1,720,959 research outputs found
Elastic Structure Preserving Control for Compliant Robots Driven by Agonistic-Antagonistic Actuators (ESPaa)
No full textThe regulation of the link positions of compliant robots, damping out undesired link oscillations while preserving the system's inherent elasticity is still a challenging task in practical applications. This task becomes even harder to be tackled in the case of compliant robots driven by agonistic-antagonistic variable stiffness actuators in which there are two motors associated with each joint of the system. In this work, leveraging on the physical realization of the elastic mechanism of such actuators, we propose a novel control law able to simultaneously achieve a good regulation performance and a desired damped behavior at the link, while preserving the elastic structure of the system as well as the possibility of adjusting the passive stiffness at the joints. Simulations on the agonistic-antagonistic actuators of the forearm and wrist joints of the Hand Arm System from the German Aerospace Center (DLR) and experiments on a planar platform with an analogous actuation unit, namely qbMove Advanced, validate the proposed method
Overcoming the Torque/Stiffness Range Tradeoff in Antagonistic Variable Stiffness Actuators
Full text linkTo face the demand for applications in which robots have to safely interact with humans and the environment, the research community developed new types of actuators with compliant characteristics. To embody compliance into the actuator, elastic elements with fixed or variable compliance can be used. Among the variable stiffness mechanisms, a popular approach is based on the agonistic-antagonistic (A-A) layout, where two prime movers are elastically connected to the output shaft of the actuator. Notwithstanding the conceptually simple realization of the A-A layout, one limitation is that, due to the nonlinear torque/deflection characteristic of the elastic transmissions and to the limited spring elongation, the stiffness range achievable at the output shaft reduces as the external torque increases. In this work, a novel layout, based on the A-A principle, is proposed to increase the torque/stiffness capability of the actuator. To achieve this result, we combine elastic transmissions with linear and nonlinear torque/deflection characteristics. The mathematical model of the new layout and a possible implementation are analyzed. Then, the design of a novel variable stiffness actuator is presented and experimental validations are shown to compare the new device with the benchmark
Time Generalization of Trajectories Learned on Articulated Soft Robots
Full text linkTo avoid feedback-related stiffening of articulated soft robots, a substantive feedforward contribution is crucial. However, obtaining reliable feedforward actions requires very accurate models, which are not always available for soft robots. Learning-based approaches are a promising solution to the problem. They proved to be an effective strategy achieving good tracking performance, while preserving the system intrinsic compliance. Nevertheless, learning methods require rich data sets, and issues of scalability and generalization still remain to be solved. This letter proposes a method to generalize learned control actions to execute a desired trajectory with different velocities - with the ultimate goal of making these learning-based architectures sample efficient. More specifically we prove that the knowledge of how to execute a same trajectory at five different speeds is necessary and sufficient to execute the same trajectory at any velocity - without any knowledge of the model. We also give a simple constructive way to calculate this new feedforward action. The effectiveness of the proposed technique is validated in extensive simulation on a Baxter robot with soft springs playing a drum, and experimentally on a VSA double pendulum performing swinging motions
Trajectory tracking of a one-link flexible arm via iterative learning control
No full textTrajectory tracking of flexible link robots is a classical control problem. Historically, the link elasticity was considered as something to be removed. Hence, the control performance was guaranteed by adopting high-gain feedback loops and, possibly, a dynamic compensation with the result to stiffen up the dynamic behavior of the robot. Nowadays, robots are pushed more and more towards a safe physical interaction with a less and less structured environment. Hence, the design and control of the robots moved to an on-purpose introduction of highly compliant elements in the robot bodies, the so-called soft robotics, and towards control approaches that aim to provide the tracking performance without a substantial change in the robot dynamic behavior. Following this approach, we present an iterative learning control that relies mainly on a feedforward component, hence preserves the robot dynamics, for trajectory tracking of a one-link flexible arm. We provide a condition, based on the system dynamics and similar to the Strong Inertially Coupled property, that ensures the applicability of the proposed control method. Finally, we report simulation and experimental tests to validate the theoretical results
A robust iterative learning control for continuous-time nonlinear systems with disturbances
Full text linkIn this paper, we study the trajectory tracking problem using iterative learning control for continuous-time nonlinear systems with a generic fixed relative degree in the presence of disturbances. This class of controllers iteratively refine the control input relying on the tracking error of the previous trials and some properly tuned learning gains. Sufficient conditions on these gains guarantee the monotonic convergence of the iterative process. However, the choice of the gains is heuristically hand-tuned given an approximated system model and no information on the disturbances. Thus, in the cases of inaccurate knowledge of the model or iteration-varying measurement errors, external disturbances, and delays, the convergence condition is unlikely to be verified at every iteration. To overcome this issue, we propose a robust convergence condition, which ensures the applicability of the pure feedforward control even if other classical conditions are not fulfilled for some trials due to the presence of disturbances. Furthermore, we quantify the upper bound of the nonrepetitive disturbance that the iterative algorithm is able to handle. Finally, we validate the convergence condition simulating the dynamics of a two degrees of freedom underactuated arm with elastic joints, where one is active, and the other is passive, and a Franka Emika Panda manipulator
Iterative Learning Control for Compliant Underactuated Arms
Full text linkOperations involving safe interactions in unstructured environments require robots with adapting behaviors. Compliant manipulators are a promising technology to achieve this goal. Despite that, some classical control problems such as following a trajectory are still open. A typical solution is to compensate the system dynamics with feedback loops. However, this solution increases the effective robot stiffness and jeopardizes the safety property provided by the compliant design. On the other hand, purely feedforward approaches can achieve good tracking performance while preserving the robot intrinsic compliance. However, a feedforward control framework for robots with passive elastic joints is still missing. This article presents an iterative learning control algorithm for purely feedforward trajectory tracking for compliant underactuated arms. Each arm is composed of active elastic joints and a generic number of passive ones connected through rigid links. We prove the convergence of the iterative method, also in the presence of uncertainties and bounded disturbances. Different output functions are analyzed providing conditions, based on the system inertial properties that ensure the algorithm applicability. Additionally, an automatic selection of the learning gain is proposed. Finally, we extensively validate the theoretical results with simulations and experiments
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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