L’utilisation de robots collaboratifs dans l’industrie de production est en plein essor. Ces robots, dont la puissance est limitée, sont dotés de capteurs leur permettant de détecter la présence d’obstacles, afin de garantir la sécurité des humains se trouvant aux alentours. On s’intéresse dans cette thèse à l’utilisation de systèmes redondants, collaboratifs et mobiles (bras articulés montés sur plateformes mobiles), dans un environnement de production aéronautique peuplé d’humains, pour la réalisation d’opérations d’assemblage. Du point de vue des process, l’utilisation de ces systèmes, souvent beaucoup moins imposants et rigides que leurs homologues non collaboratifs, est jalonnée de défis. La grande souplesse mécanique et les faibles couples qui les caractérisent peuvent induire des imprécisions de positionnement et une incapacité à soutenir l’intensité d’une interaction physique. Ce contexte induit également un besoin d’autonomie de ces systèmes, qui sont amenés à travailler dans des environnements en perpétuelle évolution. Dans cette thèse, une formulation de la redondance cinématique est d’abord présentée. Le formalisme associé permet de simplifier l’exploitation de la liberté que ces systèmes possèdent sur le choix des postures à utiliser pour réaliser des tâches de placement statique de l’effecteur. Ce formalisme est ensuite exploité pour améliorer et caractériser le comportement en déformation et la capacité d’application d’efforts des systèmes redondants sériels. Enfin, le sujet de la planification des mouvements de systèmes robotisés dans un environnement dynamique et encombré est considéré. La solution présentée adapte l’algorithme bien connu des Probabilistic RoadMaps pour y inclure une anticipation des trajectoires des obstacles dynamiques. Cette solution permet de planifier des mouvements sécuritaires, peu intrusifs et efficaces, jusqu’à la destination. / Industrial applications involving collaborative robots are regarded with a growing interest. These power-limited systems are embedded with additional sensing capabilities, which allow them to safely work around humans and conquer new industrial grounds. The subject of managing redundant, collaborative and mobile systems, for assembly operations within a human-populated aircraft production environment, is addressed in this thesis. From a process perspective, the use of these smaller and less stiff counterparts of the non-collaborative robots comes with new challenges. Their high mechanical flexibility and weak actuation can cause shortcomings in positioning accuracy or for interaction force sustainment. The ever-changing nature of human-populated environments also requires highly autonomous solutions. In this thesis, a formulation of positional redundancy is presented. It aims at simplifying the exploitation of the freedom redundant manipulators have on static-task-fulfilling postures. The associated formalism is then exploited to characterise and improve the deformational behaviour and the force capacity of redundant serial systems. Finally, the subject of planning motions within cluttered and dynamic environments is addressed. An adaptation of the well-known Probabilistic RoadMaps method is presented – to which obstacles trajectories anticipation has been included. This solution allows to plan safe, efficient and non-intrusive motions to a given destination.

