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

Coverage Path Planning with Real‐time Replanning and Surface Reconstruction for Inspection of Three‐dimensional Underwater Structures using Autonomous Underwater Vehicles

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/113717/1/rob21554.pd

Motion planning and perception : integration on humanoid robots

This thesis starts by proposing a new framework for motion planning using stochastic maps, such as occupancy-grid maps. In autonomous robotics applications, the robot's map of the environment is typically constructed online, using techniques from SLAM. These methods can construct a dense map of the environment, or a sparse map that contains a set of identifiable landmarks. In this situation, path planning would be performed using the dense map, and the path would be executed in a sensor-based fashion, using feedback control to track the reference path based on sensor information regarding landmark position. Maximum-likelihood estimation techniques are used to model the sensing process as well as to estimate the most likely nominal path that will be followed by the robot during execution of the plan. The proposed approach is potentially a practical way to plan under the specific sorts of uncertainty confronted by a humanoid robot. The next chapter, presents methods for constructing free paths in dynamic environments. The chapter begins with a comprehensive review of past methods, ranging from modifying sampling-based methods for the dynamic obstacle problem, to methods that were specifically designed for this problem. The thesis proposes to adapt a method reported originally by Leven et al.. so that it can be used to plan paths for humanoid robots in dynamic environments. The basic idea of this method is to construct a mapping from voxels in a discretized representation of the workspace to vertices and arcs in a configuration space network built using sampling-based planning methods. When an obstacle intersects a voxel in the workspace, the corresponding nodes and arcs in the configuration space roadmap are marked as invalid. The part of the network that remains comprises the set of valid candidate paths. The specific approach described here extends previous work by imposing a two-level hierarchical structure on the representation of the workspace. The methods described in Chapters 2 and 3 essentially deal with low-dimensional problems (e.g., moving a bounding box). The reduction in dimensionality is essential, since the path planning problem confronted in these chapters is complicated by uncertainty and dynamic obstacles, respectively. Chapter 4 addresses the problem of planning the full motion of a humanoid robot (whole-body task planning). The approach presented here is essentially a four-step approach. First, multiple viable goal configurations are generated using a local task solver, and these are used in a classical path planning approach with one initial condition and multiple goals. This classical problem is solved using an RRT-based method. Once a path is found, optimization methods are applied to the goal posture. Finally, classic path optimization algorithms are applied to the solution path and posture optimization. The fifth chapter describes algorithms for building a representation of the environment using stereo vision as the sensing modality. Such algorithms are necessary components of the autonomous system proposed in the first chapter of the thesis. A simple occupancy-grid based method is proposed, in which each voxel in the grid is assigned a number indicating the probability that it is occupied. The representation is updated during execution based on values received from the sensing system. The sensor model used is a simple Gaussian observation model in which measured distance is assumed to be true distance plus additive Gaussian noise. Sequential Bayes updating is then used to incrementally update occupancy values as new measurements are received. Finally, chapter 6 provides some details about the overall system architecture, and in particular, about those components of the architecture that have been taken from existing software (and therefore, do not themselves represent contributions of the thesis). Several software systems are described, including GIK, WorldModelGrid3D, HppDynamicObstacle, and GenoM

A dynamical system approach to realtime obstacle avoidance

This paper presents a novel approach to real-time obstacle avoidance based on Dynamical Systems (DS) that ensures impenetrability of multiple convex shaped objects. The proposed method can be applied to perform obstacle avoidance in Cartesian and Joint spaces and using both autonomous and non-autonomous DS-based controllers. Obstacle avoidance proceeds by modulating the original dynamics of the controller. The modulation is parameterizable and allows to determine a safety margin and to increase the robot's reactiveness in the face of uncertainty in the localization of the obstacle. The method is validated in simulation on different types of DS including locally and globally asymptotically stable DS, autonomous and non-autonomous DS, limit cycles, and unstable DS. Further, we verify it in several robot experiments on the 7 degrees of freedom Barrett WAM ar

Timed-Elastic Bands for Manipulation Motion Planning

© 2019 IEEE. Motion planning is one of the main problems studied in the field of robotics. However, it is still challenging for the state-of-the-art methods to handle multiple conditions that allow better paths to be found. For example, considering joint limits, path smoothness and a mixture of Cartesian and joint-space constraints at the same time pose a significant challenge for many of them. This letter proposes to use timed-elastic bands for representing the manipulation motion planning problem, allowing to apply continuously optimized constraints to the problem during the search for a solution. Due to the nature of our method, it is highly extensible with new constraints or optimization objectives. The proposed approach is compared against state-of-the-art methods in various manipulation scenarios. The results show that it is more consistent and less variant, while performing in a comparable manner to that of the state of the art. This behavior allows the proposed method to set a lower-bound performance guarantee for other methods to build upon

Designing Solvable Graphs for Multiple Moving Agents

Solvable Graphs (also known as Reachable Graphs) are types of graphs that any arrangement of a specified number of agents located on the graph’s vertices can be reached from any initial arrangement through agents’ moves along the graph’s edges, while avoiding deadlocks (interceptions). In this paper, the properties of Solvable Graphs are investigated, and a new concept in multi agent motion planning, called Minimal Solvable Graphs is introduced. Minimal Solvable Graphs are the smallest graphs among Solvable Graphs in terms of the number of vertices. Also, for the first time, the problem of deciding whether a graph is Solvable for m agents is answered, and a new algorithm is presented for making an existing graph solvable and lean for a given number of agents. Finally, through an industrial example, it is demonstrated that how the findings of this paper can be used in designing and reshaping transportation networks (e.g. railways, traffic roads, AGV routs, robotic workspaces, etc.) for multiple moving agents such as trains, vehicles, and robots

An Assessment of PIER Electric Grid Research 2003-2014 White Paper

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014