Advanced human inspired walking strategies for humanoid robots

Cette thèse traite du problème de la locomotion des robots humanoïdes dans le contexte du projet européen KoroiBot. En s'inspirant de l'être humain, l'objectif de ce projet est l'amélioration des capacités des robots humanoïdes à se mouvoir de façon dynamique et polyvalente. Le coeur de l'approche scientifique repose sur l'utilisation du controle optimal, à la fois pour l'identification des couts optimisés par l'être humain et pour leur mise en oeuvre sur les robots des partenaires roboticiens. Cette thèse s'illustre donc par une collaboration à la fois avec des mathématiciens du contrôle et des spécialistes de la modélisation des primitives motrices. Les contributions majeures de cette thèse reposent donc sur la conception de nouveaux algorithmes temps-réel de contrôle pour la locomotion des robots humanoïdes avec nos collégues de l'université d'Heidelberg et leur intégration sur le robot HRP-2. Deux contrôleurs seront présentés, le premier permettant la locomotion multi-contacts avec une connaissance a priori des futures positions des contacts. Le deuxième étant une extension d'un travail réalisé sur de la marche sur sol plat améliorant les performances et ajoutant des fonctionnalitées au précédent algorithme. En collaborant avec des spécialistes du mouvement humain nous avons implementé un contrôleur innovant permettant de suivre des trajectoires cycliques du centre de masse. Nous présenterons aussi un contrôleur corps-complet utilisant, pour le haut du corps, des primitives de mouvements extraites du mouvement humain et pour le bas du corps, un générateur de marche. Les résultats de cette thèse ont été intégrés dans la suite logicielle "Stack-of-Tasks" du LAAS-CNRS.This thesis covers the topic of humanoid robot locomotion in the frame of the European project KoroiBot. The goal of this project is to enhance the ability of humanoid robots to walk in a dynamic and versatile fashion as humans do. Research and innovation studies in KoroiBot rely on optimal control methods both for the identification of cost functions used by human being and for their implementations on robots owned by roboticist partners. Hence, this thesis includes fruitful collaborations with both control mathematicians and experts in motion primitive modeling. The main contributions of this PhD thesis lies in the design of new real time controllers for humanoid robot locomotion with our partners from the University of Heidelberg and their integration on the HRP-2 robot. Two controllers will be shown, one allowing multi-contact locomotion with a prior knowledge of the future contacts. And the second is an extension of a previous work improving performance and providing additional functionalities. In a collaboration with experts in human motion we designed an innovating controller for tracking cyclic trajectories of the center of mass. We also show a whole body controller using upper body movement primitives extracted from human behavior and lower body movement computed by a walking pattern generator. The results of this thesis have been integrated into the LAAS-CNRS "Stack-of-Tasks" software suit