Cette thèse porte sur le développement de lois de commande pour la marche desrobots bipèdes. Le sous actionnement engendré par le basculement, volontaire ouinvolontaire, du pied en appui sur le sol représente une difficulté majeure. Nousabordons ce problème par l’étude de robots plans avec pieds ponctuels.La première partie de la thèse est une compilation des informations issuesde la littérature que nous avons jugées intéressantes. Nous traitons dans unpremier temps de la modélisation adoptée, puis effectuons une revue des différentesméthodes existantes, et présentons la mise en oeuvre expérimentale de l’une d’entre elle : la méthode HZD.Dans une deuxième partie, nous procédons à une étude de la dissipation relativede l’énergie cinétique du robot lorsque le pied impacte le sol. Nous utilisons les résultats issus de cette étude pour planifier des trajectoires de marche dissipant peu d’énergie. De telles trajectoires ont a priori le mérite de préserver la structure du robot et de générer moins de bruit. A contrario, des trajectoires dissipant la majorité de l’énergie du robot sont utilisées pour un arrêt rapide. Une étude numérique a montré que ces résultats sont robustes à des incertitudes de modèle.Enfin, dans une dernière partie, afin de compenser les difficultés liées au sousactionnement, nous proposons d’utiliser le degré de liberté supplémentaire offert par un changement de l’échelle de temps dans les équations de la dynamique (Time Scaling) pour la classe de robots considérée. En utilisant par ailleurs un changement de coordonnées et de feedback, nous dérivons de nouvelles formes normales exactes et approximatives. / This thesis addresses the general problem of the walking control of biped robots. The foot of the robot in contact with the ground may tip over and cause the robot to be undercatuated. This is a major difficulty in term of control. This problem is addressed by considering planar biped robots with point feet.In a first part, we present a standard way of modeling such systems, a litterature review of the existing methods, and then report experimental results of the walking control of a biped robot using the HZD method.In a second part, we perform an analytic and numeric study of the relativekinetic energy dissipation when the foot of the robot impacts the ground. Usingthis study, we design trajectories with low energy dissipation at impact, which a priori result in gaits preserving the hardware of the robot and causing less noise. On the contrary, trajectories dissipating almost all the kinetic energy are used to quickly stop the robot.Finally, in an attempt to alleviate the burden due to underactuation, we proposeto investigate the additional degree of freedom provided, in the control design, by a change of time scale in the dynamic equations (Time-Scaling) for the considered class of biped robots. Using feedback transformations, we derive new exact and approximative normal forms.

Planejamento de movimento tem o propósito de determinar quais movimentos o robô deve realizar para que alcance posições ou configurações desejadas no ambiente sem que ocorram colisões com obstáculos. É comum na robótica móvel simplificar o planejamento de movimento representando o robô pelas coordenadas do seu centro e desconsiderando qualquer restrição cinemática e dinâmica de movimento. Entretanto, a maioria dos robôs móveis possuem restrições cinemáticas não-holonômicas, e para algumas tarefas e robôs, é importante considerar tais restrições juntamente com o modelo dinâmico do robô na tarefa de planejamento. Assim é possível determinar um caminho que possa ser de fato seguido pelo robô. Nesse trabalho é proposto um método de planejamento cinemático-dinâmico que permite planejar trajetórias para robôs móveis usando malhas de estados. Essa abordagem considera a cinemática e a dinâmica do robô para gerar trajetórias possíveis de serem executadas e livre de colisões com obstáculos. Quando obstáculos não representados no mapa são detectados pelos sensores do robô, uma nova trajetória é gerada para desviar desses obstáculos. O planejamento de movimento utilizando malhas de estados foi associado a um algoritmo de desvio de obstáculos baseado no método da janela dinâmica (DWA). Esse método é responsável pelo controle de seguimento de trajetória, garantindo a segurança na realização da tarefa durante a navegação. As trajetórias planejadas foram executadas em duas plataformas distintas. Essas plataformas foram utilizadas em tarefas de navegação em ambientes simulados interno e externo e em ambientes reais. Para navegação em ambientes internos utilizou-se o robô móvel Pioneer 3AT e para navegação em ambientes externos utilizou-se o veículo autônomo elétrico CaRINA 1 que está sendo desenvolvido no ICMC-USP com apoio do Instituto Nacional de Ciência e Tecnologia em Sistemas Embarcados Críticos (INCT-SEC). / Motion planning aims to determine which movements the robot must accomplish to reach a desired position or configuration in the environment without the occurrence of collisions with obstacles. It is common in mobile robotics to simplify the motion planning representing the robot by the coordinates of its center of gravity and ignoring any kinematic and dynamic constraint motion. However, most mobile robots have non-holonomic kinematic constraints, and for some tasks and robots, it is important to consider these constraints together with the dynamic model of the robot in task planning. Thus it is possible to determine a path that can actually be followed by the robot. Here we propose a method for kinematic-dynamic path planning using lattice states. This approach considers the kinematic and dynamic of the robot to generate generate feasible trajectories free of collisions with obstacles. When obstacles not represented on the map are detected by the sensors of the robot, a new trajectory is generated to avoid these obstacles. The motion planning using lattice state was associated with an obstacle avoidance algorithm based on the dynamic window approach (DWA). This method is responsible for trajectory tracking to ensure safety in navigation tasks. This method was applied in two distinct platforms. These platforms were used for navigation tasks in both indoor and outdoor simulated environments, as well as, in real environments. For navigation in indoor environments we used a Pioneer 3AT robot and for outdoor navigation we used the autonomous electric vehicle CaRINA1 being developed at ICMC-USP with support National Institute of Science and Technology in Critical Embedded Systems (INCT-SEC).

