Real-Time Support of Haptic Interaction by Means of Sampling-Based Path Planning

Haptic feedback enables the support of a human during the interaction with an environment. A variety of concepts have been developed to achieve an effective haptic support of the user in specific scenarios, e.g. Virtual Fixtures. However, most of these methods do not enable an adaptive support of the motion from a user within a (real or virtual) environment, which would be desirable in many situations. Especially when dynamical obstacles are involved or when the desired motion of the human is not known beforehand, an online computation of this support is essential, which should be based on a fast and effective determination of feasible motions.In contrast to other methods, sampling-based path planning is applicable to arbitrary interaction scenarios and enables to find a solution if it exists at all. Thus, it seems to be ideally suited for a generic framework that is able to deal with various kinematics, as e.g. a virtual prototyping test bed for the haptic evaluation of mechanisms requires. At such a test bed, the path planner could directly be coupled to the haptic rendering of a virtual scene to assist a user in approaching a target.This motivated the development of SamPP, a sampling-based path planning library with implementations of the most important algorithms. It can be used for nearly arbitrary rigid robots and environments. By performing numerous benchmarks, we prove the effectiveness and efficiency of SamPP. It is shown that a single-threaded version of the path planning can be used for real-time support of the haptic interaction at a novel actuated car door.Furthermore, we enhance the path planning performance for unknown or dynamical environments significantly by the OR-Parallelization of different path planning queries. This Generalized OR-Parallelization is a novel concept that to the best knowledge of the authors has not been proposed beforehand. We show that for the case of dynamic environments the likelihood of a fast path planning result is higher with our approach. Thus, even in dynamic or unknown environments, a real-time support of haptic interaction can be achieved. Finally, we highlight four promising research directions to exploit the concept of Generalized OR-Parallelization: 1) Combination of PRMs and RRTs to achieve a synergy of the advantages of both concepts, 2) concurrent use of different parameter sets of path planning algorithms, 3) online adaptation of these parameter sets and 4) online adaptation of the types and numbers of parallel executed path planning programs

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

A Certified-Complete Bimanual Manipulation Planner

Planning motions for two robot arms to move an object collaboratively is a difficult problem, mainly because of the closed-chain constraint, which arises whenever two robot hands simultaneously grasp a single rigid object. In this paper, we propose a manipulation planning algorithm to bring an object from an initial stable placement (position and orientation of the object on the support surface) towards a goal stable placement. The key specificity of our algorithm is that it is certified-complete: for a given object and a given environment, we provide a certificate that the algorithm will find a solution to any bimanual manipulation query in that environment whenever one exists. Moreover, the certificate is constructive: at run-time, it can be used to quickly find a solution to a given query. The algorithm is tested in software and hardware on a number of large pieces of furniture.Comment: 12 pages, 7 figures, 1 tabl

Sampling Based Motion Planning with Reachable Volumes

Motion planning for constrained systems is a version of the motion planning problem in which the motion of a robot is limited by constraints. For example, one can require that a humanoid robot such as a PR2 remain upright by constraining its torso to be above its base or require that an object such as a bucket of water remain upright by constraining the vertices of the object to be parallel to the robot’s base. Grasping can be modeled by requiring that the end effectors of the robot be located at specified handle positions. Constraints might require that the robot remain in contact with a surface, or that certain joints of the robot remain in contact with each other (e.g., closed chains). Such problems are particularly difficult because the constraints form a manifold in C-space, and planning must be restricted to this manifold. High degree of freedom motion planning and motion planning for constrained systems has applications in parallel robotics, grasping and manipulation, computational biology and molecular simulations, and animation. In this work, we introduce a new concept, reachable volumes, that are a geometric representation of the regions the joints and end effectors of a robot can reach, and use it to define a new planning space, called RV-space, where all points automatically satisfy a problem’s constraints. Visualizations of reachable volumes can enable operators to see the regions of workspace that different parts of the robot can reach. Samples and paths generated in RV-space naturally conform to constraints, making planning for constrained systems no more difficult than planning for unconstrained systems. Consequently, constrained motion planning problems that were previously difficult or unsolvable become manageable and in many cases trivial. We provide tools and techniques to extend the state of the art sampling based motion planning algorithms to RV-space. We define a reachable volume sampler, a reachable volume local planner and a reachable volume distance metric. We showcase the effectiveness of RV-space by applying these tools to motion planning problems for robots with constraints on the end effectors and/or internal joints of the robot. We show that RV-based planners are more efficient than existing methods, particularly for higher dimensional problems, solving problems with 1000+ degrees of freedom for multi-loop, and tree-like linkages

Generalizing Informed Sampling for Asymptotically Optimal Sampling-based Kinodynamic Planning via Markov Chain Monte Carlo

Asymptotically-optimal motion planners such as RRT* have been shown to incrementally approximate the shortest path between start and goal states. Once an initial solution is found, their performance can be dramatically improved by restricting subsequent samples to regions of the state space that can potentially improve the current solution. When the motion planning problem lies in a Euclidean space, this region $X_{inf}$, called the informed set, can be sampled directly. However, when planning with differential constraints in non-Euclidean state spaces, no analytic solutions exists to sampling $X_{inf}$ directly. State-of-the-art approaches to sampling $X_{inf}$ in such domains such as Hierarchical Rejection Sampling (HRS) may still be slow in high-dimensional state space. This may cause the planning algorithm to spend most of its time trying to produces samples in $X_{inf}$ rather than explore it. In this paper, we suggest an alternative approach to produce samples in the informed set $X_{inf}$ for a wide range of settings. Our main insight is to recast this problem as one of sampling uniformly within the sub-level-set of an implicit non-convex function. This recasting enables us to apply Monte Carlo sampling methods, used very effectively in the Machine Learning and Optimization communities, to solve our problem. We show for a wide range of scenarios that using our sampler can accelerate the convergence rate to high-quality solutions in high-dimensional problems