Temporal models of motions and forces for Human-Robot Interactive manipulation

L'intérêt pour la robotique a débuté dans les années 70 et depuis les robots n'ont cessé de remplacer les humains dans l'industrie. L'automatisation à outrance n'apporte cependant pas que des avantages, car elle nécessite des environnements parfaitement contrôlés et la reprogrammation d'une tâche est longue et fastidieuse. Le besoin accru d'adaptabilité et de ré-utilisabilité des systèmes d'assemblage force la robotique à se révolutionner en amenant notamment l'homme et le robot à interagir. Ce nouveau type de collaboration permet de combiner les forces respectives des humains et des robots. Cependant l'homme ne pourra être inclus en tant qu'agent actif dans ces nouveaux espaces de travail collaboratifs que si l'on dispose de robots sûrs, intuitifs et facilement reprogrammables. C'est à la lumière de ce constat qu'on peut deviner le rôle crucial de la génération de mouvement pour les robots de demain. Pour que les humains et les robots puissent collaborer, ces derniers doivent générer des mouvements sûrs afin de garantir la sécurité de l'homme tant physique que psychologique. Les trajectoires sont un excellent modèle pour la génération de mouvements adaptés aux robots collaboratifs, car elles offrent une description simple et précise de l'évolution du mouvement. Les trajectoires dîtes souples sont bien connues pour générer des mouvements sûrs et confortables pour l'homme. Dans cette thèse nous proposons un algorithme de génération de trajectoires temps-réel basé sur des séquences de segments de fonctions polynomiales de degré trois pour construire des trajectoires souples. Ces trajectoires sont construites à partir de conditions initiales et finales arbitraires, une condition nécessaire pour que les robots soient capables de réagir instantanément à des événements imprévus. L'approche basée sur un modèle à jerk-contraint offre des solutions orientées performance: les trajectoires sont optimales en temps sous contraintes de sécurité. Ces contraintes de sécurité sont des contraintes cinématiques qui dépendent de la tâche et du contexte et doivent être spécifiées. Pour guider le choix de ces contraintes, nous avons étudié le rôle de la cinématique dans la définition des propriétés ergonomiques du mouvement. L'algorithme a également été étendu pour accepter des configurations initiales non admissibles permettant la génération de trajectoires sous contraintes cinématiques non constantes. Cette extension est essentielle dans le contexte des interactions physiques homme-robot, car le robot doit être capable d'adapter son comportement en temps-réel pour préserver la sécurité physique et psychologique des humains. Cependant considérer le problème de la génération de trajectoires ne suffit pas si on ne considère pas le contrôle. Le passage d'une trajectoire à une autre est un problème difficile pour la plupart des systèmes robotiques dans des contextes applicatifs réels. Pour cela, nous proposons une stratégie de contrôle réactif de ces trajectoires ainsi qu'une architecture construite autour de l'utilisation des trajectoires.It was in the 70s when the interest for robotics really emerged. It was barely half a century ago, and since then robots have been replacing humans in the industry. This robot-oriented solution doesn't come without drawbacks as full automation requires time-consuming programming as well as rigid environments. With the increased need for adaptability and reusability of assembly systems, robotics is undergoing major changes and see the emergence of a new type of collaboration between humans and robots. Human-Robot collaboration get the best of both world by combining the respective strengths of humans and robots. But, to include the human as an active agent in these new collaborative workspaces, safe and flexible robots are required. It is in this context that we can apprehend the crucial role of motion generation in tomorrow's robotics. For the emergence of human-robot cooperation, robots have to generate motions ensuring the safety of humans, both physical and physchological. For this reason motion generation has been a restricting factor to the growth of robotics in the past. Trajectories are excellent candidates in the making of desirable motions designed for collaborative robots, because they allow to simply and precisely describe the motions. Smooth trajectories are well known to provide safe motions with good ergonomic properties. In this thesis we propose an Online Trajectory Generation algorithm based on sequences of segment of third degree polynomial functions to build smooth trajectories. These trajectories are built from arbitrary initial and final conditions, a requirement for robots to be able to react instantaneously to unforeseen events. Our approach built on a constrained-jerk model offers performance-oriented solutions : the trajectories are time-optimal under safety constraints. These safety constraints are kinematic constraints that are task and context dependent and must be specified. To guide the choice of these constraints we investigated the role of kinematics in the definition of ergonomics properties of motions. We also extended our algorithm to cope with non-admissible initial configurations, opening the way to trajectory generation under non-constant motion constraints. This feature is essential in the context of physical Human-Robot Interactions, as the robot must adapt its behavior in real time to preserve both the physical and psychological safety of humans. However, only considering the trajectory generation problem is not enough and the control of these trajectories must be adressed. Switching from a trajectory to another is a difficult problem for most robotic systems in real applicative contexts. For this purpose we propose a strategy for the Reactive Control of these Trajectories as well as an architecture built around the use of trajectories