An Object Template Approach to Manipulation for Semi-autonomous Avatar Robots

Nowadays, the first steps towards the use of mobile robots to perform manipulation tasks in remote environments have been made possible. This opens new possibilities for research and development, since robots can help humans to perform tasks in many scenarios. A remote robot can be used as avatar in applications such as for medical or industrial use, in rescue and disaster recovery tasks which might be hazardous environments for human beings to enter, as well as for more distant scenarios like planetary explorations. Among the most typical applications in recent years, research towards the deployment of robots to mitigate disaster scenarios has been of great interest in the robotics field. Disaster scenarios present challenges that need to be tackled. Their unstructured nature makes them difficult to predict and even though some assumptions can be made for human-designed scenarios, there is no certainty on the expected conditions. Communications with a robot inside these scenarios might also be challenged; wired communications limit reachability and wireless communications are limited by bandwidth. Despite the great progress in the robotics research field, these difficulties have prevented the current autonomous robotic approaches to perform efficiently in unstructured remote scenarios. On one side, acquiring physical and abstract information from unknown objects in a full autonomous way in uncontrolled environmental conditions is still an unsolved problem. Several challenges have to be overcome such as object recognition, grasp planning, manipulation, and mission planning among others. On the other side, purely teleoperated robots require a reliable communication link robust to reachability, bandwidth, and latency which can provide all the necessary feedback that a human operator needs in order to achieve sufficiently good situational awareness, e.g., worldmodel, robot state, forces, and torques exerted. Processing this amount of information plus the necessary training to perform joint motions with the robot represent a high mental workload for the operator which results in very low execution times. Additionally, a pure teleoperated approach is error-prone given that the success in a manipulation task strongly depends on the ability and expertise of the human operating the robot. Both, autonomous and teleoperated robotic approaches have pros and cons, for this reason a middle ground approach has emerged. In an approach where a human supervises a semi-autonomous remote robot, strengths from both, full autonomous and purely teleoperated approaches can be combined while at the same time their weaknesses can be tackled. A remote manipulation task can be divided into sub-tasks such as planning, perception, action, and evaluation. A proper distribution of these sub-tasks between the human operator and the remote robot can increase the efficiency and potential of success in a manipulation task. On the one hand, a human operator can trivially plan a task (planning), identify objects in the sensor data acquired by the robot (perception), and verify the completion of a task (evaluation). On the other hand, it is challenging to remotely control in joint space a robotic system like a humanoid robot that can easily have over 25 degrees of freedom (DOF). For this reason, in this approach the complex sub-tasks such as motion planning, motion execution, and obstacle avoidance (action) are performed autonomously by the remote robot. With this distribution of tasks, the challenge of converting the operator intent into a robot action arises. This thesis investigates concepts of how to efficiently provide a remote robot with the operator intent in a flexible means of interaction. While current approaches focus on an object-grasp-centered means of interaction, this thesis aims at providing physical and abstract properties of the objects of interest. With this information, the robot can perform autonomous subtasks like locomotion through the environment, grasping objects, and manipulating them at an affordance-level avoiding collisions with the environment in order to efficiently accomplish the manipulation task needed. For this purpose, the concept of Object Template (OT) has been developed in this thesis. An OT is a virtual representation of an object of interest that contains information that a remote robot can use to manipulate such object or other similar objects. The object template concept presented here goes beyond state-of-the-art related concepts by extending the robot capabilities to use affordance information of the object. This concept includes physical information (mass, center of mass, inertia tensor) as well as abstract information (potential grasps, affordances, and usabilities). Because humans are very good at analysing a situation, planning new ways of how to solve a task, even using objects for different purposes, it is important to allow communicating the planning and perception performed by the operator such that the robot can execute the action based on the information contained in the OT. This leverages human intelligence with robot capabilities. For example, as an implementation in a 3D environment, an OT can be visualized as a 3D geometry mesh that simulates an object of interest. A human operator can manipulate the OT and move it so that it overlaps with the visualized sensor data of the real object. Information of the object template type and its pose can be compressed and sent using low bandwidth communication. Then, the remote robot can use the information of the OT to approach, grasp, and manipulate the real object. The use of remote humanoid robots as avatars is expected to be intuitive to operators (or potential human response forces) since the kinematic chains and degrees of freedom are similar to humans. This allows operators to visualize themselves in the remote environment and think how to solve a task, however, task requirements such as special tools might not be found. For this reason, a flexible means of interaction that can account for allowing improvisation from the operator is also needed. In this approach, improvisation is described as "a change of a plan on how to achieve a certain task, depending on the current situation". A human operator can then improvise by adapting the affordances of known objects into new unknown objects. For example, by utilizing the affordances defined in an OT on a new object that has similar physical properties or which manipulation skills belong to the same class. The experimental results presented in this thesis validate the proposed approach by demonstrating the successful achievement of several manipulation tasks using object templates. Systematic laboratory experimentation has been performed to evaluate the individual aspects of this approach. The performance of the approach has been tested in three different humanoid robotic systems (one of these robots belongs to another research laboratory). These three robotic platforms also participated in the renowned international competition DARPA Robotics Challenge (DRC) which between 2012 and 2015 was considered the most ambitious and challenging robotic competition

