Motion Planning : from Digital Actors to Humanoid Robots

Le but de ce travail est de développer des algorithmes de planification de mouvement pour des figures anthropomorphes en tenant compte de la géométrie, de la cinématique et de la dynamique du mécanisme et de son environnement. Par planification de mouvement, on entend la capacité de donner des directives à un niveau élevé et de les transformer en instructions de bas niveau qui produiront une séquence de valeurs articulaires qui reproduissent les mouvements humains. Ces instructions doivent considérer l'évitement des obstacles dans un environnement qui peut être plus au moins contraint. Ceci a comme consequence que l'on peut exprimer des directives comme “porte ce plat de la table jusqu'ac'estu coin du piano”, qui seront ensuite traduites en une série de buts intermédiaires et de contraintes qui produiront les mouvements appropriés des articulations du robot, de façon a effectuer l'action demandée tout en evitant les obstacles dans la chambre. Nos algorithmes se basent sur l'observation que les humains ne planifient pas des mouvements précis pour aller à un endroit donné. On planifie grossièrement la direction de marche et, tout en avançant, on exécute les mouvements nécessaires des articulations afin de nous mener à l'endroit voulu. Nous avons donc cherché à concevoir des algorithmes au sein d'un tel paradigme, algorithmes qui: 1. Produisent un chemin sans collision avec une version réduite du mécanisme et qui le mènent au but spécifié. 2. Utilisent les contrôleurs disponibles pour générer un mouvement qui assigne des valeurs à chacune des articulations du mécanisme pour suivre le chemin trouvé précédemment. 3. Modifient itérativement ces trajectoires jusqu'à ce que toutes les contraintes géométriques, cinématiques et dynamiques soient satisfaites. Dans ce travail nous appliquons cette approche à trois étages au problème de la planification de mouvements pour des figures anthropomorphes qui manipulent des objets encombrants tout en marchant. Dans le processus, plusieurs problèmes intéressants, ainsi que des propositions pour les résoudre, sont présentés. Ces problèmes sont principalement l'évitement tri-dimensionnel des obstacles, la manipulation des objets à deux mains, la manipulation coopérative des objets et la combinaison de comportements hétérogènes. La contribution principale de ce travail est la modélisation du problème de la génération automatique des mouvements de manipulation et de locomotion. Ce modèle considère les difficultés exprimées ci dessus, dans les contexte de mécanismes bipèdes. Trois principes fondent notre modèle: une décomposition fonctionnelle des membres du mécanisme, un modèle de manipulation coopérative et, un modéle simplifié des facultés de déplacement du mécanisme dans son environnement.Ce travail est principalement et surtout, un travail de synthèse. Nous nous servons des techniques disponibles pour commander la locomotion des mécanismes bipèdes (contrôleurs) provenant soit de l'animation par ordinateur, soit de la robotique humanoïde, et nous les relions dans un planificateur des mouvements original. Ce planificateur de mouvements est agnostique vis-à-vis du contrôleur utilisé, c'est-à-dire qu'il est capable de produire des mouvements libres de collision avec n'importe quel contrôleur tandis que les entrées et sorties restent compatibles. Naturellement, l'exécution de notre planificateur dépend en grand partie de la qualité du contrôleur utilisé. Dans cette thèse, le planificateur de mouvement est relié à différents contrôleurs et ses bonnes performances sont validées avec des mécanismes différents, tant virtuels que physiques. Ce travail à été fait dans le cadre des projets de recherche communs entre la France, la Russie et le Japon, où nous avons fourni le cadre de planification de mouvement à ses différents contrôleurs. Plusieurs publications issues de ces collaborations ont été présentées dans des conférences internationales. Ces résultats sont compilés et présentés dans cette thèse, et le choix des techniques ainsi que les avantages et inconvénients de notre approche sont discutés. ABSTRACT : The goal of this work is to develop motion planning algorithms for human-like figures taking into account the geometry, kinematics and dynamics of the mechanism and its environment. By motion planning it is understood the ability to specify high-level directives and transform them into low-level instructions for the articulations of the human-like figure. This is usually done while considering obstacle avoidance within the environment. This results in one being able to express directives as “carry this plate from the table to the piano corner” and have them translate into a series of goals and constraints that result in the pertinent motions from the robot's articulations in such a way as to carry out the action while avoiding collisions with the obstacles in the room. Our algorithms are based on the observation that humans do not plan their exact motions when getting to a location. We roughly plan our direction and, as we advance, we execute the motions needed to get to the desired place. This has led us to design algorithms that: 1. Produce a rough collision free path that takes a simplified model of the mechanism to the desired location. 2. Use available controllers to generate a trajectory that assigns values to each of the mechanism's articulations to follow the path. 3. Modify iteratively these trajectories until all the geometric, kinematic and dynamic constraints of the problem are satisfied.Throughout this work, we apply this three-stage approach with the problem of generating motions for human-like figures that manipulate bulky objects while walking. In the process, several interesting problems and their solution are brought into focus. These problems are, three- imensional collision avoidance, two-hand object manipulation, cooperative manipulation among several characters or robots and the combination of different behaviors. The main contribution of this work is the modeling of the automatic generation of cooperative manipulation motions. This model considers the above difficulties, all in the context of bipedal walking mechanisms. Three principles inform the model: a functional decomposition of the mechanism's limbs, a model for cooperative manipulation and, a simplified model to represent the mechanism when generating the rough path. This work is mainly and above all, one of synthesis. We make use of available techniques for controlling locomotion of bipedal mechanisms (controllers), from the fields of computer graphics and robotics, and connect them to a novel motion planner. This motion planner is controller-agnostic, that is, it is able to produce collision-free motions with any controller, despite whatever errors introduced by the controller itself. Of course, the performance of our motion planner depends on the quality of the used controller. In this thesis, the motion planner, connected to different controllers, is used and tested in different mechanisms, both virtual and physical. This in the context of different research projects in France, Russia and Japan, where we have provided the motion planning framework to their controllers. Several papers in peer-reviewed international conferences have resulted from these collaborations. The present work compiles these results and provides a more comprehensive and detailed depiction of the system and its benefits, both when applied to different mechanisms and compared to alternative approache

