interfèrent pas avec celles de plus haute priorité. À cette fin, un QP hiérarchique est utilisé, avec l’avantage d’être en mesure de pré- ciser tâches d’égalité ou d’inégalité à tous les niveaux de la hiérarchie. La capacité de traiter plusieurs contacts non-coplanaires est montrée par des mouvements où le robot s’assoit sur une chaise et monte une échelle. Le cadre générique de génération de mouvements est ensuite appliqué à des études de cas à l’aide de HRP-2 et Romeo. Les mouvements complexes et similaires à l’humain sont obtenus en utilisant l’imitation du mouvement humain où le mouvement acquis passe par un processus cinématique et dynamique. Pour faire face à la nature instantanée de la dynamique inverse, un générateur de cycle de marche est utilisé comme entrée pour la pile de tâches qui effectue une correction locale de la position des pieds sur la base des points de contact permettant de marcher sur un terrain accidenté. La vision stéréo est également introduite pour aider dans le processus de marche. Pour une récupération rapide d’équilibre, le capture point est utilisé comme une tâche contrôlée dans une région désirée de l’espace. En outre, la génération de mouvements est présentée pour CHIMP, qui a besoin d’un traitement particulier. Acknowledgements In the first place, I would like to thank my Ph.D. advisors Nicolas Mansard and Philippe Souères for their guidance during my thesis work. Their experience, knowledge and contin- uous advices have allowed me to become a better researcher and have ultimately made this thesis possible. I am specially grateful to Nicolas, who has always supported me on many scientific and technical topics since I arrived to LAAS-CNRS, and to whom I owe most of the achievements of my work. Thanks to Francesco Nori and Christine Chevallereau for accepting to examine this thesis and to be part of the jury. Their constructive feedback and their close insights have been valuable for the improvement of the manuscript. Thanks to Ludovic Righetti and Patrick Danès for accepting to be members of the jury, for their interest in my work and for their feedback on the thesis. Many thanks to Siddhartha Srinivasa for receiving me during my stay at Carnegie Mellon University, in Pittsburgh. Special thanks to Clark Haynes, who gave me many insights, advice and support on the work with CHIMP. Thanks to Christopher Dellin, Kyle Strabala, José Gonzalez Mora, Mike Vande-Weghe, David Stager, and all the other members of the Tartan Rescue Team with whom I had the pleasure to work. Thanks for the great experience at the DARPA Robotics Challenge (DRC) Trials in Miami. I would also like to thank Abderrahmane Kheddar for receiving me at the French-Japanese Joint Robotics Lab (JRL) during my short stay at AIST in Tsukuba, Japan. I sincerely thank the great researchers in the Gepetto team at LAAS-CNRS who, in one or other way, have contributed to my achievements. Thanks to Jean-Paul Laumond, the former head of the team who gave me the opportunity to be part of it. Thanks to Florent Lamiraux, who gave me support with the software framework. Special thanks to Olivier Stasse, with whom I had the pleasure to work for the Novela project, and whose vast experience with the robot has made possible the experiments with HRP-2. I would also like to thank the former and current non-permanent members of the Gepetto Team. Many thanks to Layale Saab, who started the work on inverse dynamics control, to Sovannara Hak, whose expertise on motion capture was fundamental for motion imita- tion, to Mauricio García, whose work on stereo vision was useful for one part of this thesis, and to Andrea del Prete, with whom I have been working by the end of the thesis towards the real-time implementation of the stack of tasks. Thanks to the people in my last office, iv namely Naoko Abe, Mukunda Bharatheesha, and Joseph Mirabel, who provided a suitable and friendly environment as well as interesting discussions. Thanks to Valentin Aurousseau, Renliw Fleurmond, Antonio El-Khoury, Kai Henning Koch, Olivier Roussel, Andreas Orthey, Justin Carpentier, Ganesh Kumar, Maximilien Naveau, Nirmal Giftsun, Mehdi Benallegue, Ixchel Ramirez, Galo Maldonado, and the other members of the team for providing a very friendly environment in the laboratory. Finally, my deepest thanks go out to my parents for their constant support, advice, love and help not only during the development of this thesis, but throughout all of my life. Thanks for always being there when I most needed it, in spite of the distance; thanks for all the encouragements at all times; thanks for all the beliefs I was always taught. And, overall, thanks God for the life and for always giving me the strength to keep forward despite all the difficulties. Contents 1 Introduction 1 1.1 Problem Statement ................................. 1 1.2 Chapter Organization ............................... 2 1.3 Publications ..................................... 2 2 State of the Art 4 2.1 Introduction ..................................... 4 2.1.1 Service Robotics .............................. 5 2.1.2 Why Humanoid Robots? .......................... 5 2.1.3 History of Humanoid Robots ....................... 7 2.1.4 Challenges .................................. 9 2.1.5 Applications of Humanoid Robots .................... 10 2.2 Approaches in the Control of Humanoid Robots ................. 11 2.2.1 Motion Planning .............................. 11 2.2.2 Inverse Kinematics (IK) .......................... 14 2.2.3 Inverse Dynamics .............................. 17 2.2.4 Optimal Control .............................. 20 2.3 Conclusion ...................................... 22 3 Inverse-Dynamics Whole Body Motion 24 3.1 Dynamic Considerations for a Humanoid Robot ................. 25 3.1.1 Rigid Contact Constraints ......................... 25 3.1.2 The Zero-Moment Point (ZMP) ...................... 28 3.1.3 Dynamic Model of a Robot ........................ 29 3.1.4 Representation of a Humanoid Robot ................... 31 3.1.5 Dynamic Model of a Humanoid Robot .................. 32 3.2 Task Function Approach .............................. 34 3.2.1 Generic Formulation ............................ 34 3.2.2 Inverse Kinematics Case .......................... 35 3.2.3 Inverse Dynamics Case ........................... 36 3.3 Inverse Dynamics Control ............................. 37 3.3.1 Inverse Dynamics Problem ......................... 37 3.3.2 Hierarchical Quadratic Program (HQP) ................. 39 Contents vi 3.3.3 Inverse Dynamics Stack of Tasks (SoT) ................