Scaled Autonomy for Networked Humanoids

Humanoid robots have been developed with the intention of aiding in environments designed for humans. As such, the control of humanoid morphology and effectiveness of human robot interaction form the two principal research issues for deploying these robots in the real world. In this thesis work, the issue of humanoid control is coupled with human robot interaction under the framework of scaled autonomy, where the human and robot exchange levels of control depending on the environment and task at hand. This scaled autonomy is approached with control algorithms for reactive stabilization of human commands and planned trajectories that encode semantically meaningful motion preferences in a sequential convex optimization framework. The control and planning algorithms have been extensively tested in the field for robustness and system verification. The RoboCup competition provides a benchmark competition for autonomous agents that are trained with a human supervisor. The kid-sized and adult-sized humanoid robots coordinate over a noisy network in a known environment with adversarial opponents, and the software and routines in this work allowed for five consecutive championships. Furthermore, the motion planning and user interfaces developed in the work have been tested in the noisy network of the DARPA Robotics Challenge (DRC) Trials and Finals in an unknown environment. Overall, the ability to extend simplified locomotion models to aid in semi-autonomous manipulation allows untrained humans to operate complex, high dimensional robots. This represents another step in the path to deploying humanoids in the real world, based on the low dimensional motion abstractions and proven performance in real world tasks like RoboCup and the DRC

Motion Primitives and Planning for Robots with Closed Chain Systems and Changing Topologies

When operating in human environments, a robot should use predictable motions that allow humans to trust and anticipate its behavior. Heuristic search-based planning offers predictable motions and guarantees on completeness and sub-optimality of solutions. While search-based planning on motion primitive-based (lattice-based) graphs has been used extensively in navigation, application to high-dimensional state-spaces has, until recently, been thought impractical. This dissertation presents methods we have developed for applying these graphs to mobile manipulation, specifically for systems which contain closed chains. The formation of closed chains in tasks that involve contacts with the environment may reduce the number of available degrees-of-freedom but adds complexity in terms of constraints in the high-dimensional state-space. We exploit the dimensionality reduction inherent in closed kinematic chains to get efficient search-based planning. Our planner handles changing topologies (switching between open and closed-chains) in a single plan, including what transitions to include and when to include them. Thus, we can leverage existing results for search-based planning for open chains, combining open and closed chain manipulation planning into one framework. Proofs regarding the framework are introduced for the application to graph-search and its theoretical guarantees of optimality. The dimensionality-reduction is done in a manner that enables finding optimal solutions to low-dimensional problems which map to correspondingly optimal full-dimensional solutions. We apply this framework to planning for opening and navigating through non-spring and spring-loaded doors using a Willow Garage PR2. The framework motivates our approaches to the Atlas humanoid robot from Boston Dynamics for both stationary manipulation and quasi-static walking, as a closed chain is formed when both feet are on the ground