Novel path curvature optimization algorithm for intelligent wheelchair to smoothly pass a narrow space

This paper presents a novel algorithm to address the smooth narrow pass traversing issue, which is based on optimizing the curvature of the wheelchair path. Being aware of the fact that the path smoothness is determined by the path curvature and its change rate, after calculating the position of the narrow pass relative to the base frame of the wheelchair from perception sensor data, the algorithm takes the curvature and its change rate of Bezier curve as the optimal objective, and the wheelchair heading and the condition that the Bezier curve polygon should be convex polygon as constraints, and plans a smooth and optimal path for the controlled wheelchair to follow. This process is iterated dynamically to enable the intelligent wheelchair to traverse the narrow pass smoothly. Simulation is firstly conducted to compare the performances of our method and the A*-based path planning navigation algorithm, which shows that the proposed algorithm is able to achieve more smooth path with smaller curvature when the wheelchair traverses narrow path. Furthermore, the algorithm can control the wheelchair to traverse narrow pass smoothly even without any global map and localization. Real experiment with detailed explanation of algorithm implementation is also given to verify the effectiveness of the proposed algorithm

Robotics 2010

Without a doubt, robotics has made an incredible progress over the last decades. The vision of developing, designing and creating technical systems that help humans to achieve hard and complex tasks, has intelligently led to an incredible variety of solutions. There are barely technical fields that could exhibit more interdisciplinary interconnections like robotics. This fact is generated by highly complex challenges imposed by robotic systems, especially the requirement on intelligent and autonomous operation. This book tries to give an insight into the evolutionary process that takes place in robotics. It provides articles covering a wide range of this exciting area. The progress of technical challenges and concepts may illuminate the relationship between developments that seem to be completely different at first sight. The robotics remains an exciting scientific and engineering field. The community looks optimistically ahead and also looks forward for the future challenges and new development

