Dissertations / Theses on the topic 'Human-inspired robotics'

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<br>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

Indiana University-Purdue University Indianapolis (IUPUI)<br>There have been many endeavors to design humanoid robots that have human characteristics such as dexterity, autonomy and intelligence. Humanoid robots are intended to cooperate with humans and perform useful work that humans can perform. The main advantage of humanoid robots over other machines is that they are flexible and multi-purpose. In this thesis, a human-like robotic arm is designed and used in a task which is typically performed by humans, namely, catching a ball. The robotic arm was designed to closely resemble a human arm, based on anthropometric studies. A rigid multibody dynamics software was used to create a virtual model of the robotic arm, perform experiments, and collect data. The inverse kinematics of the robotic arm was solved using a Newton-Raphson numerical method with a numerically calculated Jacobian. The system was validated by testing its ability to find a kinematic solution for the catch position and successfully catch the ball within the robot's workspace. The tests were conducted by throwing the ball such that its path intersects different target points within the robot's workspace. The method used for determining the catch location consists of finding the intersection of the ball's trajectory with a virtual catch plane. The hand orientation was set so that the normal vector to the palm of the hand is parallel to the trajectory of the ball at the intersection point and a vector perpendicular to this normal vector remains in a constant orientation during the catch. It was found that this catch orientation approach was reliable within a 0.35 x 0.4 meter window in the robot's workspace. For all tests within this window, the robotic arm successfully caught and dropped the ball in a bin. Also, for the tests within this window, the maximum position and orientation (Euler angle) tracking errors were 13.6 mm and 4.3 degrees, respectively. The average position and orientation tracking errors were 3.5 mm and 0.3 degrees, respectively. The work presented in this study can be applied to humanoid robots in industrial assembly lines and hazardous environment recovery tasks, amongst other applications.

Biologically inspired robotic systems are becoming increasingly popular, especially in the field of medical robotics, in which building robotic devices able to replicate the human behavior guarantees obtaining motor recovery, functional substitution or human-robot interaction as human-like as possible. It is widely recognized that robotic rehabilitation devices improve the performance of the rehabilitation therapy performed by a human therapist in terms of action repetition and accurate tracking of the desired trajectory. Taking advantage from the plasticity of the neuro-muscular system, a human-inspired robotic rehabilitation therapy helps patients to re-learn movements. In the field of upper limb prosthetics, since the aim of a prosthetic hand is to replace a human hand, the robotic device has to be not only functional, but also as similar as possible to the human one both from the morphological point of view and as regards movement naturalness. On the other hand, since grasping is one of the human skills that robotic researchers mostly attempt at imitating, in the development of new robotic hands, the inspiration to the human hand behavior is increasing. From the analysis of the grasping action performed by human beings and from the study of the anatomy of the human hand and of its behavior during grasping, it is possible to obtain useful information for developing human-like grasping algorithms so as to acquire a better knowledge of the hand kinematics in order to design new human-like robotic hands and new rehabilitation devices. The definition of the kinematic structure of the hand and of the fingers is, in fact, the basis for designing new dexterous robotic hands and devices devoted to interact with the human hand (such as rehabilitation devices). Therefore this work is focused on the study of the hand kinematics, providing the basis for a further study regarding the hand dynamics. All the experiments done are in fact adaptable for a future study of the hand dynamics. In assistive robotics, as well as in the field of hand prostheses, the ability of performing smooth movements and obtaining a stable grasp is essential. Therefore, one of the aims of this thesis is to develop a bio-inspired approach for posture prediction and finger trajectory planning with a robotic hand. In order to do that, the human grasping action has been deeply analyzed. It has been decomposed in three main phases: reaching, pre-shaping and grasping. In order to reduce the complexity of planning dexterous hand grasps, it is useful to find the best hand preshape: therefore, this work is focused on this grasping phase. An accurate analysis of anatomy, surgery and rehabilitation literature has been done. In order to confirm the literature results and to cope with the lack of information, e.g. about thumb behavior, different methods for acquiring information about the human hand behavior have been used. Some important features about grasping have been collected from the analysis of the data obtained from two different devices for movement analysis: the Vicon system and a sensorized glove (the CyberGlove). The hand joint behavior during the grasping action has been analyzed asking different subjects to realize four different grasping tasks. The selected tasks guarantee that the subjects pose the hand in the most commonly used configurations. The experiments were performed asking subjects to wear the CyberGlove or attaching on their hands markers visible by the Vicon cameras. The obtained data have been analyzed using different hand kinematic human-inspired models. In order to overcome the drawbacks of the motion analysis devices listed before (as the not completely natural movements performed wearing a data glove, the impossibility to use the CyberGlove from people of different hand sizes and the high cost of the Vicon system), and to obtain information about the hand movements, the Kinect motion sensing device has also been used. For determining the finger joint positions and trajectories during hand movements, a finger tracking algorithm for the Kinect camera has been implemented. Blue markers have been placed on the hand joints following the same configuration used in the experiments performed with the Vicon cameras. A coloured blob detection algorithm and a multiple object tracking algorithm based on particle filters and extended Kalman filter has been implemented. When observing the human grasping behavior, thanks to the input devices listed before, it has been possible to notice some common characteristics among different subjects. The literature results about the dependence of grasping shape on object properties and grip types have been confirmed. The relationship between hand joints for each subject and among different subject has been investigated. One of the obtained results has been finding a constant value of the hand aperture angle (the angle between thumb and index finger). Also the curvature of the fingers is constant among different subjects (related to hand dimensions). Therefore, on the basis of neurological studies and of the analysis of the obtained data, a bio-inspired algorithm for predicting the power-grip posture and planning the finger trajectory of a robotic hand has been developed. The method estimates the best joint hand configuration during diagonal and transverse volar grasp minimizing a purposely defined objective function given by the sum of the joint distances from the object center of rotation (COR). The developed grasping algorithm calculates the position of the fingers for grasping, finding the best hand configuration that ensures a stable human-like grasp. The implementation of the algorithm on a real robotic platform has validated its effectiveness. From the above discussion, it is clear that the aim of this work is to find a way of exploiting the knowledge about a natural system, namely the human hand, in order to design a robotic system. After investigating and understanding in depth the human grasping action, the obtained results have multiple applications such as: overcoming the structural lack of the actual robotic hands (for instance, the non opposable thumb); developing new interfaces for rehabilitation (the finger tracking algorithm developed for the Kinect motion sensing device could be a new rehabilitation interface with potential application in the rehabilitation field); developing bio-inspired approaches for posture prediction and finger trajectory planning in order to perform a stable human-like grasp with a robotic hand.