Nonlinear Model Predictive Control for Motion Generation of Humanoids

Das Ziel dieser Arbeit ist die Untersuchung und Entwicklung numerischer Methoden zur Bewegungserzeugung von humanoiden Robotern basierend auf nichtlinearer modell-prädiktiver Regelung. Ausgehend von der Modellierung der Humanoiden als komplexe Mehrkörpermodelle, die sowohl durch unilaterale Kontaktbedingungen beschränkt als auch durch die Formulierung unteraktuiert sind, wird die Bewegungserzeugung als Optimalsteuerungsproblem formuliert. In dieser Arbeit werden numerische Erweiterungen basierend auf den Prinzipien der Automatischen Differentiation für rekursive Algorithmen, die eine effiziente Auswertung der dynamischen Größen der oben genannten Mehrkörperformulierung erlauben, hergeleitet, sodass sowohl die nominellen Größen als auch deren ersten Ableitungen effizient ausgewertet werden können. Basierend auf diesen Ideen werden Erweiterungen für die Auswertung der Kontaktdynamik und der Berechnung des Kontaktimpulses vorgeschlagen. Die Echtzeitfähigkeit der Berechnung von Regelantworten hängt stark von der Komplexität der für die Bewegungerzeugung gewählten Mehrkörperformulierung und der zur Verfügung stehenden Rechenleistung ab. Um einen optimalen Trade-Off zu ermöglichen, untersucht diese Arbeit einerseits die mögliche Reduktion der Mehrkörperdynamik und andererseits werden maßgeschneiderte numerische Methoden entwickelt, um die Echtzeitfähigkeit der Regelung zu realisieren. Im Rahmen dieser Arbeit werden hierfür zwei reduzierte Modelle hergeleitet: eine nichtlineare Erweiterung des linearen inversen Pendelmodells sowie eine reduzierte Modellvariante basierend auf der centroidalen Mehrkörperdynamik. Ferner wird ein Regelaufbau zur GanzkörperBewegungserzeugung vorgestellt, deren Hauptbestandteil jeweils aus einem speziell diskretisierten Problem der nichtlinearen modell-prädiktiven Regelung sowie einer maßgeschneiderter Optimierungsmethode besteht. Die Echtzeitfähigkeit des Ansatzes wird durch Experimente mit den Robotern HRP-2 und HeiCub verifiziert. Diese Arbeit schlägt eine Methode der nichtlinear modell-prädiktiven Regelung vor, die trotz der Komplexität der vollen Mehrkörperformulierung eine Berechnung der Regelungsantwort in Echtzeit ermöglicht. Dies wird durch die geschickte Kombination von linearer und nichtlinearer modell-prädiktiver Regelung auf der aktuellen beziehungsweise der letzten Linearisierung des Problems in einer parallelen Regelstrategie realisiert. Experimente mit dem humanoiden Roboter Leo zeigen, dass, im Vergleich zur nominellen Strategie, erst durch den Einsatz dieser Methode eine Bewegungserzeugung auf dem Roboter möglich ist. Neben Methoden der modell-basierten Optimalsteuerung werden auch modell-freie Methoden des verstärkenden Lernens (Reinforcement Learning) für die Bewegungserzeugung untersucht, mit dem Fokus auf den schwierig zu modellierenden Modellunsicherheiten der Roboter. Im Rahmen dieser Arbeit werden eine allgemeine vergleichende Studie sowie Leistungskennzahlen entwickelt, die es erlauben, modell-basierte und -freie Methoden quantitativ bezüglich ihres Lösungsverhaltens zu vergleichen. Die Anwendung der Studie auf ein akademisches Beispiel zeigt Unterschiede und Kompromisse sowie Break-Even-Punkte zwischen den Problemformulierungen. Diese Arbeit schlägt basierend auf dieser Grundlage zwei mögliche Kombinationen vor, deren Eigenschaften bewiesen und in Simulation untersucht werden. Außerdem wird die besser abschneidende Variante auf dem humanoiden Roboter Leo implementiert und mit einem nominellen modell-basierten Regler verglichen

Motion Planning and Feedback Control of Simulated Robots in Multi-Contact Scenarios

Diese Dissertation präsentiert eine optimale steuerungsbasierte Architektur für die Bewegungsplanung und Rückkopplungssteuerung simulierter Roboter in Multikontaktszenarien. Bewegungsplanung und -steuerung sind grundlegende Bausteine für die Erstellung wirklich autonomer Roboter. Während in diesen Bereichen enorme Fortschritte für Manipulatoren mit festem Sockel und Radrobotern in den letzten Jahren erzielt wurden, besteht das Problem der Bewegungsplanung und -steuerung für Roboter mit Armen und Beinen immer noch ein ungelöstes Problem, das die Notwendigkeit effizienterer und robusterer Algorithmen belegt. In diesem Zusammenhang wird in dieser Dissertation eine Architektur vorgeschlagen, mit der zwei Hauptherausforderungen angegangen werden sollen, nämlich die effiziente Planung von Kontaktsequenzen und Ganzkörperbewegungen für Floating-Base-Roboter sowie deren erfolgreiche Ausführung mit Rückkopplungsregelungsstrategien, die Umgebungsunsicherheiten bewältigen könne

Climbing and Walking Robots

Nowadays robotics is one of the most dynamic fields of scientific researches. The shift of robotics researches from manufacturing to services applications is clear. During the last decades interest in studying climbing and walking robots has been increased. This increasing interest has been in many areas that most important ones of them are: mechanics, electronics, medical engineering, cybernetics, controls, and computers. Today’s climbing and walking robots are a combination of manipulative, perceptive, communicative, and cognitive abilities and they are capable of performing many tasks in industrial and non- industrial environments. Surveillance, planetary exploration, emergence rescue operations, reconnaissance, petrochemical applications, construction, entertainment, personal services, intervention in severe environments, transportation, medical and etc are some applications from a very diverse application fields of climbing and walking robots. By great progress in this area of robotics it is anticipated that next generation climbing and walking robots will enhance lives and will change the way the human works, thinks and makes decisions. This book presents the state of the art achievments, recent developments, applications and future challenges of climbing and walking robots. These are presented in 24 chapters by authors throughtot the world The book serves as a reference especially for the researchers who are interested in mobile robots. It also is useful for industrial engineers and graduate students in advanced study

