2,037 research outputs found

    Robot Excitation Trajectories for Dynamic Parameter Estimation using Optimized B-Splines

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    In this paper we adressed the problem of finding exciting trajectories for the identification of manipulator link inertia parameters. This can be formulated as a constraint nonlinear optimization problem. The new approach in the presented method is the parameterization of the trajectories with optimized B-splines. Experiments are carried out on a 7 joint Light-Weight robot with torque sensoring in each joint. Thus, unmodeled joint friction and noisy motor current measurements must not be taken into account. The estimated dynamic model is verified on a different validation trajectory. The results show a clear improvement of the estimated dynamic model compared to a CAD-valued model

    Motion Planning of Uncertain Ordinary Differential Equation Systems

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    This work presents a novel motion planning framework, rooted in nonlinear programming theory, that treats uncertain fully and under-actuated dynamical systems described by ordinary differential equations. Uncertainty in multibody dynamical systems comes from various sources, such as: system parameters, initial conditions, sensor and actuator noise, and external forcing. Treatment of uncertainty in design is of paramount practical importance because all real-life systems are affected by it, and poor robustness and suboptimal performance result if it’s not accounted for in a given design. In this work uncertainties are modeled using Generalized Polynomial Chaos and are solved quantitatively using a least-square collocation method. The computational efficiency of this approach enables the inclusion of uncertainty statistics in the nonlinear programming optimization process. As such, the proposed framework allows the user to pose, and answer, new design questions related to uncertain dynamical systems. Specifically, the new framework is explained in the context of forward, inverse, and hybrid dynamics formulations. The forward dynamics formulation, applicable to both fully and under-actuated systems, prescribes deterministic actuator inputs which yield uncertain state trajectories. The inverse dynamics formulation is the dual to the forward dynamic, and is only applicable to fully-actuated systems; deterministic state trajectories are prescribed and yield uncertain actuator inputs. The inverse dynamics formulation is more computationally efficient as it requires only algebraic evaluations and completely avoids numerical integration. Finally, the hybrid dynamics formulation is applicable to under-actuated systems where it leverages the benefits of inverse dynamics for actuated joints and forward dynamics for unactuated joints; it prescribes actuated state and unactuated input trajectories which yield uncertain unactuated states and actuated inputs. The benefits of the ability to quantify uncertainty when planning the motion of multibody dynamic systems are illustrated through several case-studies. The resulting designs determine optimal motion plans—subject to deterministic and statistical constraints—for all possible systems within the probability space

    Numerical approach of collision avoidance and optimal control on robotic manipulators

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    Collision-free optimal motion and trajectory planning for robotic manipulators are solved by a method of sequential gradient restoration algorithm. Numerical examples of a two degree-of-freedom (DOF) robotic manipulator are demonstrated to show the excellence of the optimization technique and obstacle avoidance scheme. The obstacle is put on the midway, or even further inward on purpose, of the previous no-obstacle optimal trajectory. For the minimum-time purpose, the trajectory grazes by the obstacle and the minimum-time motion successfully avoids the obstacle. The minimum-time is longer for the obstacle avoidance cases than the one without obstacle. The obstacle avoidance scheme can deal with multiple obstacles in any ellipsoid forms by using artificial potential fields as penalty functions via distance functions. The method is promising in solving collision-free optimal control problems for robotics and can be applied to any DOF robotic manipulators with any performance indices and mobile robots as well. Since this method generates optimum solution based on Pontryagin Extremum Principle, rather than based on assumptions, the results provide a benchmark against which any optimization techniques can be measured

    Manipulating liquids with robots: A sloshing-free solution

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    This paper addresses the problem of suppressing sloshing dynamics in liquid handling robotic systems by an appropriate design of position/orientation trajectories. Specifically, a dynamic system, i.e. the exponential filter, is used to filter the desired trajectory for the liquid-filled vessel moved by the robot and counteract the sloshing effect. To this aim, the vessel has been modelled as a spherical pendulum of proper mass/length subject to the accelerations imposed by the robot and the problem has been approached in terms of vibration suppression to cancel the residual oscillations of the pendulum, i.e. the pendulum swing at the end of the reference rest-to-rest motion. In addition, in order to reduce the relative motion between liquid and vessel, an orientation compensation mechanism has been devised aiming to maintain the vessel aligned with the pendulum during the motion. The effectiveness of the proposed approach, both in simple point-to-point motions and complex multi-point trajectories, has been proved by means of an exhaustive set of experimental tests on an industrial manipulator that moves a cylindrical vessel filled with water. This innovative solution effectively uses all the degrees of freedom of the robotic manipulator to successfully suppress sloshing, thus significantly improving the performances of the robotic system. Furthermore, the proposed solution, showing a high degree of robustness as well as intrinsic design simplicity, is very promising for designing novel industrial robotics applications with a short time-to-market across key manufacturing sectors (e.g., food and beverage, among others)
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