Un robot humanoïde est un système complexe doté de nombreux degrés de liberté, et dont le comportement est sujet aux équations non linéaires du mouvement. Par conséquent, la planification de mouvement pour un tel système est une tâche difficile d'un point de vue calculatoire. Dans ce mémoire, nous avons pour objectif de développer une méthode permettant d'utiliser la puissance de calcul des GPUs dans le contexte de la planification de mouvement corps-complet basée sur de l'optimisation. Nous montrons dans un premier temps les propriétés du problème d'optimisation, et des pistes d'étude pour la parallélisation de ce dernier. Ensuite, nous présentons notre approche du calcul de la dynamique, adaptée aux architectures de calcul parallèle. Cela nous permet de proposer une implémentation de notre problème de planification de mouvement sur GPU: contraintes et gradients sont calculés en parallèle, tandis que la résolution du problème même se déroule sur le CPU. Nous proposons en outre une nouvelle paramétrisation des forces de contact adaptée à notre problème d'optimisation. Enfin, nous étudions l'extension de notre travail au contrôle prédictif. / A humanoid robot is a complex system with numerous degrees of freedom, whose behavior is subject to the nonlinear equations of motion. As a result, planning its motion is a difficult task from a computational perspective.In this thesis, we aim at developing a method that can leverage the computing power of GPUs in the context of optimization-based whole-body motion planning. We first exhibit the properties of the optimization problem, and show that several avenues can be exploited in the context of parallel computing. Then, we present our approach of the dynamics computation, suitable for highly-parallel processing architectures. Next, we propose a many-core GPU implementation of the motion planning problem. Our approach computes the constraints and their gradients in parallel, and feeds the result to a nonlinear optimization solver running on the CPU. Because each constraint and its gradient can be evaluated independently for each time interval, we end up with a highly parallelizable problem that can take advantage of GPUs. We also propose a new parametrization of contact forces adapted to our optimization problem. Finally, we investigate the extension of our work to model predictive control.