A Predictive Technique for the Real-Time Trajectory Scaling under High-Order Constraints

Modern robotic systems must be able to react to unexpected environmental events. To this purpose, planning techniques for the real-time generation/modification of trajectories have been developed in recent times. In the frequent case of applications which require following a predefined path, the assigned time-law must be inspected in real time so as to verify whether it satisfies the system constraints or, conversely, if it must be scaled in order to obtain a feasible trajectory. The problem has been addressed in several ways in the literature. One of the known approaches, based on the use of nonlinear filters, is revised in this paper in order to return feasible solutions under any circumstances. Differently from alternative strategies, it manages constraints up to the torque derivatives and has evaluation times compatible with the ones required by modern control systems. The proposed technique is validated through simulations and real experiments. Comparisons are proposed with an algorithm based on a model predictive technique and with an alternative scaling system

Experimental comparison of parameter estimation methods in adaptive robot control

In the literature on adaptive robot control a large variety of parameter estimation methods have been proposed, ranging from tracking-error-driven gradient methods to combined tracking- and prediction-error-driven least-squares type adaptation methods. This paper presents experimental data from a comparative study between these adaptation methods, performed on a two-degrees-of-freedom robot manipulator. Our results show that the prediction error concept is sensitive to unavoidable model uncertainties. We also demonstrate empirically the fast convergence properties of least-squares adaptation relative to gradient approaches. However, in view of the noise sensitivity of the least-squares method, the marginal performance benefits, and the computational burden, we (cautiously) conclude that the tracking-error driven gradient method is preferred for parameter adaptation in robotic applications

Damped Harmonic Smoother for Trajectory Planning and Vibration Suppression

In this brief, a novel filter for online trajectory generation is presented. The filter can be categorized as an input smoother since it acts on the input signal by increasing its continuity level. When fed with simple signals, as, e.g., a step input, it behaves like a trajectory generator that produces harmonic motions. Moreover, it can be combined with other filters, and in particular, with smoothers having a rectangular impulse response, in order to generate (online) more complex trajectories compliant with several kinematic constraints. On the other hand, being a filter, it possesses the capability of shaping the frequency spectrum of the output signal. This possibility can be profitably exploited to suppress residual vibration by imposing that the zeros of the filter cancel the oscillatory dynamics of the plant. For this purpose, the standard harmonic filter has been generalized in order to consider not only the natural frequency but also the damping coefficient of the plant. In this manner, the so-called ``damped harmonic filter" and the related ``damped harmonic trajectory" have been defined. By means of theoretical considerations, supported by experimental tests, the novel approach has been compared with the existing methods, and the advantages of its use have been proved

Optimal Trajectories for Vibration Reduction Based on Exponential Filters

In this paper, a new type of trajectory, based on an exponential jerk, is presented along with filters for their online generation. The goal is to generalize constant jerk trajectories, widely used in industrial applications, in order to reduce vibrations of motion systems. As a matter of fact, constant jerk trajectories do not assure a complete vibration suppression when the damping of the resonant modes is not negligible. The values of the parameters (decay rate and duration) of the jerk impulses that allow residual vibration cancellation are derived in an analytical way as a function of the dynamic characteristics of the plant. Comparisons with the well-known input shaping techniques and with system-inversion-based filters show the advantages of the proposed method in terms of robustness with respect to modeling errors, smoothness of the resulting trajectory, and time duration of the motion under velocity and acceleration constraints