A Dynamical System-based Approach to Modeling Stable Robot Control Policies via Imitation Learning

Despite tremendous advances in robotics, we are still amazed by the proficiency with which humans perform movements. Even new waves of robotic systems still rely heavily on hardcoded motions with a limited ability to react autonomously and robustly to a dynamically changing environment. This thesis focuses on providing possible mechanisms to push the level of adaptivity, reactivity, and robustness of robotic systems closer to human movements. Specifically, it aims at developing these mechanisms for a subclass of robot motions called “reaching movements”, i.e. movements in space stopping at a given target (also referred to as episodic motions, discrete motions, or point-to-point motions). These reaching movements can then be used as building blocks to form more advanced robot tasks. To achieve a high level of proficiency as described above, this thesis particularly seeks to derive control policies that: 1) resemble human motions, 2) guarantee the accomplishment of the task (if the target is reachable), and 3) can instantly adapt to changes in dynamic environments. To avoid manually hardcoding robot motions, this thesis exploits the power of machine learning techniques and takes an Imitation Learning (IL) approach to build a generic model of robot movements from a few examples provided by an expert. To achieve the required level of robustness and reactivity, the perspective adopted in this thesis is that a reaching movement can be described with a nonlinear Dynamical System (DS). When building an estimate of DS from demonstrations, there are two key problems that need to be addressed: the problem of generating motions that resemble at best the demonstrations (the “how-to-imitate” problem), and most importantly, the problem of ensuring the accomplishment of the task, i.e. reaching the target (the “stability” problem). Although there are numerous well-established approaches in robotics that could answer each of these problems separately, tackling both problems simultaneously is challenging and has not been extensively studied yet. This thesis first tackles the problem mentioned above by introducing an iterative method to build an estimate of autonomous nonlinear DS that are formulated as a mixture of Gaussian functions. This method minimizes the number of Gaussian functions required for achieving both local asymptotic stability at the target and accuracy in following demonstrations. We then extend this formulation and provide sufficient conditions to ensure global asymptotic stability of autonomous DS at the target. In this approach, an estimation of the underlying DS is built by solving a constraint optimization problem, where the metric of accuracy and the stability conditions are formulated as the optimization objective and constraints, respectively. In addition to ensuring convergence of all motions to the target within the local or global stability regions, these approaches offer an inherent adaptability and robustness to changes in dynamic environments. This thesis further extends the previous approaches and ensures global asymptotic stability of DS-based motions at the target independently of the choice of the regression technique. Therefore, it offers the possibility to choose the most appropriate regression technique based on the requirements of the task at hand without compromising DS stability. This approach also provides the possibility of online learning and using a combination of two or more regression methods to model more advanced robot tasks, and can be applied to estimate motions that are represented with both autonomous and non-autonomous DS. Additionally, this thesis suggests a reformulation to modeling robot motions that allows encoding of a considerably wider set of tasks ranging from reaching movements to agile robot movements that require hitting a given target with a specific speed and direction. This approach is validated in the context of playing the challenging task of minigolf. Finally, the last part of this thesis proposes a DS-based approach to realtime obstacle avoidance. The presented approach provides a modulation that instantly modifies the robot’s motion to avoid collision with multiple static and moving convex obstacles. This approach can be applied on all the techniques described above without affecting their adaptability, swiftness, or robustness. The techniques that are developed in this thesis have been validated in simulation and on different robotic platforms including the humanoid robots HOAP-3 and iCub, and the robot arms KATANA, WAM, and LWR. Throughout this thesis we show that the DS-based approach to modeling robot discrete movements can offer a high level of adaptability, reactivity, and robustness almost effortlessly when interacting with dynamic environments

