Thesis (M.S.)--Ohio University, June, 2001. / Title from PDF t.p.

The main goal of this thesis is to simulate the response of a humanoid robot using a specified control algorithm which can achieve a sustainable biped locomotion with 4 basic locomotion phases. Basic parts for the body of the humanoid robot model are shaped according to the specified basic physical parameters and assumed kinematic model. The kinematic model, which does not change according to locomotion phases and consists of 27 segments including 14 virtual segments, provides a humanoid robot model with 26 degrees of freedom (DOF). Corresponding kinematic relations for the robot model are obtained by recursive formulations. Derivation of dynamic equations is carried out by the Newton-Euler formulation. A trajectory definition algorithm which defines positions, orientations, translational and angular velocities for the hip and its mass center, toe part of the foot and its toe point is created. A control strategy based on predictive optimum command acceleration calculations and computed torque control method is implemented. The simulation is executed in Simulink and the visualization of the simulation is established in a virtual environment by Virtual Reality Toolbox of MATLAB. The simulation results and the user defined reference input are displayed simultaneously in the virtual environment. In this study, a simulation environment for the biped locomotion of humanoid robots is created. By the help of this thesis, the user can test various control strategies by modifying the modular structure of the simulation and acquire necessary information for the preliminary design study of a humanoid robot construction.

In this thesis, we introduce our lasso-type grasping scheme. This 3-D lasso-type grasping scheme, different from the previously performed grasping schemes which are either planar or fixed base, is the novelty of our approach where the snake robot grasps an object with any of its body links which are at close proximity to the object while undergoing its serpentine motion with the remaining links and dragging the grasped object. Since our snake robot has the pitch motion for every link, we can ensure that the links do not run into each other as they wrap around the object. A lasso-type power grasp is then possible for our 15-link snake robot as seen in the simulation results of this thesis. Furthermore we develop the kinematic and control models for lasso-type grasping and for dragging the grasped object to a desired state. This control model includes an adaptively changing feedback gain which prevents excessively large inputs to corrupt the serpentine locomotion control. According to our lasso-type grasping model, while the snake robot can grasp the object beginning with the any body link at close proximity of the object, the contact points can be controlled to generate the curvilinear grasping curve by using our lasso-type grasping procedure. For dragging the grasped object, we define a scheme which can determine the appropriate desired state to drag the grasped object to a desired position. The stability of the grasped object is important to resist the disturbance forces as well as the force closure grasping is important to counteract the disturbance force. To analyze the stability of the lasso-type grasping, we introduce a stability model of lasso-type grasping based on contact stiffness matrices that faces the snake to regrasp when gone unstable.

Cette thèse a été effectuée dans le cadre du projet européen Koroibot dont l'objectif est le développement d'algorithmes de marche avancés pour les robots humanoïdes. Dans le but de contrôler les robots d'une manière sûre et efficace chez les humains, il est nécessaire de comprendre les règles, les principes et les stratégies de l'homme lors de la locomotion et de les transférer à des robots. L'objectif de cette thèse est d'étudier et d'identifier les stratégies de locomotion humaine et créer des algorithmes qui pourraient être utilisés pour améliorer les capacités du robot. La contribution principale est l'analyse sur les principes de piétons qui guident les stratégies d'évitement des collisions. En particulier, nous observons comment les humains adapter une tâche de locomotion objectif direct quand ils ont à interférer avec un obstacle en mouvement traversant leur chemin. Nous montrons les différences entre la stratégie définie par les humains pour éviter un obstacle non-collaboratif et la stratégie pour éviter un autre être humain, et la façon dont les humains interagissent avec un objet si se déplaçant en manier simil à l'humaine. Deuxièmement, nous présentons un travail effectué en collaboration avec les neuroscientifiques de calcul. Nous proposons une nouvelle approche pour synthétiser réalistes complexes mouvements du robot humanoïde avec des primitives de mouvement. Trajectoires humaines walking-to-grasp ont été enregistrés. L'ensemble des mouvements du corps sont reciblées et proportionnée afin de correspondre à la cinématique de robots humanoïdes. Sur la base de cette base de données des mouvements, nous extrayons les primitives de mouvement. Nous montrons que ces signaux sources peuvent être exprimées sous forme de solutions stables d'un système dynamique autonome, qui peut être considéré comme un système de central pattern generators (CPGs). Sur la base de cette approche, les stratégies réactives walking-to-grasp ont été développés et expérimenté avec succès sur le robot humanoïde HRP-2 au LAAS-CNRS. Dans la troisième partie de la thèse, nous présentons une nouvelle approche du problème de pilotage d'un robot soumis à des contraintes non holonomes par une porte en utilisant l'asservissement visuel. La porte est représentée par deux points de repère situés sur ses supports verticaux. La plan géométric qui a été construit autour de la porte est constituée de faisceaux de hyperboles, des ellipses et des cercles orthogonaux. Nous montrons que cette géométrie peut être mesurée directement dans le plan d'image de la caméra et que la stratégie basée sur la vision présentée peut également être lié à l'homme. Simulation et expériences réalistes sont présentés pour montrer l'efficacité de nos solutions. / This thesis has been done within the framework of the European Project Koroibot which aims at developing advanced algorithms to improve the humanoid robots locomotion. It is organized in three parts. With the aim of steering robots in a safe and efficient manner among humans it is required to understand the rules, principles and strategies of human during locomotion and transfer them to robots. The goal of this thesis is to investigate and identify the human locomotion strategies and create algorithms that could be used to improve robot capabilities. A first contribution is the analysis on pedestrian principles which guide collision avoidance strategies. In particular, we observe how humans adapt a goal-direct locomotion task when they have to interfere with a moving obstacle crossing their way. We show differences both in the strategy set by humans to avoid a non-collaborative obstacle with respect to avoid another human, and the way humans interact with an object moving in human-like way. Secondly, we present a work done in collaboration with computational neuroscientists. We propose a new approach to synthetize realistic complex humanoid robot movements with motion primitives. Human walking-to-grasp trajectories have been recorded. The whole body movements are retargeted and scaled in order to match the humanoid robot kinematics. Based on this database of movements, we extract the motion primitives. We prove that these sources signals can be expressed as stable solutions of an autonomous dynamical system, which can be regarded as a system of coupled central pattern generators (CPGs). Based on this approach, reactive walking-to-grasp strategies have been developed and successfully experimented on the humanoid robot HRP at LAAS-CNRS. In the third part of the thesis, we present a new approach to the problem of vision-based steering of robot subject to non-holonomic constrained to pass through a door. The door is represented by two landmarks located on its vertical supports. The planar geometry that has been built around the door consists of bundles of hyperbolae, ellipses, and orthogonal circles. We prove that this geometry can be directly measured in the camera image plane and that the proposed vision-based control strategy can also be related to human. Realistic simulation and experiments are reported to show the effectiveness of our solutions.