The development of fire detection robot

Bu tez çalışmasının amacı; özellikle endüstriyel alanlarda, erken yangın algılamada kullanılacak bir yangın algılama robotu tasarlamak ve imal etmektir. Bu robot; önceden belirlenen sanal güzergâh üzerinde engel algılama fonksiyonuyla ve yeniden programlanabilir hareket ünitesiyle devriye gezebilecek ve yangın kaynağını tespit edebilmek için ortam taraması yapabilecek şekilde tasarlanmış ve imal edilmiştir. Sistem; hareket planlama ünitesine tanımlanan programlar ile değişken devriye güzergâhlarını takip edebilme yeteneğine sahiptir. Robotun tasarım ve uygulama süreçleri şu şekildedir; mekanik sistemin tasarımı ve geliştirilmesi, elektronik sistemin tasarımı ve geliştirilmesi ve gerekli yazılımların hazırlanmasıdır. Mekanik sistemin tasarım ve geliştirilme sürecinde; taslak çizimleri, ölçülendirmeler ve üç boyutlu modelleme için bilgisayar destekli tasarım ve katı modelleme programları kullanılmıştır. Robotun taşıyıcı gövdesi; ucuz, sağlam ve kolay işlenebilir malzemeler olan ahşap ve sert plastik köpük kullanılarak imal edilmiştir. Robot sürüş sisteminde diferansiyel metot kullanılmıştır. Yarı otomatik robot dört adet fırçalı doğru akım motoru ile çalışmaktadır. Elektronik sistemin tasarımı ve geliştirilmesi sürecinde; hazır kart almak yerine ihtiyaca uygun elektronik veri kazanım ve kontrol devreleri tasarlanıp üretilmiştir. Bu devrelerin şematik diyagramı ve baskı devresi Proteus elektronik tasarım programı kullanılarak hazırlanmıştır. Bu devreler; motor hareketlerini kontrol etmekte ve dizüstü bilgisayar ile algılama üniteleri arasında bir köprü kurmakta kullanılmıştır. Yazılımların hazırlanma sürecinde; engel algılamada ve güzergâh takibinde kullanılacak akıllı yazılımlar geliştirilmiştir. Ayrıca daha güvenilir yangın algılama sağlamak için; çoklu sensör algılama ve değerlendirme algoritması geliştirilmiştir. Bu tezin sonucunda; özellikle endüstriyel alanlarda kullanılabilecek, çeşitli fonksiyonlara sahip bir yangın algılama robotu tasarlanıp imal edilmiştir. Yapılan testlerle; sistemin en fazla 100 cm mesafedeki yangını, robot 0,5 m/s hızla ilerlerken tespit edebildiği sonucuna varılmıştır.The aim of this thesis is to design and manufacture a fire detection robot that especially operates in industrial areas for fire inspection and early detection. Robot is designed and implemented to track prescribed paths with obstacle avoidance function through obstacle avoidance and motion planning units and to scan the environment in order to detect fire source using fire detection unit. Robot is able to track patrolling routes using virtual lines that defined to the motion planning unit. The design and implementation processes of the robot are as follow; the design and the development of mechanical, electronic systems and software. The design and the development of mechanical system; for the sketch drawings, dimensioning and solid state modeling of the robot, computer aided design and solid modelling computer programs were used. The carrier board of the robot is produced using wooden material and rigid plastic foam which are cheap, strong enough and easy to manufacture. Differential steering method is selected for semi-autonomous robot driving system and it is powered by four brushed DC (direct current) motors. The design and the development of electronic system; electronic circuits were designed and produced, instead of buying a commercial card. Both schematic diagrams and circuits of the data acquisition and control circuits are designed using Proteus electronic design program. These circuits are used to control the motion of the motors and establish a data flow between the laptop and the other peripheral sensing components. Software development; intelligent algorithms for obstacle avoidance and path tracking have been developed. A sensor data fusion algorithm for the sensors was also developed to get more reliable fire detection information. In conclusion; a fire inspection and detection robot with various functions to especially can be used in industrial areas was designed and manufactured. The functions of the robot were tested. It can be concluded that system is able to detect the fire source maximum 100 cm distance away while robot is moving with 0.5 m/s forward speed