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Vehicle and Traffic Safety

The book is devoted to contemporary issues regarding the safety of motor vehicles and road traffic. It presents the achievements of scientists, specialists, and industry representatives in the following selected areas of road transport safety and automotive engineering: active and passive vehicle safety, vehicle dynamics and stability, testing of vehicles (and their assemblies), including electric cars as well as autonomous vehicles. Selected issues from the area of accident analysis and reconstruction are discussed. The impact on road safety of aspects such as traffic control systems, road infrastructure, and human factors is also considered

Entropie‐dominierte Selbstorganisationsprozesse birnenförmiger Teilchensysteme

The ambition to recreate highly complex and functional nanostructures found in living organisms marks one of the pillars of today‘s research in bio- and soft matter physics. Here, self-assembly has evolved into a prominent strategy in nanostructure formation and has proven to be a useful tool for many complex structures. However, it is still a challenge to design and realise particle properties such that they self-organise into a desired target configuration. One of the key design parameters is the shape of the constituent particles. This thesis focuses in particular on the shape sensitivity of liquid crystal phases by addressing the entropically driven colloidal self-assembly of tapered ellipsoids, reminiscent of „pear-shaped“ particles. Therefore, we analyse the formation of the gyroid and of the accompanying bilayer architecture, reported earlier in the so-called pear hard Gaussian overlap (PHGO) approximation, by applying various geometrical tools like Set-Voronoi tessellation and clustering algorithms. Using computational simulations, we also indicate a method to stabilise other bicontinuous structures like the diamond phase. Moreover, we investigate both computationally and theoretically(density functional theory) the influence of minor variations in shape on different pearshaped particle systems, including the stability of the PHGO gyroid phase. We show that the formation of the gyroid is due to small non-additive properties of the PHGO potential. This phase does not form in pears with a „true“ hard pear-shaped potential. Overall our results allow for a better general understanding of necessity and sufficiency of particle shape in regards to colloidal self-assembly processes. Furthermore, the pear-shaped particle system sheds light on a unique collective mechanism to generate bicontinuous phases. It suggests a new alternative pathway which might help us to solve still unknown characteristics and properties of naturally occurring gyroid-like nano- and microstructures.Ein wichtiger Bestandteil der heutigen Forschung in Bio- und Soft Matter Physik besteht daraus, Technologien zu entwickeln, um hoch komplexe und funktionelle Strukturen, die uns aus der Natur bekannt sind, nachzubilden. Hinsichtlich dessen ist vor allem die Methode der Selbstorganisation von Mikro- und Nanoteilchen hervorzuheben, durch die eine Vielzahl verschiedener Strukturen erzeugt werden konnten. Jedoch stehen wir bei diesem Verfahren noch immer vor der Herausforderung, Teilchen mit bestimmten Eigenschaften zu entwerfen, welche die spontane Anordnung der Teilchen in eine gewünschte Struktur bewirken. Einer der wichtigsten Designparameter ist dabei die Form der Bausteinteilchen. In dieser Dissertation konzentrieren wir uns besonders auf die Anfälligkeit von Flüssigkristallphasen bezüglich kleiner Änderungen der Teilchenform und nutzen dabei das Beispiel der Selbstorganisation von Entropie-dominierter Kolloide, die dem Umriss nach verjüngten Ellipsoiden oder "Birnen" ähneln. Mit Hilfe von geometrischen Werkzeugen wie z.B. Set-Voronoi Tessellation oder Cluster-Algorithmen analysieren wir insbesondere die Entstehung der Gyroidphase und der dazugehörigen Bilagenformation, welche bereits in Systemen von harten Birnen, die durch das pear hard Gaussian overlap (PHGO) Potential angenähert werden, entdeckt wurden. Des Weiteren zeigen wir durch Computersimulationen eine Strategie auf, um andere bikontinuierliche Strukturen, wie die Diamentenphase, zu stabilisieren. Schlussendlich betrachten wir sowohl rechnerisch (durch Simulationen) als auch theoretisch (durch Dichtefunktionaltheorie) die Auswirkungen kleiner Abweichungen der Teilchenform auf das Verhalten des kolloiden, birnenförmigen Teilchensystems, inklusive der Stabilität der PHGO Gyroidphase. Wir zeigen, dass die Entstehung des Gyroids auf kleinen nicht-additiven Eigenschaften des PHGO Birnenmodells beruhen. In ''echten'' harten Teilchensystemen entwickelt sich diese Struktur nicht. Insgesamt ermöglichen unsere Ergebnisse einen besseren Einblick auf das Konzept von notwendiger und hinreichender Teilchenform in Selbstorganistationsprozessen. Die birnenförmigen Teilchensysteme geben außerdem Aufschluss über einen ungewöhnlichen, kollektiven Mechanismus, um bikontinuierliche Phasen zu erzeugen. Dies deutet auf einen neuen, alternativen Konstruktionsweg hin, der uns möglicherweise hilft, noch unbekannte Eigenschaften natürlich vorkommender, gyroidähnlicher Nano- und Mikrostrukturen zu erklären