The thesis shows how to achieve bipedal robotic walking on flat-ground, up-slope and rough terrain by using Human-Inspired control. We begin by considering human walking data and find outputs (or virtual constraints) that, when calculated from the human data, are described by simple functions of time (termed canonical walking functions). Formally, we construct a torque controller, through model inversion, that drives the outputs of the robot to the outputs of the human as represented by the canonical walking function; while these functions fit the human data well, they do not apriori guarantee robotic walking (due to do the physical differences between humans and robots). An optimization problem is presented that determines the best fit of the canonical walking function to the human data, while guaranteeing walking for a specific bipedal robot; in addition, constraints can be added that guarantee physically realizable walking. We consider a physical bipedal robot, AMBER, and considering the special property of the motors used in the robot, i.e., low leakage inductance, we approximate the motor model and use the formal controllers that satisfy the constraints and translate into an efficient voltage-based controller that can be directly implemented on AMBER. The end result is walking on flat-ground and up-slope which is not just human-like, but also amazingly robust. Having obtained walking on specific well defined terrains separately, rough terrain walking is achieved by dynamically changing the extended canonical walking functions (ECWF) that the robot outputs should track at every step. The state of the robot, after every non-stance foot strike, is actively sensed and the new CWF is constructed to ensure Hybrid Zero Dynamics is respected in the next step. Finally, the technique developed is tried on different terrains in simulation and in AMBER showing how the walking gait morphs depending on the terrain.

One of the greatest challenges in controlling robotic hands is grasping and manipulating objects in unstructured and uncertain environments. Robotic hands are typically too rigid to react against unexpected impacts and disturbances in order to prevent damage. The human hands have great versatility and robustness due, in part, to the passive compliance and damping. Designing mechanical elements that are inspired by the nonlinear joint compliance of human hands is a promising solution to achieve human-like grasping and manipulation. However, the exact role of biomechanical elements in realizing joint stiffness is unknown. We conducted a series of experiments to investigate nonlinear stiffness and damping of the metacarpophalangeal (MCP) joint at the index finger. We designed a custom-made mechanism to integrate electromyography sensors (EMGs) and a motion capture system to collect data from 19 subjects. We investigated the relative contributions of muscle-tendon units and the MCP capsule ligament complex to joint stiffness with subject-specific modeling. The results show that the muscle-tendon units provide limited contribution to the passive joint compliance. This findings indicate that the parallel compliance, in the form of the capsule-ligament complex, is significant in defining the passive properties of the hand. To identify the passive damping, we used the hysteresis loops to investigate the energy dissipation function. We used symbolic regression and principal component analysis to derive and interpret the damping models. The results show that the nonlinear viscous damping depends on the cyclic frequency, and fluid and structural types of damping also exist at the MCP joint. Inspired by the nonlinear stiffness of the MCP joint, we developed a miniaturized mechanism that uses pouring liquid plastic to design energy storing elements. The key innovations in this design are: a) a set of nonlinear elasticity of compliant materials, b) variable pulley configurations to tune the stiffness profile, and c) pretension mechanism to scale the stiffness profile. The design exhibits human-like passive compliance. By taking advantage of miniaturized joint size and additive manufacturing, we incorporated the novel joint design in a novel robotic manipulator with six series elastic actuators (SEA). The robotic manipulator has passive joint compliance with the intrinsic property of human hands. To validate the system, we investigated the Cartesian stiffness of grasping with low-level force control. The results show that that the overall system performs a great force tracking with position feedback. The parallel compliance decreases the motor efforts and can stabilize the system.<br>text