Robôs manipuladores vem desempenhando um importante papel em operações orbitais, e isso foi possível devido ao grande avanço da robótica espacial nas últimas décadas. Porém, o planejamento do movimento ainda é considerado um dos maiores desafios nesse campo, embora diversos métodos e considerações tenham sido propostas para resolver esse problema. As primeiras contribuições na área de planejamento de movimento dependiam de uma representação explícita do espaço de configuração do robô. Dessa forma, o planejamento de movimento para sistemas robóticos com muitos graus de liberdade era impraticável. Para lidar com esse problema, surgiram os métodos baseados em amostragem, dentre eles, o método de Árvore Aleatória de Exploração Rápida - RRT (do inglês, Rapidly- Exploring Random Tree). Estes métodos, ao invés da construção de todo o conjunto de configurações livre de colisões, requerem apenas a verificação de colisão com obstáculos para um conjunto discreto e finito de configurações do robô. Consequentemente, para este tipo de problema, são métodos mais vantajosos em termos computacionais. Com esta motivação, o presente trabalho tem como objetivo o desenvolvimento de um planejador de rota e de um planejador de trajetória para um robô manipulador espacial de base livre flutuante com dois braços, ambos planejadores com suporte a desvio de obstáculos estáticos. O conceito de manipulador dinamicamente equivalente é utilizado para modelar o manipulador espacial. Isso permite que o planejamento seja feito para um manipulador de base fixa subatuado dinamicamente equivalente ao manipulador de base livre flutuante. Os algoritmos baseados em amostragem RRT* e RRTControl disponibilizados na biblioteca OMPL (do inglês, Open Motion Planning Library) foram adaptados para resolver este problema de planejamento. O algoritmo RRT* é usado para o planejamento de rota, e o RRTControl para o planejamento de trajetória. Ambos planejadores utilizam o espaço de configuração das juntas do robô. Para possibilitar que a orientação e posição final dos dois efetuadores do robô pudessem ser especificadas no espaço da tarefa, um algoritmo de cinemática inversa baseado em algoritmo genético também foi desenvolvido para encontrar a solução da cinemática inversa do manipulador. / Robot manipulator has played an important role in orbital missions and this was possible due to the advance of space robotics in recent decades. However, motion planning is still considered one of the biggest challenges of the field though various methods and considerations were proposed by researchers to handle this problem. The first contributions in this field were dependent on an explicit representation of the free configuration space. Consequently, it was impractical to solve the motion planning problem for robotic systems with many degrees of freedom. In order to cope with this limitation, sampling-based methods were proposed, in particular, the Rapidly-Exploring Random Tree – RRT. Sampling-based methods only requires a procedure to verify collision with obstacles for a discrete amount of robot configuration during planning. Therefore, they are more advantageous in computational terms. In this work a path planner and a trajectory planner were developed for a free-floating planar manipulator with two arms with support for static obstacle avoidance. The Dynamically Equivalent Manipulator approach was used for modelling the robot. Thus, the planners were developed based on a fixed-base underactuated manipulator model which is dynamically equivalent to the free-floating manipulator. The sampling-based algorithms RRT* and RRTControl of the Open Motion Planning Library (OMPL) were adapted to solve this motion planning problem. The RRT* were used for path planning, and the RRTControl for trajectory planning, both carried out in the robot joint space. As the desired orientations and positions of the two end-effectors were specified in the task-space, a genetic algorithm was also developed to compute the inverse kinematics of the manipulator.

"A number of outstanding problems in robotic motion and manipulation involve tasks where degrees of freedom (DoF), be they part of the robot, an object being manipulated, or the surrounding environment, cannot be accurately controlled by the actuators of the robot alone. Rather, they are also controlled by physical properties or interactions - contact, robot dynamics, actuator behavior - that are influenced by the actuators of the robot. In particular, we focus on two important areas of poorly controlled robotic manipulation: motion planning for deformable objects and in deformable environments; and manipulation with uncertainty. Many everyday tasks we wish robots to perform, such as cooking and cleaning, require the robot to manipulate deformable objects. The limitations of real robotic actuators and sensors result in uncertainty that we must address to reliably perform fine manipulation. Notably, both areas share a common principle: contact, which is usually prohibited in motion planners, is not only sometimes unavoidable, but often necessary to accurately complete the task at hand. We make four contributions that enable robot manipulation in these poorly controlled tasks: First, an efficient discretized representation of elastic deformable objects and cost function that assess a ``cost of deformation' for a specific configuration of a deformable object that enables deformable object manipulation tasks to be performed without physical simulation. Second, a method using active learning and inverse-optimal control to build these discretized representations from expert demonstrations. Third, a motion planner and policy-based execution approach to manipulation with uncertainty which incorporates contact with the environment and compliance of the robot to generate motion policies which are then adapted during execution to reflect actual robot behavior. Fourth, work towards the development of an efficient path quality metric for paths executed with actuation uncertainty that can be used inside a motion planner or trajectory optimizer."