Entropically driven self-assembly of pear-shaped nanoparticles

This thesis addresses the entropically driven colloidal self-assembly of pear-shaped particle ensembles, including the formation of nanostructures based on triply periodic minimal surfaces, in particular of the Ia3d gyroid. One of the key results is that the formation of the Ia3d gyroid, re-ported earlier in the so-called pear hard Gaussian overlap (PHGO) approximation and confirmed here, is due to a slight non-additivity of that potential; this phase does not form in pears with true hard-core potential. First, we computationally study the PHGO system and present the phase diagram of pears with an aspect ratio of 3 in terms of global density and particle shape (degree of taper), containing gyroid, isotropic, nematic and smectic phases. We confirm that it is adequate to interpret the gyroid as a warped smectic bilayer phase. The collective behaviour to arrange into interdigitated sheets with negative Gauss curvature, from which the gyroid results, is investigated through correlations of (Set-)Voronoi cells and local curvature. This geometric arrangement within the bilayers suggests a fundamentally different stabilisation mechanism of the pear gyroid phase compared to those found in both lipid-water and di-block copolymer systems forming the Ia3d gyroid. The PHGO model is only an approximation for hard-core interactions, and we additionally investigate, by much slower simulations, pear-assemblies with true hard-core interactions (HPR). We find that HPR phase diagram only contains isotropic and nematic phases, but neither gyroid nor smectic phases. To understand this shape sensitivity more profoundly, the depletion interactions of both models are studied in two pear-shaped colloids dissolved in a hard sphere solvent. The HPR particles act as one would expect from a geometric analysis of the excluded-volume minimisation, whereas the PHGO particles show deviations from this expectation. These differences are attributed to the unusual angle dependency of the (non-additive) contact function and, more so, to small overlaps induced by the approximation. For the PHGO model, we further demonstrate that the addition of a small concentration of hard spheres ("solvent") drives the system towards a Pn3m diamond phase. This result is explained by the greater spatial heterogeneity of the diamond geometry compared to the gyroid where additional material is needed to relieve packing frustration. In contrast to copolymer systems, however, the solvent mostly aggregates near the diamond minimal surface, driven by the non-additivity of the PHGO pears. At high solvent concentrations, the mixture phase separates into “inverse” micelle-like structures with the blunt ends at the micellar centres and thin ends pointing out-wards. The micelles themselves spontaneously cluster, indicative of a hierarchical self-assembly process for bicontinuous structures. Finally, we develop a density functional for hard solids of revolution (including pears) within the framework of fundamental measure theory. It is applied to low-density ensembles of pear-shaped particles, where we analyse their response near a hard substrate. A complex orientational ordering close to the wall is predicted, which is directly linked to the particle shape and gives insight into adsorption processes of asymmetric particles. This predicted behaviour and the differences between the PHGO and HPR model are confirmed by MC simulations