Large quantities of high-level radioactive waste were generated during WWII. This waste is being stored in facilities such as double-shell tanks in Washington, and the Waste Isolation Pilot Plant in New Mexico. Due to the dangerous nature of radioactive waste, these facilities must undergo periodic inspections to ensure that leaks are detected quickly. In this work, we provide a set of methodologies to aid in the monitoring and inspection of these hazardous facilities. This allows inspection of dangerous regions without a human operator, and for the inspection of locations where a person would not be physically able to enter. First, we describe a robot equipped with sensors which uses a modified A* path-planning algorithm to navigate in a complex environment with a tether constraint. This is then augmented with an adaptive informative path planning approach that uses the assimilated sensor data within a Gaussian Process distribution model. The model's predictive outputs are used to adaptively plan the robot's path, to quickly map and localize areas from an unknown field of interest. The work was validated in extensive simulation testing and early hardware tests. Next, we focused on how to assign tasks to a heterogeneous set of robots. Task assignment is done in a manner which allows for task-robot dependencies, prioritization of tasks, collision checking, and more realistic travel estimates among other improvements from the state-of-the-art. Simulation testing of this work shows an increase in the number of tasks which are completed ahead of a deadline. Finally, we consider the case where robots are not able to complete planned tasks fully autonomously and require operator assistance during parts of their planned trajectory. We present a sampling-based methodology for allocating operator attention across multiple robots, or across different parts of a more sophisticated robot. This allows few operators to oversee large numbers of robots, allowing for a more scalable robotic infrastructure. This work was tested in simulation for both multi-robot deployment, and high degree-of-freedom robots, and was also tested in multi-robot hardware deployments. The work here can allow robots to carry out complex tasks, autonomously or with operator assistance. Altogether, these three components provide a comprehensive approach towards robotic deployment within the deactivation and decommissioning tasks faced by the Department of Energy.

Robots must plan and execute tasks in the presence of uncertainty. Uncertainty arises from sensing errors, control errors, and uncertainty in the geometry of the environment. The last, which is called model error, has received little previous attention. We present a framework for computing motion strategies that are guaranteed to succeed in the presence of all three kinds of uncertainty. The motion strategies comprise sensor-based gross motions, compliant motions, and simple pushing motions.

This report presents optimal control methods integrated with hierarchical control framework to realize real-time collision-free optimal trajectories for motion control in kinematic chain manipulator (KCM) robot systems under dynamic environments. Recently, they have been increasingly used in applications where manipulators are required to interact with random objects and humans. As a result, more complex trajectory planning schemes are required. The main objective of this research is to develop new motion control strategies that can enable such robots to operate efficiently and optimally in such unknown and dynamic environments. Two direct optimal control methods: The direct collocation method and discrete mechanics for optimal control methods are investigated for solving the related constrained optimal control problem and the results are compared. Using the receding horizon control structure, open-loop sub-optimal trajectories are generated as real-time input to the controller as opposed to the predefined trajectory over the entire time duration. This, in essence, captures the dynamic nature of the obstacles. The closed-loop position controller is then engaged to span the robot end-effector along this desired optimal path by computing appropriate torque commands for the joint actuators. Employing a two-degree of freedom technique, collision-free trajectories and robot environment information are transmitted in real-time by the aid of a bidirectional connectionless datagram transfer. A hierarchical network control platform is designed to condition triggering of precedent activities between a dedicated machine computing the optimal trajectory and the real-time computer running a low-level controller. Experimental results on a 2-link planar robot are presented to validate the main ideas. Real-time implementation of collision-free workspace trajectory control is achieved for cases where obstacles are arbitrarily changing in the robot workspace.

One of the basic problems which have to be solved by Unmanned Automated Vehicles (UAV) involves the computation of a motion plan that would enable the system to reach a target given a set of initial conditions in presence of uncertainties on the vehicle dynamics and in the environment. Recent research efforts in this area have relied on deterministic models. To address the problem of inevitable uncertainties, a low-level control layer is typically used to ensure proper robust trajectory tracking. Such decision-tracking algorithms correct model disturbances a posteriori, while the whole movement planning is done in a purely deterministic fashion. We argue that the decision making process that takes place during movement planning, as performed by experienced human pilots, is not a purely deterministic operation, but is heavily influenced by the presence of uncertainties and reflects a risk-management policy. This research aims at addressing these uncertainties and developing an optimal control strategy that would account for the presence of system uncertainties. The underlying description of UAV trajectories will be based on a modeling language, the Maneuver Automaton, that takes into full account the vehicle dynamics, and hence guarantees flyable and trackable paths and results in a discretized solution space. Two optimal control problems, a nominal problem omitting uncertainties and a robust problem addressing the presence of uncertainties, will be defined and compared throughout this work. The incorporation of uncertainties, will ensure that the generated motion planning policies will maximize the probability to meet mission goals, weighing risks against performance.